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Author |
Schaap, B.F.; Reidsma, P.; Verhagen, J.; Wolf, J.; van Ittersum, M.K. |
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Title |
Participatory design of farm level adaptation to climate risks in an arable region in The Netherlands |
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Journal Article |
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Year |
2013 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
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Volume |
48 |
Issue  |
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Pages |
30-42 |
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Keywords |
adaptation; climate change; impact; crop production; wheat; onion; potato; sugar beet; crop production; change impacts; agriculture; variability; events; europe; model |
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Abstract |
In the arable farming region Flevoland in The Netherlands climate change, including extreme events and pests and diseases, will likely pose risks to a variety of crops including high value crops such as seed potato, ware potato and seed onion. A well designed adaptation strategy at the farm level can reduce risks for farmers in Flevoland. Currently, most of the impact assessments rely heavily on (modelling) techniques that cannot take into account extreme events and pests and diseases and cannot address all crops, and are thus not suited as input for a comprehensive adaptation strategy at the farm level. To identify major climate risks and impacts and develop an adaptation measure portfolio for the most relevant risks we complemented crop growth modelling with a semi-quantitative and participatory approach, the Agro Climatic Calendar (ACC), A cost-benefit analysis and stakeholder workshops were used to identify robust adaptation measures and design an adaptation strategy for contrasting scenarios in 2050. For Flevoland, potential yields of main crops were projected to increase, but five main climate risks were identified, and these are likely to offset the positive impacts. Optimized adaptation strategies differ per scenario (frequency of occurrence of climate risks) and per farm (difference in economic loss). When impacts are high (in the +2 degrees C and A1 SRES scenario) drip irrigation was identified as the best adaptation measure against the main climate risk heat wave that causes second-growth in seed and ware potato. When impacts are smaller (the +1 degrees C and B2 SRES scenario), other options including no adaptation are more cost-effective. Our study shows that with relatively simple techniques such as the ACC combined with a stakeholder process, adaptation strategies can be designed for whole farming systems. Important benefits of this approach compared to modelling techniques are that all crops can be included, all climate factors can be addressed, and a large range of adaptation measures can be explored. This enhances that the identified adaptation strategies are recognizable and relevant for stakeholders. (C) 2013 Elsevier B.V. All rights reserved. |
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2016-10-31 |
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ISSN |
1161-0301 |
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CropM |
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MA @ admin @ |
Serial |
4809 |
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Author |
Höglind, M.; Van Oijen, M.; Cameron, D.; Persson, T. |
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Title |
Process-based simulation of growth and overwintering of grassland using the BASGRA model |
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Journal Article |
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Year |
2016 |
Publication |
Ecological Modelling |
Abbreviated Journal |
Ecol. Model. |
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Volume |
335 |
Issue |
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Pages |
1-15 |
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Keywords |
Cold hardening; Frost injury; Phleum pratense L.; Process-based; modelling; Winter survival; Yield; low-temperature tolerance; perennial forage crops; dry-matter; production; climate-change; nutritive-value; snow-cover; bayesian; calibration; timothy regrowth; phleum-pratense; lolium-perenne |
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Abstract |
Process-based models (PBM) for simulation of weather dependent grass growth can assist farmers and plant breeders in addressing the challenges of climate change by simulating alternative roads of adaptation. They can also provide management decision support under current conditions. A drawback of existing grass models is that they do not take into account the effect of winter stresses, limiting their use for full-year simulations in areas where winter survival is a key factor for yield security. Here, we present a novel full-year PBM for grassland named BASGRA. It was developed by combining the LINGRA grassland model (Van Oijen et al., 2005a) with models for cold hardening and soil physical winter processes. We present the model and show how it was parameterized for timothy (Phleum pratense L.), the most important forage grass in Scandinavia and parts of North America and Asia. Uniquely, BASGRA simulates the processes taking place in the sward during the transition from summer to winter, including growth cessation and gradual cold hardening, and functions for simulating plant injury due to low temperatures, snow and ice affecting regrowth in spring. For the calibration, we used detailed data from five different