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Author |
Rötter, R.P.; Appiah, M.; Fichtler, E.; Kersebaum, K.C.; Trnka, M.; Hoffmann, M.P. |
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Title |
Linking modelling and experimentation to better capture crop impacts of agroclimatic extremes-A review |
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Journal Article |
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Year |
2018 |
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Field Crops Research |
Abbreviated Journal |
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221 |
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142-156 |
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Keywords |
ft_macsur; Agroclimatic extremes; Crop model; Heat; Drought; Heavy rain; Anthropogenic Climate-Change; Head-Emergence Frost; Weather Extremes; Wheat Yields; Temperature Variability; Induced Sterility; Food Security; Soil-Moisture; Plant-Growth; Winter-Wheat |
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Climate change implies higher frequency and magnitude of agroclimatic extremes threatening plant production and the provision of other ecosystem services. This review is motivated by a mismatch between advances made regarding deeper understanding of abiotic stress physiology and its incorporation into ecophysiological models in order to more accurately quantifying the impacts of extreme events at crop system or higher aggregation levels. Adverse agroclimatic extremes considered most detrimental to crop production include drought, heat, heavy rains/hail and storm, flooding and frost, and, in particular, combinations of them. Our core question is: How have and could empirical data be exploited to improve the capability of widely used crop simulation models in assessing crop impacts of key agroclimatic extremes for the globally most important grain crops? To date there is no comprehensive review synthesizing available knowledge for a broad range of extremes, grain crops and crop models as a basis for identifying research gaps and prospects. To address these issues, we selected eight major grain crops and performed three systematic reviews using SCOPUS for period 1995-2016. Furthermore, we amended/complemented the reviews manually and performed an in-depth analysis using a sub-sample of papers. Results show that by far the majority of empirical studies (1631 out of 1772) concentrate on the three agroclimatic extremes drought, heat and heavy rain and on the three major staples wheat, maize and rice (1259 out of 1772); the concentration on just a few has increased over time. With respect to modelling studies two model families, i.e. CERES-DSSAT and APSIM, are dearly dominating for wheat and maize; for rice, ORYZA2000 and CERES-Rice predominate and are equally strong. For crops other than maize and wheat the number of studies is small. Empirical and modelling papers don’t differ much in the proportions the various extreme events are dealt with drought and heat stress together account for approx. 80% of the studies. There has been a dramatic increase in the number of papers, especially after 2010. As a way forward, we suggest to have very targeted and well-designed experiments on the specific crop impacts of a given extreme as well as of combinations of them. This in particular refers to extremes addressed with insufficient specificity (e.g. drought) or being under-researched in relation to their economic importance (heavy rains/storm and flooding). Furthermore, we strongly recommend extending research to crops other than wheat, maize and rice. |
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MA @ admin @ |
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5199 |
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Walkiewicz, A.; Bulak, P.; Brzezinska, M.; Wnuk, E.; Bieganowski, A. |
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Title |
Methane oxidation in heavy metal contaminated Mollic Gleysol under oxic and hypoxic conditions |
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Journal Article |
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Year |
2016 |
Publication |
Environmental Pollution |
Abbreviated Journal |
Environ. Pollut. |
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213 |
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403-411 |
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Soil; Methane oxidation; CH4; Heavy metals; Oxygen status; Dehydrogenase; activity; methanotrophic bacteria; dehydrogenase-activity; potential activity; forest soils; responses; landfill; community; ch4; co2; bioremediation |
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Soils are the largest terrestrial sink for methane (CH4). However, heavy metals may exert toxicity to soil microorganisms, including methanotrophic bacteria. We tested the effect of lead (Pb), zinc (Zn) and nickel (Ni) on CH4 oxidation (1% v/v) and dehydrogenase activity, an index of the activity of the total soil microbial community in Mollic Gleysol soil in oxic and hypoxic conditions (oxia and hypoxia, 20% and 10% v/v O2, respectively). Metals were added in doses corresponding to the amounts permitted of Pb, Zn, Ni in agricultural soils (60, 120, 35 mg kg(-1), respectively), and half and double of these doses. Relatively low metal contents and O2 status reflect the conditions of most agricultural soils of temperate regions. Methane consumption showed high tolerance to heavy metals. The effect of O2 status was stronger than that of metals. CH4 consumption was enhanced under hypoxia, where both the start and the completion of the control and contaminated treatment were faster than under oxic conditions. Dehydrogenase activity, showed higher sensitivity to the contamination (except for low Ni dose), with a stronger effect of heavy metals, than that of the O2 status. |
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Newsletter July 2016 |
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0269-7491 |
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CropM, ft_macsur |
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MA @ admin @ |
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4771 |
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Sándor, R.; Barcza, Z.; Acutis, M.; Doro, L.; Hidy, D.; Köchy, M.; Minet, J.; Lellei-Kovács, E.; Ma, S.; Perego, A.; Rolinski, S.; Ruget, F.; Sanna, M.; Seddaiu, G.; Wu, L.; Bellocchi, G. |
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Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance |
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2016 |
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European Journal of Agronomy |
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European Journal of Agronomy |
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Biomass; Grasslands; Modelling; Multi-model ensemble; Soil processes |
