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Sanz-Cobena, A.; García-Marco, S.; Quemada, M.; Gabriel, J.L.; Almendros, P.; Vallejo, A. |
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Do cover crops enhance N2O, CO2 or CH4 emissions from soil in Mediterranean arable systems? |
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Journal Article |
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Year |
2014 |
Publication |
Science of the Total Environment |
Abbreviated Journal |
Science of the Total Environment |
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466-467 |
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164-174 |
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Keywords |
Agriculture/*methods; Air Pollutants/*metabolism; Brassica napus/growth & development/metabolism; Crops, Agricultural/growth & development/*metabolism; Gases/metabolism; Greenhouse Effect; Hordeum/growth & development/metabolism; Manure/*analysis; Nitrogen/metabolism; Nitrogen Dioxide/metabolism; Spain; Vicia/growth & development/metabolism; Zea mays/growth & development; Cover crops; GHG emissions; Green manure; Irrigation; Maize |
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This study evaluates the effect of planting three cover crops (CCs) (barley, Hordeum vulgare L.; vetch, Vicia villosa L.; rape, Brassica napus L.) on the direct emission of N(2)O, CO(2) and CH(4) in the intercrop period and the impact of incorporating these CCs on the emission of greenhouse gas (GHG) from the forthcoming irrigated maize (Zea mays L.) crop. Vetch and barley were the CCs with the highest N(2)O and CO(2) losses (75 and 47% increase compared with the control, respectively) in the fallow period. In all cases, fluxes of N(2)O were increased through N fertilization and the incorporation of barley and rape residues (40 and 17% increase, respectively). The combination of a high C:N ratio with the addition of an external source of mineral N increased the fluxes of N(2)O compared with -Ba and -Rp. The direct emissions of N(2)O were lower than expected for a fertilized crop (0.10% emission factor, EF) compared with other studies and the IPCC EF. These results are believed to be associated with a decreased NO(3)(-) pool due to highly denitrifying conditions and increased drainage. The fluxes of CO(2) were in the range of other fertilized crops (i.e., 1118.71-1736.52 kg CO(2)-Cha(-1)). The incorporation of CC residues enhanced soil respiration in the range of 21-28% for barley and rape although no significant differences between treatments were detected. Negative CH(4) fluxes were measured and displayed an overall sink effect for all incorporated CC (mean values of -0.12 and -0.10 kg CH(4)-Cha(-1) for plots with and without incorporated CCs, respectively). |
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0048-9697 |
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MA @ admin @ |
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4639 |
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Rötter, R.P.; Palosuo, T.; Kersebaum, K.-C.; Angulo, C.; Bindi, M.; Ewert, F.; Ferrise, R.; Hlavinka, P.; Moriondo, M.; Olesen, J.E.; Takáč, J.; Trnka, M. |
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Title |
Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models |
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Journal Article |
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Year |
2012 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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133 |
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23-36 |
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Keywords |
Climate; Crop growth simulation; Model comparison; Spring barley; Yield variability; Uncertainty; change impacts; nitrogen dynamics; high-temperature; soil-moisture; elevated co2; ceres-wheat; data set; growth; drought; sensitivity |
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► We compared nine crop simulation models for spring barley at seven sites in Europe. ► Applying crop models with restricted calibration leads to high uncertainties. ► Multi-crop model mean yield estimates were in good agreement with observations. ► The degree of uncertainty for simulated grain yield of barley was similar to winter wheat. ► We need more suitable data enabling us to verify different processes in the models. In this study, the performance of nine widely used and accessible crop growth simulation models (APES-ACE, CROPSYST, DAISY, DSSAT-CERES, FASSET, HERMES, MONICA, STICS and WOFOST) was compared during 44 growing seasons of spring barley (Hordeum vulgare L) at seven sites in Northern and Central Europe. The aims of this model comparison were to examine how different process-based crop models perform at multiple sites across Europe when applied with minimal information for model calibration of spring barley at field scale, whether individual models perform better than the multi-model mean, and what the uncertainty ranges are in simulated grain yields. The reasons for differences among the models and how results for barley compare to winter wheat are discussed. Regarding yield estimation, best performing based on the root mean square error (RMSE) were models HERMES, MONICA and WOFOST with lowest values of 1124, 1282 and 1325 (kg ha(-1)), respectively. Applying the index of agreement (IA), models WOFOST, DAISY and HERMES scored best having highest values (0.632, 0.631 and 0.585, respectively). Most models systematically underestimated yields, whereby CROPSYST showed the highest deviation