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Lai, R., Arca, P., Lagomarsino, A., Cappai, C., Seddaiu, G., Demurtas, C. E., et al. (2017). Manure fertilization increases soil respiration and creates a negative carbon budget in a Mediterranean maize (Zea mays L.)-based cropping system. Catena, 151, 202–212.
Abstract: Agronomic research is important to identify suitable options for improving soil carbon (C) sequestration and reducing soil CO2 emissions. Therefore, the objectives of this study were i) to analyse the on-farm effects of different nitrogen fertilization sources on soil respiration, ii) to explore the effect of fertilization on soil respiration sensitivity to soil temperature (T) and iii) to assess the effect of the different fertilization regimes on the soil C balance. We hypothesized that i) the soil CO2 emission dynamics in Mediterranean irrigated cropping systems were mainly affected by fertilization management and T and ii) fertilization affected the soil C budget via different C inputs and CO2 efflux. Four fertilization systems (farmyard manure, cattle slurry, cattle slurry + mineral, and mineral) were compared in a double-crop rotation based on silage maize (Zea mays L.) and a mixture of Italian ryegrass (Lolium multiflorum Lam.) and oats (Avena sativa L.). The research was performed in the dairy district of Arborea, in the coastal zone of Sardinia (Italy), from May 2011 to May 2012. The soil was a Psammentic Palexeralfs with a sandy texture (940 g sand kg− 1). The soil total respiration (SR), heterotrophic respiration (Rh), T and soil water content (SWC) were simultaneously measured in situ. The soil C balance was computed considering the Rh C losses and the soil C inputs from fertilizer and crop residues. The results showed that the maximum soil CO2 emission rates soon after the application of organic fertilizer reached values up to 12 μmol m− 2 s− 1. On average, the manure fertilizer showed significantly higher CO2 emissions, which resulted in a negative annual C balance (− 2.9 t ha− 1). T also affected the soil respiration temporal dynamics during the summer, consistently with results obtained in other temperate climatic regions that are characterized by wet summers and contrary to results from rainfed Mediterranean systems where the summer SR and Rh are constrained by the low SWC. The sensitivity of soil respiration to temperature significantly increased with C input from fertilizer. In conclusion, this research supported the hypotheses tested. Furthermore, the results indicated that i) soil CO2 efflux was significantly affected by fertilization management and T, and ii) fertilization with manure increased the soil respiration and resulted in a significantly negative soil C budget. This latter finding could be primarily explained by a reduction in productivity and, consequently, in crop residue with organic fertilization alone as compared to mineral, by the favourable SWC and T for mineralization, and by the sandy soil texture, which hindered the formation of macroaggregates and hence soil C stabilization, making fertilizer organic inputs highly susceptible to mineralization.
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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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Grosz, B., Dechow, R., Gebbert, S., Hoffmann, H., Zhao, G., Constantin, J., et al. (2017). The implication of input data aggregation on up-scaling soil organic carbon changes. Env. Model. Softw., 96, 361–377.
Abstract: In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1 km and 100 km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low. (C)2017 Elsevier Ltd. All rights reserved.
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Hidy, D., Barcza, Z., Haszpra, L., Churkina, G., Pintér, K., & Nagy, Z. (2012). Development of the Biome-BGC model for simulation of managed herbaceous ecosystems. Ecol. Model., 226, 99–119.
Abstract: Apart from measurements, numerical models are the most convenient instruments to analyze the carbon and water balance of terrestrial ecosystems and their interactions with changing environmental conditions. The process-based Biome-BGC model is widely used to simulate the storage and flux of water, carbon, and nitrogen within the vegetation, litter, and soil of unmanaged terrestrial ecosystems. Considering herbaceous vegetation related simulations with Biome-BGC, soil moisture and growing season control on ecosystem functioning is inaccurate due to the simple soil hydrology and plant phenology representation within the model. Consequently, Biome-BGC has limited applicability in herbaceous ecosystems because (1) they are usually managed; (2) they are sensitive to soil processes, most of all hydrology; and (3) their carbon balance is closely connected with the growing season length. Our aim was to improve the applicability of Biome-BGC for managed herbaceous ecosystems by implementing several new modules, including management. A new index (heatsum growing season index) was defined to accurately estimate the first and the final days of the growing season. Instead of a simple bucket soil sub-model, a multilayer soil sub-model was implemented, which can handle the processes of runoff, diffusion and percolation. A new module was implemented to simulate the ecophysiological effect of drought stress on plant mortality. Mowing and grazing modules were integrated in order to quantify the functioning of managed ecosystems. After modifications, the Biome-BGC model was calibrated and validated using eddy covariance-based measurement data collected in Hungarian managed grassland ecosystems. Model calibration was performed based on the Bayes theorem. As a result of these developments and calibration, the performance of the model was substantially improved. Comparison with measurement-based estimate showed that the start and the end of the growing season are now predicted with an average accuracy of 5 and 4 days instead of 46 and 85 days as in the original model. Regarding the different sites and modeled fluxes (gross primary production, total ecosystem respiration, evapotranspiration), relative errors were between 18-60% using the original model and 10-18% using the developed model; squares of the correlation coefficients were between 0.02-0.49 using the original model and 0.50-0.81 using the developed model. (c) 2011 Elsevier B.V. All rights reserved.
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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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