Kersebaum, K. C., Boote, K. J., Jorgenson, J. S., Nendel, C., Bindi, M., Frühauf, C., et al. (2015). Analysis and classification of data sets for calibration and validation of agro-ecosystem models. Env. Model. Softw., 72, 402–417.
Abstract: Experimental field data are used at different levels of complexity to calibrate, validate and improve agroecosystem models to enhance their reliability for regional impact assessment. A methodological framework and software are presented to evaluate and classify data sets into four classes regarding their suitability for different modelling purposes. Weighting of inputs and variables for testing was set from the aspect of crop modelling. The software allows users to adjust weights according to their specific requirements. Background information is given for the variables with respect to their relevance for modelling and possible uncertainties. Examples are given for data sets of the different classes. The framework helps to assemble high quality data bases, to select data from data bases according to modellers requirements and gives guidelines to experimentalists for experimental design and decide on the most effective measurements to improve the usefulness of their data for modelling, statistical analysis and data assimilation. (C) 2015 Elsevier Ltd. All rights reserved.
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Kraus, D., Weller, S., Klatt, S., Haas, E., Wassmann, R., Kiese, R., et al. (2015). A new LandscapeDNDC biogeochemical module to predict CH4 and N2O emissions from lowland rice and upland cropping systems. Plant Soil, 386(1-2), 125–149.
Abstract: Replacing paddy rice by upland systems such as maize cultivation is an on-going trend in SE Asia caused by increasing water scarcity and higher demand for meat. How such land management changes will feedback on soil C and N cycles and soil greenhouse gas emissions is not well understood at present. A new LandscapeDNDC biogeochemical module was developed that allows the effect of land management changes on soil C and N cycle to be simulated. The new module is applied in combination with further modules simulating microclimate and crop growth and evaluated against observations from field experiments. The model simulations agree well with observed dynamics of CH (4) emissions in paddy rice depending on changes in climatic conditions and agricultural management. Magnitude and peak emission periods of N (2) O from maize cultivation are simulated correctly, though there are still deficits in reproducing day-to-day dynamics. These shortcomings are most likely related to simulated soil hydrology and may only be resolved if LandscapeDNDC is coupled to more complex hydrological models. LandscapeDNDC allows for simulation of changing land management practices in SE Asia. The possibility to couple LandscapeDNDC to more complex hydrological models is a feature needed to better understand related effects on soil-atmosphere-hydrosphere interactions.
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Kim, Y., Berger, S., Kettering, J., Tenhunen, J., Haas, E., & Kiese, R. (2014). Simulation of N2O emissions and nitrate leaching from plastic mulch radish cultivation with LandscapeDNDC. Ecol. Res., 29(3), 441–454.
Abstract: Radish is one of the major dry field crops in Asia commonly grown with plastic mulch and high rates of N fertilization, and potentially harming the environment due to N2O emissions and nitrate leaching. Despite the widespread use of plastic mulch, biogeochemical models so far do not yet consider impacts of mulch on soil environmental conditions and biogeochemistry. In this study, we adapted and successfully tested the LandscapeDNDC model against field data by simulating crop growth, C and N turnover and associated N2O emissions as well as nitrate leaching for radish cultivation with plastic mulch and in conjunction with different rates of N fertilization (465-765 kg N ha(-1) year(-1)). Due to the sandy soil texture and monsoon climate, nitrate leaching with rates up to 350 kg N ha(-1) year(-1) was the dominant reason for overall low nitrogen use efficiency (32-43 %). Direct or indirect N2O emissions (calculated from simulated nitrate leaching rates and IPCC EFind = 0.0075) ranged between 2 and 3 kg N ha(-1) year(-1), thus contributing an equal amount to total field emissions of about 5 kg N ha(-1) year(-1). Based on our results, emission factors for direct N2O emissions ranged between 0.004 and 0.005. These values are only half of the IPCC default value (0.01), demonstrating the need of biogeochemical models for developing site and/or region specific EFs. Simulation results also revealed that changes in agricultural management by applying the fertilizer only to the rows would be an efficient mitigation strategy, effectively decreasing field nitrate leaching and N2O emissions by 50-60 %.
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Dumont, B., Basso, B., Leemans, V., Bodson, B., Destain, J. - P., & Destain, M. - F. (2015). Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions. Precision Agric., 16(4), 361–384.
Abstract: At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque-Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha(-1)), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of -1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha(-1)) of the distribution.
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Bulak, P., Walkiewicz, A., & Brzezińska, M. (2014). Plant growth regulators-assisted phytoextraction. Biol. Plant., 58(1), 1–8.
Abstract: Plant growth regulators (PRG)-assisted phytoremediation is a technique that could enhance the yield of heavy metal accumulation in plant tissues. So far, a small number of experiments have helped identify three groups of plant hormones that may be useful for this purpose: auxins, cytokinins, and gibberellins. Studies have shown that these hormones positively affect the degree of accumulation of metallic impurities and improve the growth and stress resistance of plants. This review summarizes the present knowledge about PGRs’ impact on phytoextraction yield.
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