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Webber, H.; Gaiser, T.; Oomen, R.; Teixeira, E.; Zhao, G.; Wallach, D.; Zimmermann, A.; Ewert, F. |
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Title |
Uncertainty in future irrigation water demand and risk of crop failure for maize in Europe |
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Journal Article |
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2016 |
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Environmental Research Letters |
Abbreviated Journal |
Environ. Res. Lett. |
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Keywords |
crop model; impact assessment; crop water use; evapotranspiration; irrigation; drought; uncertainty |
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While crop models are widely used to assess the change in crop productivity with climate change, their skill in assessing irrigation water demand or the risk of crop failure in large area impact assessments is relatively unknown. The objective of this study is to investigate which aspects of modeling crop water use (reference crop evapotranspiration (ET0), soil water extraction, soil evaporation, soil water balance and root growth) contributes most to the variability in estimates of maize crop water use and the risk of crop failure, and demonstrate the resulting uncertainty in a climate change impact study for Europe. The SIMPLACE crop modeling framework was used to couple the LINTUL5 crop model in factorial combinations of 2-3 different approaches for simulating the 5 aspects of crop water use, resulting in 51 modeling approaches. Using experiments in France and New Zeland, analysis of total sensitivity revealed that ET0 explained the most variability in both irrigated maize water use and rainfed grain yield levels, with soil evaporation also imporatant in the French experiment. In the European impact study, net irrigation requirement differed by 36% between the Penman and Hargreaves ET0 methods in the baseline period. Average EU grain yields were similar between models, but differences approached 1-2 tonnes in parts of France and Southern Europe. EU wide esimates of crop failure in the historical period ranged between 5.4 years for Priestley-Taylor to every 7.9 years for the Penman ET0 methods. While the uncertainty in absolute values between models was significant, estimates of relative changes were similar between models, confirming the utility of crop models in assessing climate change impacts. If ET0 estimates in crop models can be improved, through the use of appropriate methods, uncertainty in irrigation water demand as well as in yield estimates under drought can be reduced. |
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2016-09-13 |
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Newsletter July |
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CropM; wos; ft=macsur; |
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MA @ admin @ |
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4778 |
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Sandor, R.; Ehrhardt, F.; Grace, P.; Recous, S.; Smith, P.; Snow, V.; Soussana, J.-F.; Basso, B.; Bhatia, A.; Brilli, L.; Doltra, J.; Dorich, C.D.; Doro, L.; Fitton, N.; Grant, B.; Harrison, M.T.; Kirschbaum, M.U.F.; Klumpp, K.; Laville, P.; Leonard, J.; Martin, R.; Massad, R.-S.; Moore, A.; Myrgiotis, V.; Pattey, E.; Rolinski, S.; Sharp, J.; Skiba, U.; Smith, W.; Wu, L.; Zhang, Q.; Bellocchi, G. |
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Title |
Ensemble modelling of carbon fluxes in grasslands and croplands |
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Journal Article |
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Year |
2020 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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252 |
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107791 |
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C fluxes; croplands; grasslands; multi-model ensemble; multi-model; median (mmm); soil organic-carbon; greenhouse-gas emissions; climate-change impacts; crop model; data aggregation; use efficiency; n2o emissions; maize; yield; wheat; productivity |
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Croplands and grasslands are agricultural systems that contribute to land–atmosphere exchanges of carbon (C). We evaluated and compared gross primary production (GPP), ecosystem respiration (RECO), net ecosystem exchange (NEE) of CO2, and two derived outputs – C use efficiency (CUE=-NEE/GPP) and C emission intensity (IntC= -NEE/Offtake [grazed or harvested biomass]). The outputs came from 23 models (11 crop-specific, eight grassland-specific, and four models covering both systems) at three cropping sites over several rotations with spring and winter cereals, soybean and rapeseed in Canada, France and India, and two temperate permanent grasslands in France and the United Kingdom. The models were run independently over multi-year simulation periods in five stages (S), either blind with no calibration and initialization data (S1), using historical management and climate for initialization (S2), calibrated against plant data (S3), plant and soil data together (S4), or with the addition of C and N fluxes (S5). Here, we provide a framework to address methodological uncertainties and contextualize results. Most of the models overestimated or underestimated the C fluxes observed during the growing seasons (or the whole years for grasslands), with substantial differences between models. For each simulated variable, changes in the multi-model median (MMM) from S1 to S5 was used as a descriptor of the ensemble performance. Overall, the greatest improvements (MMM approaching the mean of observations) were achieved at S3 or higher calibration stages. For instance, grassland GPP MMM was equal to 1632 g C m−2 yr-1 (S5) while the observed mean was equal to 1763 m-2 yr-1 (average for two sites). Nash-Sutcliffe modelling efficiency coefficients indicated that MMM outperformed individual models in 92.3 % of cases. Our study suggests a cautious use of large-scale, multi-model ensembles to estimate C fluxes in agricultural sites if some site-specific plant and soil observations are available for model calibration. The further development of crop/grassland ensemble modelling will hinge upon the interpretation of results in light of the way models represent the processes underlying C fluxes in complex agricultural systems (grassland and crop rotations including fallow periods). |
