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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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0304-3800 |
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CropM, LiveM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4764 |
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Author |
Ghaley, B.B.; Porter, J.R. |
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Title |
Determination of biomass accumulation in mixed belts of Salix, Corylus and Alnus species in combined food and energy production system |
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Journal Article |
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Year |
2014 |
Publication |
Biomass and Bioenergy |
Abbreviated Journal |
Biomass and Bioenergy |
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Volume |
63 |
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Pages |
86-91 |
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Keywords |
allometric equation; destructive and non-destructive method; stool and biomass yield; bio-energy belts; food and fodder crops; short rotation woody crops; short-rotation forestry; willow; plantations; sweden; coppice; equations; growth; poplar; trees; yield |
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Abstract |
Given the energetic, demographic and the climatic challenges faced today, we designed a combined food and energy (CFE) production system integrating food, fodder and mixed belts of Salix, Alnus and Corylus sp. as bioenergy belts. The objective was to assess the shoot dry weight-stem diameter allometric relationship based on stem diameter at 10 (SD10) and 55 cm (SD55) from the shoot base in the mixed bioenergy belts. Allometric relations based on SD10 and SD55 explained 90-96% and 90-98% of the variation in shoot dry weights respectively with no differences between the destructive and the non-destructive methods. The individual stool yields varied widely among the species and within willow species with biomass yield range of 37.60-92.00 oven dry tons (ODT) ha (1) in 4-year growth cycle. The biomass yield of the bioenergy belt, predicted by allometric relations was 48.84 ODT ha 1 in 4-year growth cycle corresponding to 12.21 ODT ha (1) year (1). The relatively high biomass yield is attributed to the border effects and the ‘fertilizing effect’ of alder due to nitrogen fixation, benefitting other SWRC components. On termination of 4-year growth cycle, the bioenergy belts were harvested and the biomass yield recorded was 12.54 ODT ha (1) year (1), in close proximity to the biomass yield predicted by the allometric equations, lending confidence and robustness of the model for biomass yield determination in such integrated agro-ecosystem. (C) 2014 Elsevier Ltd. All rights reserved. |
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0961-9534 |
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CropM |
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MA @ admin @ |
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4624 |
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Author |
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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Volume |
281 |
Issue |
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Pages |
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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Call Number |
MA @ admin @ |
Serial |
5232 |
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Author |
Constantin, J.; Raynal, H.; Casellas, E.; Hoffman, H.; Bindi, M.; Doro, L.; Eckersten, H.; Gaiser, T.; Grosz, B.; Haas, E.; Kersebaum, K.-C.; Klatt, S.; Kuhnert, M.; Lewan, E.; Maharjan, G.R.; Moriondo, M.; Nendel, C.; Roggero, P.P.; Specka, X.; Trombi, G.; Villa, A.; Wang, E.; Weihermueller, L.; Yeluripati, J.; Zhao, Z.; Ewert, F.; Bergez, J.-E. |
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Title |
Management and spatial resolution effects on yield and water balance at regional scale in crop models |
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Journal Article |
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Year |
2019 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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Volume |
275 |
Issue |
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184-195 |
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Keywords |
Drainage; Evapotranspiration; Aggregation; Decision rules; Scaling; winter-wheat yield; data aggregation; sowing dates; area index; input; data; carbon; growth; irrigation; productivity; assimilation |
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Due to the more frequent use of crop models at regional and national scale, the effects of spatial data input resolution have gained increased attention. However, little is known about the influence of variability in crop management on model outputs. A constant and uniform crop management is often considered over the simulated area and period. This study determines the influence of crop management adapted to climatic conditions and input data resolution on regional-scale outputs of crop models. For this purpose, winter wheat and maize were simulated over 30 years with spatially and temporally uniform management or adaptive management for North Rhine-Westphalia ((similar to)34 083 km(2)), Germany. Adaptive management to local climatic conditions was used for 1) sowing date, 2) N fertilization dates, 3) N amounts, and 4) crop cycle length. Therefore, the models were applied with four different management sets for each crop. Input data for climate, soil and management were selected at five resolutions, from 1 x 1 km to 100 x 100 km grid size. Overall, 11 crop models were used to predict regional mean crop yield, actual evapotranspiration, and drainage. Adaptive management had little effect (< 10% difference) on the 30-year mean of the three output variables for most models and did not depend on soil, climate, and management resolution. Nevertheless, the effect was substantial for certain models, up to 31% on yield, 27% on evapotranspiration, and 12% on drainage compared to the uniform management reference. In general, effects were stronger on yield than on evapotranspiration and drainage, which had little sensitivity to changes in management. Scaling effects were generally lower than management effects on yield and evapotranspiration as opposed to drainage. Despite this trend, sensitivity to management and scaling varied greatly among the models. At the annual scale, effects were stronger in certain years, particularly the management effect on yield. These results imply that depending on the model, the representation of management should be carefully chosen, particularly when simulating yields and for predictions on annual scale. |
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2020-02-14 |
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0168-1923 |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
5225 |
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Author |
Van Oijen, M.; Höglind, M. |
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Title |
Toward a Bayesian procedure for using process-based models in plant breeding, with application to ideotype design |
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Journal Article |
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Year |
2016 |
Publication |
Euphytica |
Abbreviated Journal |
Euphytica |
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Volume |
207 |
Issue |
3 |
Pages |
627-643 |
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Keywords |
BASGRA; cold tolerance; genotype-environment interaction; plant breeding; process-based modelling; yield stability; grassland productivity; timothy regrowth; climate-change; water-deficit; forest models; late blight; leaf-area; calibration; growth; tolerance |
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Process-based grassland models (PBMs) simulate growth and development of vegetation over time. The models tend to have a large number of parameters that represent properties of the plants. To simulate different cultivars of the same species, different parameter values are required. Parameter differences may be interpreted as genetic variation for plant traits. Despite this natural connection between PBMs and plant genetics, there are only few examples of successful use of PBMs in plant breeding. Here we present a new procedure by which PBMs can help design ideotypes, i.e. virtual cultivars that optimally combine properties of existing cultivars. Ideotypes constitute selection targets for breeding. The procedure consists of four steps: (1) Bayesian calibration of model parameters using data from cultivar trials, (2) Estimating genetic variation for parameters from the combination of cultivar-specific calibrated parameter distributions, (3) Identifying parameter combinations that meet breeding objectives, (4) Translating model results to practice, i.e. interpreting parameters in terms of practical selection criteria. We show an application of the procedure to timothy (Phleum pratense L.) as grown in different regions of Norway. |
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2016-10-31 |
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0014-2336 |
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CropM, ft_macsur |
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MA @ admin @ |
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4820 |
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