| Records |
| Author |
Perego, A.; Giussani, A.; Sanna, M.; Fumagalli, M.; Carozzi, M.; Alfieri, L.; Brenna, S.; Acutis, M. |
| Title |
The ARMOSA simulation crop model: overall features, calibration and validation results |
Type |
Journal Article |
| Year |
2013 |
Publication |
Italian Journal of Agrometeorology |
Abbreviated Journal |
Italian Journal of Agrometeorology |
| Volume |
3 |
Issue |
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Pages |
23-38 |
Keywords  |
simulation model; crop growth; water dynamics; nitrogen leaching; performance assessment; nitrogen dilution curve; field-scale; soil; systems; maize; water; dynamics; growth; winter; evaporation |
| Abstract |
ARMOSA is a dynamic simulation model which was developed to simulate crop growth and development, water and nitrogen dynamics under different pedoclimatic conditions and cropping systems in the arable land. The model is meant to be a tool for the evaluation of the impact of different crop management practices on soil nitrogen and carbon cycles and groundwater nitrate pollution. A large data set collected over three to six years from six monitoring sites in Lombardia plain was used to calibrate and validate the model parameters. Measured meteorological data, soil chemical and physical characterizations, crop-related data of different cropping systems allowed for a proper parameterization. Fit indexes showed the reliability of the model in adequately predicting crop-related variables, such as above ground biomass (RRMSE=11.18, EF=0.94, r=0.97), Leaf Area Index maximum value (RRMSE=8.24, EF=0.37, r=0.72), harvest index (RRMSE=19.4, EF=0.32, r=0.74), and crop N uptake (RRMSE=20.25, EF=0.69, r=0.85). Using two different one-year data set from each monitoring site, the model was calibrated and validated, getting to encouraging results: RRMSE=6.28, EF=0.52, r=0.68 for soil water content at different depths, and RRMSE=34.89, EF=0.59, r=0.75 for soil NO3-N content along soil profile. The simulated N leaching was in full agreement with measured data (RRMSE=26.62, EF=0.88, r=0.98). |
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English |
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Series Issue |
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Edition |
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| ISSN |
2038-5625 |
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Article |
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| Notes |
CropM, ftnotmacsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4612 |
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| Author |
Fan, F.; Henriksen, C.B.; Porter, J. |
| Title |
Valuation of ecosystem services in organic cereal crop production systems with different management practices in relation to organic matter input |
Type |
Journal Article |
| Year |
2016 |
Publication |
Ecosystem Services |
Abbreviated Journal |
Ecosystem Services |
| Volume |
22 |
Issue |
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Pages |
117-127 |
| Keywords |
soil physical-properties; carbon sequestration; microbial biomass; farming systems; nitrogen mineralization; earthworm populations; straw; incorporation; economic valuation; agricultural soils; different tillage; Organic farming; Ecosystem services; Economic valuation; Management; Informed decision making |
| Abstract |
As the degradation of global ecosystem services (ES) continues in the last five decades, maintaining or even enhancing the ES of agro-ecosystem is one of the approaches to mitigate the global ES loss. This study provides the first estimate of an economic valuation of ES provided by organic cereal crop production systems with different management practices in relation to organic matter input (low, medium and high). Our results show that organic matter inputs significantly affect the total ES value on organic cereal crop production systems. The system with high organic matter input has the highest gross total ES value (US$ 1969 ha(-1) yr(-1)), followed by the low organic matter input system (US$ 1688 ha(-1) yr(-1)), and the lowest ES value are found in the medium organic matter input system (US$ 1492 ha(-1) yr(-1)). Organic matter inputs have strong positive relationship with non-marketable ES values, while this relationship was not found in marketable ES values. Monetizing the ES can be used by land managers and policy makers to adjust management practices in terms of organic matter input in cereal production system with a long term goal for sustainable agriculture. |
| Address |
2017-01-12 |
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English |
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Series Issue |
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Edition |
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| ISSN |
2212-0416 |
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Article |
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| Notes |
CropM, ft_MACSUR |
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no |
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MA @ admin @ |
Serial |
4934 |
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| Author |