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2020-06-08 |
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LiveM |
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MA @ admin @ |
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5230 |
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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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2018 |
Publication |
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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Zheng, B.; Chapman, S.C.; Christopher, J.T.; Frederiks, T.M.; Chenu, K. |
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Frost trends and their estimated impact on yield in the Australian wheatbelt |
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Journal Article |
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2015 |
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Journal of Experimental Botany |
Abbreviated Journal |
J. Experim. Bot. |
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66 |
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12 |
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3611-3623 |
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Keywords |
Adaptation, Physiological/genetics; Australia; Computer Simulation; Ecotype; *Freezing; Genotype; Geography; Seasons; Triticum/genetics/*growth & development/physiology; Breeding; climate change; crop adaptation; crop modelling; ideotype; post-head-emergence frost; reproductive frost; spring radiant frost |
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Radiant spring frosts occurring during reproductive developmental stages can result in catastrophic yield loss for wheat producers. To better understand the spatial and temporal variability of frost, the occurrence and impact of frost events on rain-fed wheat production was estimated across the Australian wheatbelt for 1957-2013 using a 0.05 ° gridded weather data set. Simulated yield outcomes at 60 key locations were compared with those for virtual genotypes with different levels of frost tolerance. Over the last six decades, more frost events, later last frost day, and a significant increase in frost impact on yield were found in certain regions of the Australian wheatbelt, in particular in the South-East and West. Increasing trends in frost-related yield losses were simulated in regions where no significant trend of frost occurrence was observed, due to higher mean temperatures accelerating crop development and causing sensitive post-heading stages to occur earlier, during the frost risk period. Simulations indicated that with frost-tolerant lines the mean national yield could be improved by up to 20% through (i) reduced frost damage (~10% improvement) and (ii) the ability to use earlier sowing dates (adding a further 10% improvement). In the simulations, genotypes with an improved frost tolerance to temperatures 1 °C lower than the current 0 °C reference provided substantial benefit in most cropping regions, while greater tolerance (to 3 °C lower temperatures) brought further benefits in the East. The results indicate that breeding for improved reproductive frost tolerance should remain a priority for the Australian wheat industry, despite warming climates. |
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0022-0957 |
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CropM |
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MA @ admin @ |
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4580 |
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Ramirez-Villegas, J.; Watson, J.; Challinor, A.J. |
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Identifying traits for genotypic adaptation using crop models |
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Journal Article |
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2015 |
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Journal of Experimental Botany |
Abbreviated Journal |
J. Experim. Bot. |
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66 |
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12 |
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3451-3462 |
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Adaptation, Physiological/*genetics; Crops, Agricultural/*genetics; Environment; Genotype; *Models, Theoretical; *Quantitative Trait, Heritable; Climate change; crop models; genotypic adaptation; ideotypes; impacts |
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Genotypic adaptation involves the incorporation of novel traits in crop varieties so as to enhance food productivity and stability and is expected to be one of the most important adaptation strategies to future climate change. Simulation modelling can provide the basis for evaluating the biophysical potential of crop traits for genotypic adaptation. This review focuses on the use of models for assessing the potential benefits of genotypic adaptation as a response strategy to projected climate change impacts. Some key crop responses to the environment, as well as the role of models and model ensembles for assessing impacts and adaptation, are first reviewed. Next, the review describes crop-climate models can help focus the development of future-adapted crop germplasm in breeding programmes. While recently published modelling studies have demonstrated the potential of genotypic adaptation strategies and ideotype design, it is argued that, for model-based studies of genotypic adaptation to be used in crop breeding, it is critical that modelled traits are better grounded in genetic and physiological knowledge. To this aim, two main goals need to be pursued in future studies: (i) a better understanding of plant processes that limit productivity under future climate change; and (ii) a coupling between genetic and crop growth models-perhaps at the expense of the number of traits analysed. Importantly, the latter may imply additional complexity (and likely uncertainty) in crop modelling studies. Hence, appropriately constraining processes and parameters in models and a shift from simply quantifying uncertainty to actually quantifying robustness towards modelling choices are two key aspects that need to be included into future crop model-based analyses of genotypic adaptation. |
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0022-0957 1460-2431 |
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Review |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4645 |
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