locations in Norway, covering a wide range of agroclimatic regions, day lengths (latitudes from 59 degrees to 70 degrees N) and soil conditions. The total dataset included 11 variables, notably above-ground dry matter, leaf area index, tiller density, content of C reserves, and frost tolerance. All data were used in the calibration. When BASGRA was run with the maximum a-posteriori (MAP) parameter vector from the single, Bayesian calibration, nearly all measured variables were simulated to an overall normalized root mean squared error (NRMSE) <0.5. For many site x experiment combinations, NRMSE was <0.3. The temporal dynamics were captured well for most variables, as evaluated by comparing simulated time courses versus data for the individual sites. The results may suggest that BASGRA is a reasonably robust model, allowing for simulation of growth and several important underlying processes with acceptable accuracy for a range of agroclimatic conditions. However, the robustness of the model needs to be tested further using independent data from a wide range of growing conditions. Finally we show an example of application of the model, comparing overwintering risks in two climatically different sites, and discuss future model applications. Further development work should include improved simulation of the dynamics of C reserves, and validation of winter tiller dynamics against independent data. (C) 2016 Elsevier B.V. All rights reserved. |
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2016-07-28 |
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ISSN |
0304-3800 |
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Notes |
CropM, LiveM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4764 |
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Author |
Zhao, G.; Siebert, S.; Enders, A.; Rezaei, E.E.; Yan, C.; Ewert, F. |
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Title |
Demand for multi-scale weather data for regional crop modeling |
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Journal Article |
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Year |
2015 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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Volume |
200 |
Issue |
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Pages |
156-171 |
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Keywords |
multi-scale; spatial heterogeneity; spatial resolution; crop model; climate variability; climate-change scenarios; integrated assessment; large-scale; phenological development; agricultural systems; spatial-resolution; data aggregation; european-union; winter-wheat; input data |
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Abstract |
A spatial resolution needs to be determined prior to using models to simulate crop yields at a regional scale, but a dilemma exists in compromising between different demands. A fine spatial resolution demands extensive computation load for input data assembly, model runs, and output analysis. A coarse spatial resolution could result in loss of spatial detail in variability. This paper studied the impact of spatial resolution, data aggregation and spatial heterogeneity of weather data on simulations of crop yields, thus providing guidelines for choosing a proper spatial resolution for simulations of crop yields at regional scale. Using a process-based crop model SIMPLACE (LINTUL2) and daily weather data at 1 km resolution we simulated a continuous rainfed winter wheat cropping system at the national scale of Germany. Then we aggregated the weather data to four resolutions from 10 to 100 km, repeated the simulation, compared them with the 1 km results, and correlated the difference with the intra-pixel heterogeneity quantified by an ensemble of four semivariogram models. Aggregation of weather data had small effects over regions with a flat terrain located in northern Germany, but large effects over southern regions with a complex topography. The spatial distribution of yield bias at different spatial resolutions was consistent with the intra-pixel spatial heterogeneity of the terrain and a log-log linear relationship between them was established. By using this relationship we demonstrated the way to optimize the model resolution to minimize both the number of simulation runs and the expected loss of spatial detail in variability due to aggregation effects. We concluded that a high spatial resolution is desired for regions with high spatial environmental heterogeneity, and vice versa. This calls for the development of multi-scale approaches in regional and global crop modeling. The obtained results require substantiation for other production situations, crops, output variables and for different crop models. (C) 2014 Elsevier B.V. All rights reserved. |
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ISSN |
0168-1923 |
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Notes |
CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4753 |
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Author |
Gabaldón-Leal, C.; Lorite, I.J.; Mínguez, M.I.; Lizaso, J.I.; Dosio, A.; Sanchez, E.; Ruiz-Ramos, M. |
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Title |
Strategies for adapting maize to climate change and extreme temperatures in Andalusia, Spain |
Type |
Journal Article |
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Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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Volume |
65 |
Issue |
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Pages |
159-173 |
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Keywords |