as indicated by the mean bias error (MBE) (-1159 kg ha(-1)). While the wide range of simulated yields across all sites and years shows the high uncertainties in model estimates with only restricted calibration, mean predictions from the nine models agreed well with observations. Results of this paper also show that models that were more accurate in predicting phenology were not necessarily the ones better estimating grain yields. Total above-ground biomass estimates often did not follow the patterns of grain yield estimates and, thus, harvest indices were also different. Estimates of soil moisture dynamics varied greatly. In comparison, even though the growing cycle for winter wheat is several months longer than for spring barley, using RMSE and IA as indicators, models performed slightly, but not significantly, better in predicting wheat yields. Errors in reproducing crop phenology were similar, which in conjunction with the shorter growth cycle of barley has higher effects on accuracy in yield prediction. |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4592 |
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Tao, F.; Palosuo, T.; Roetter, R.P.; Hernandez Diaz-Ambrona, C.G.; Ines Minguez, M.; Semenov, M.A.; Kersebaum, K.C.; Cammarano, D.; Specka, X.; Nendel, C.; Srivastava, A.K.; Ewert, F.; Padovan, G.; Ferrise, R.; Martre, P.; Rodriguez, L.; Ruiz-Ramos, M.; Gaiser, T.; Hohn, J.G.; Salo, T.; Dibari, C.; Schulman, A.H. |
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Title |
Why do crop models diverge substantially in climate impact projections? A comprehensive analysis based on eight barley crop models |
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Journal Article |
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Year |
2020 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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281 |
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107851 |
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Keywords |
agriculture; climate change; crop growth simulation; impact; model; improvement; uncertainty; air CO2 enrichment; elevated CO2; wheat growth; nitrogen dynamics; simulation-models; field experiment; atmospheric CO2; rice phenology; temperature; uncertainty |
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Robust projections of climate impact on crop growth and productivity by crop models are key to designing effective adaptations to cope with future climate risk. However, current crop models diverge strongly in their climate impact projections. Previous studies tried to compare or improve crop models regarding the impact of one single climate variable. However, this approach is insufficient, considering that crop growth and yield are affected by the interactive impacts of multiple climate change factors and multiple interrelated biophysical processes. Here, a new comprehensive analysis was conducted to look holistically at the reasons why crop models diverge substantially in climate impact projections and to investigate which biophysical processes and knowledge gaps are key factors affecting this uncertainty and should be given the highest priorities for improvement. First, eight barley models and eight climate projections for the 2050s were applied to investigate the uncertainty from crop model structure in climate impact projections for barley growth and yield at two sites: Jokioinen, Finland (Boreal) and Lleida, Spain (Mediterranean). Sensitivity analyses were then conducted on the responses of major crop processes to major climatic variables including temperature, precipitation, irradiation, and CO2, as well as their interactions, for each of the eight crop models. The results showed that the temperature and CO2 relationships in the models were the major sources of the large discrepancies among the models in climate impact projections. In particular, the impacts of increases in temperature and CO2 on leaf area development were identified as the major causes for the large uncertainty in simulating changes in evapotranspiration, above-ground biomass, and grain yield. Our findings highlight that advancements in understanding the basic processes and thresholds by which climate warming and CO2 increases will affect leaf area development, crop evapotranspiration, photosynthesis, and grain formation in contrasting environments are needed for modeling their impacts. |
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2020-06-08 |
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CropM, ft_macsur |
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MA @ admin @ |
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5232 |
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Montesino-San Martín, M.; Olesen, J.E.; Porter, J.R. |
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A genotype, environment and management (GxExM) analysis of adaptation in winter wheat to climate change in Denmark |
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Journal Article |
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2014 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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187 |
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1-13 |
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Winter wheat; Climate change; Adaptation; Uncertainty; Europe; food security; model hadgem1; physical-properties; regional climate; change impacts; field-scale; land-use; yield; nitrogen; variability |