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• We simulate biomass, soil water content (SWC) and temperature (ST) in grasslands. • We compare nine models to the multi-model median (MMM) at nine sites. • With model calibration, we obtain satisfactory estimates of ST, less of SWC and biomass. • We observe discrepancies across models in the simulation of grassland processes. • We improve performance with multi-model approach. This study presents results from a major grassland model intercomparison exercise, and highlights the main challenges faced in the implementation of a multi-model ensemble prediction system in grasslands. Nine, independently developed simulation models linking climate, soil, vegetation and management to grassland biogeochemical cycles and production were compared in a simulation of soil water content (SWC) and soil temperature (ST) in the topsoil, and of biomass production. The results were assessed against SWC and ST data from five observational grassland sites representing a range of conditions – Grillenburg in Germany, Laqueuille in France with both extensive and intensive management, Monte Bondone in Italy and Oensingen in Switzerland – and against yield measurements from the same sites and other experimental grassland sites in Europe and Israel. We present a comparison of model estimates from individual models to the multi-model ensemble (represented by multi-model median: MMM). With calibration (seven out of nine models), the performances were acceptable for weekly-aggregated ST (R² > 0.7 with individual models and >0.8–0.9 with MMM), but less satisfactory with SWC (R² < 0.6 with individual models and < ∼ 0.5 with MMM) and biomass (R² < ∼0.3 with both individual models and MMM). With individual models, maximum biases of about −5 °C for ST, −0.3 m3 m−3 for SWC and 360 g DM m−2 for yield, as well as negative modelling efficiencies and some high relative root mean square errors indicate low model performance, especially for biomass. We also found substantial discrepancies across different models, indicating considerable uncertainties regarding the simulation of grassland processes. The multi-model approach allowed for improved performance, but further progress is strongly needed in the way models represent processes in managed grassland systems. |
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1161-0301 |
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LiveM |
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MA @ admin @ |
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4768 |
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Ventrella, D.; Stellacci, A.M.; Castrignanò, A.; Charfeddine, M.; Castellini, M. |
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Effects of crop residue management on winter durum wheat productivity in a long term experiment in Southern Italy |
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Journal Article |
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2016 |
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European Journal of Agronomy |
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European Journal of Agronomy |
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77 |
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188-198 |
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Crop residue incorporation; Crop residue burning; Residual; autocorrelation; Mixed models; soil organic-matter; straw management; yield patterns; use efficiency; grain-yield; nitrogen; quality; systems; rotation; tillage |
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A long-term experiment comparing different crop residue (CR) managements was established in 1977 in Foggia (Apulia region, southern Italy). The objective of this study was to investigate the long-term effects of different types of crop residue management on main yield response parameters in a continuous cropping system of winter durum wheat. In order to correctly interpret the results, models accounting for spatial error autocorrelation were used and compared with ordinary least square models. Eight crop residue management treatments, based on burning of wheat straw and stubble or their incorporation with or without N fertilization and irrigation, were compared. The experimental design was a complete randomized block with five replicates. Results indicated that the dynamics of yield, grain protein content and hectolitric weight of winter durum wheat did not show any decline as usually expected when a monoculture is carried out for a long time. In addition, the temporal variability of productivity was more affected by meteorological factors, such as air temperature and rainfall, than CR management treatments. Higher wheat grain yields and hectolitric weights quite frequently occurred after burning of wheat straw compared with straw incorporation without nitrogen fertilization and autumn irrigation and this was attributed to temporary mineral N immobilization in the soil. The rate of 50 kg ha(-1) of N seemed to counterbalance this negative effect when good condition of soil moisture occurred in the autumn period, so yielding the same productive level of straw burning treatment. (C) 2016 Elsevier B.V. All rights reserved. |
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1161-0301 |
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CropM, ft_macsur |
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MA @ admin @ |
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4770 |
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Zhao, G.; Hoffmann, H.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.L.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.G.; Nendel, C.; Kiese, R.; Eckersten, H.; Haas, E.; Vanuytrecht, E.; Wang, E.; Kuhnert, M.; Trombi, G.; Moriondo, M.; Bindi, M.; Lewan, E.; Bach, M.; Kersebaum, K.C.; Rotter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables |
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2015 |
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Climate Research |
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Clim. Res. |
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65 |
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141-157 |
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crop model; model comparison; spatial resolution; data aggregation; spatial heterogeneity; scaling; climate-change scenarios; sub-saharan africa; winter-wheat; spatial-resolution; yield response; input data; systems simulation; large-scale; soil data; part i |
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We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area. |
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0936-577x |
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CropM, ft_macsur |
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MA @ admin @ |
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4754 |
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