climate change; impact; adaptation; maize; crop model; regional climate model; extreme temperature; elevated carbon-dioxide; iberian peninsula; future climate; mediterranean environment; crop productivity; model simulations; pollen viability; european climate; bias correction; change impacts |
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Abstract |
Climate projections indicate that rising temperatures will affect summer crops in the southern Iberian Peninsula. The aim of this study was to obtain projections of the impacts of rising temperatures, and of higher frequency of extreme events on irrigated maize, and to evaluate some adaptation strategies. The study was conducted at several locations in Andalusia using the CERES-Maize crop model, previously calibrated/validated with local experimental datasets. The simulated climate consisted of projections from regional climate models from the ENSEMBLES project; these were corrected for daily temperature and precipitation with regard to the E-OBS observational dataset. These bias-corrected projections were used with the CERES-Maize model to generate future impacts. Crop model results showed a decrease in maize yield by the end of the 21st century from 6 to 20%, a decrease of up to 25% in irrigation water requirements, and an increase in irrigation water productivity of up to 22%, due to earlier maturity dates and stomatal closure caused by CO2 increase. When adaptation strategies combining earlier sowing dates and cultivar changes were considered, impacts were compensated, and maize yield increased up to 14%, compared with the baseline period (1981-2010), with similar reductions in crop irrigation water requirements. Effects of extreme maximum temperatures rose to 40% at the end of the 21st century, compared with the baseline. Adaptation resulted in an overall reduction in extreme T-max damages in all locations, with the exception of Granada, where losses were limited to 8%. |
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2016-06-01 |
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0936-577x 1616-1572 |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
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4738 |
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Author |
Balkovič, J.; van der Velde, M.; Schmid, E.; Skalský, R.; Khabarov, N.; Obersteiner, M.; Stürmer, B.; Xiong, W. |
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Title |
Pan-European crop modelling with EPIC: Implementation, up-scaling and regional crop yield validation |
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Journal Article |
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Year |
2013 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agricultural Systems |
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Volume |
120 |
Issue |
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Pages |
61-75 |
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Keywords |
EPIC; large-scale crop modelling; model performance testing; EU; climate-change; high-resolution; organic-carbon; growth-model; wheat yield; water; calibration; impacts; productivity; simulations |
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Justifiable usage of large-scale crop model simulations requires transparent, comprehensive and spatially extensive evaluations of their performance and associated accuracy. Simulated crop yields of a Pan-European implementation of the Environmental Policy Integrated Climate (EPIC) crop model were satisfactorily evaluated with reported regional yield data from EUROSTAT for four major crops, including winter wheat, rainfed and irrigated maize, spring barley and winter rye. European-wide land use, elevation, soil and daily meteorological gridded data were integrated in GIS and coupled with EPIC. Default EPIC crop and biophysical process parameter values were used with some minor adjustments according to suggestions from scientific literature. The model performance was improved by spatial calculations of crop sowing densities, potential heat units, operation schedules, and nutrient application rates. EPIC performed reasonable in the simulation of regional crop yields, with long-term averages predicted better than inter-annual variability: linear regression R-2 ranged from 0.58 (maize) to 0.91 (spring barley) and relative estimation errors were between +/- 30% for most of the European regions. The modelled and reported crop yields demonstrated similar responses to driving meteorological variables. However, EPIC performed better in dry compared to wet years. A yield sensitivity analysis of crop nutrient and irrigation management factors and cultivar specific characteristics for contrasting regions in Europe revealed a range in model response and attainable yields. We also show that modelled crop yield is strongly dependent on the chosen PET method. The simulated crop yield variability was lower compared to reported crop yields. This assessment should contribute to the availability of harmonised and transparently evaluated agricultural modelling tools in the EU as well as the establishment of modelling benchmarks as a requirement for sound and ongoing policy evaluations in the agricultural and environmental domains. (C) 2013 The Authors. Published by Elsevier Ltd. All rights reserved. |
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2016-06-01 |
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0308-521x |
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CropM, ft_macsur |
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MA @ admin @ |
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4737 |
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