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Wheat yields in Europe have shown stagnating trends during the last two decades, partly attributed to climate change. Such developments challenge the needs for increased production, in particular at higher latitudes, to meet increasing global demands and expected productivity reductions at lower latitudes. Climate change projections from three General Circulation Models or GCMs (UKMO-HadGEM1, INM-GM3.0 and CSIRO-Mk3.1) for the A1FI SIZES emission scenario for 2000 to 2100 were downscaled at a northern latitude location (Foulum, Denmark) using LARS-WG5.3. The scenarios accounted for changes in temperature, precipitation and atmospheric CO2 concentration. In addition, three temperature-variability scenarios were included assuming different levels of decreased temperature variability in winter and increased in summer. Crop yield was simulated for the different climate change scenarios by a calibrated version of AFRCWHEAT2 to model several combinations of genotypes (varying in crop growth, development and tolerance to water and nitrogen scarcity) and management (sowing dates and nitrogen fertilization rate). The simulations showed a slight improvement of grain yields (0.3-1.2 Mg ha(-1)) in the medium-term (2030-2050), but not enough to cope with expected increases in demand for food and feed. Optimum management added up to 1.8 Mg ha(-1). Genetic modifications regarding winter wheat crop development exhibit the greatest sensitivity to climate and larger potential for improvement (+3.8 Mg ha(-1)). The results consistently points towards need for cultivars with a longer reproductive phases (2.9-7.5% per 1 degrees C) and lower photoperiod sensitivities. Due to the positive synergies between several genotypic characteristics, multiple-target breeding programmes would be necessary, possibly assisted by model-based assessments of optimal phenotypic characteristics. |
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0168-1923 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4630 |
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Sanz-Cobena, A.; Sánchez-Martín, L.; García-Torres, L.; Vallejo, A. |
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Gaseous emissions of N2O and NO and NO3 − leaching from urea applied with urease and nitrification inhibitors to a maize (Zea mays) crop |
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Journal Article |
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Year |
2012 |
Publication |
Agriculture, Ecosystems and Environment |
Abbreviated Journal |
Agric. Ecosyst. Environ. |
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149 |
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64-73 |
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Urease inhibitor; Nitrogen losses; Irrigation; Nitrification |
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Urea has become the predominant source of synthetic nitrogen (N) fertilizer used throughout the world. Among the various available mitigation tools, urease inhibitors like NBPT have the most potential to improve efficiency of urea by reducing N losses, mainly via ammonia volatilization. However, there is a lack of information on the effect of N-(n-butyl) thiophosphoric triamide (NBPT) on other N losses such as gaseous emissions of N2O and NO and NO3− leaching. A two-year field experiment using irrigated maize (Zea mays) crop was carried out under Mediterranean conditions to evaluate the effectiveness of urea coated with NBPT (0.4%, w/w) alone and with both NBPT and nitrification inhibitor dicyandiamide (DCD) (0.4 and 3%, w/w, respectively) to mitigate N2O–N, NO–N and NO3−–N losses. The different treatments of U, U+NBPT and U+NBPT+DCD were applied to the maize crop in 2009 and then in 2010. The 2010 maize crop followed a fallow period, during which the 2009 crop residues were incorporated into the soil. Two different irrigation regimes were followed each year. In 2009, irrigation was controlled for the first 2 weeks following urea fertilization; whereas, the 2010 crop period was characterized by increased irrigation in the same period. After each treatment application, measurements of the changes in soil mineral N, gaseous emissions of N2O and NO, nitrate leaching and biomass production were made. N2O emissions were effectively abated by NBPT and NBPT+DCD and were reduced by 54 and 24%, respectively, in 2009. A reduction in nitrification rate by the inhibitors was also observed during 2009. In 2010 cropping period, NBPT reduced N2O emissions by 4%; while the combination of NBPT and DCD treatment reduced N2O emission by 43%. Yield-scaled N2O emissions were reduced by 50 and 18% by NBPT and the mixture of NBPT+DCD, respectively, in 2009. Applying inhibitors did not have any significant effect on yield-scaled N2O emissions in the 2010 crop period. Total NO losses from urea were 2.25 kg NO–N ha−1 in the 2009 crop period and 5 times lower in the following year; this may provide an indicator of the prevalence of nitrification as the main process in the production of N2O in the 2009 maize crop. Most of the NO3− was lost within the fallow period (i.e. 92, 81 and 75% of the total NO3− leached for U, U+NBPT and U+NBPT+DCD, respectively), so the incorporation of crop residues was not as effective as expected at reducing these N losses. Our study suggests that the effectiveness of NBPT and combination of NBPT+DCD in reducing N losses from applied urea is influenced by management practices, such as irrigation, and climatic conditions. |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4593 |
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