Coucheney, E.; Buis, S.; Launay, M.; Constantin, J.; Mary, B.; García de Cortázar-Atauri, I.; Ripoche, D.; Beaudoin, N.; Ruget, F.; &rianarisoa, K.S.; Le Bas, C.; Justes, E.; Léonard, J. |
| Title |
Accuracy, robustness and behavior of the STICS soil–crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France |
Type |
Journal Article |
| Year |
2015 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
| Volume |
64 |
Issue |
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Pages |
177-190 |
| Keywords |
soil-crop model; stics; model performances; plant biomass; soil nitrogen; soil water; remote-sensing data; goodness-of-fit; hydrological model; simulation-models; solar-radiation; regional-scale; climate-change; generic model; data set; validation |
| Abstract |
Soil-crop models are increasingly used as predictive tools to assess yield and environmental impacts of agriculture in a growing diversity of contexts. They are however seldom evaluated at a given time over a wide domain of use. We tested here the performances of the STICS model (v8.2.2) with its standard set of parameters over a dataset covering 15 crops and a wide range of agropedoclimatic conditions in France. Model results showed a good overall accuracy, with little bias. Relative RMSE was larger for soil nitrate (49%) than for plant biomass (35%) and nitrogen (33%) and smallest for soil water (10%). Trends induced by contrasted environmental conditions and management practices were well reproduced. Finally, limited dependency of model errors on crops or environments indicated a satisfactory robustness. Such performances make STICS a valuable tool for studying the effects of changes in agro-ecosystems over the domain explored. (C) 2014 Elsevier Ltd. All rights reserved. |
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English |
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Series Issue |
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Edition |
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| ISSN |
1364-8152 |
ISBN |
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Article |
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Conference |
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| Notes |
CropM |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4554 |
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| Author |
Hoffmann, H.; Zhao, G.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.; Wang, E.; Nendel, C.; Kersebaum, K.C.; Haas, E.; Kiese, R.; Klatt, S.; Eckersten, H.; Vanuytrecht, E.; Kuhnert, M.; Lewan, E.; Rötter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. |
| Title |
Variability of effects of spatial climate data aggregation on regional yield simulation by crop models |
Type |
Journal Article |
| Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
| Volume |
65 |
Issue |
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Pages |
53-69 |
| Keywords |
spatial aggregation effects; crop simulation model; input data; scaling; variability; yield simulation; model comparison; input data aggregation; systems simulation; nitrogen dynamics; data resolution; n2o emissions; winter-wheat; scale; water; impact; apsim |
| Abstract |
Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize) and 3 production situations (potential, water-limited and nitrogen-water-limited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha(-1), whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization. |
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Edition |
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| ISSN |
0936-577x 1616-1572 |
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Article |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4694 |
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| Author |
Dumont, B.; Basso, B.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
| Title |
A comparison of within-season yield prediction algorithms based on crop model behaviour analysis |
Type |
Journal Article |
| Year |
2015 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
| Volume |
204 |
Issue |
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Pages |
10-21 |
| Keywords |
stics crop model; climate variability; lars-wg; yield prediction; log-normal distribution; convergence in law theorem; central limit theorem; weather generator; nitrogen balances; generic model; wheat; simulation; climate; stics; variability; skewness; efficiency |
| Abstract |
The development of methodologies for predicting crop yield, in real-time and in response to different agro-climatic conditions, could help to improve the farm management decision process by providing an analysis of expected yields in relation to the costs of investment in particular practices. Based on the use of crop models, this paper compares the ability of two methodologies to predict wheat yield (Triticum aestivum L.), one based on stochastically generated climatic data and the other on mean climate data. It was shown that the numerical experimental yield distribution could be considered as a log-normal distribution. This function is representative of the overall model behaviour. The lack of statistical differences between the numerical realisations and the logistic curve showed in turn that the Generalised Central Limit Theorem (GCLT) was applicable to our case study. In addition, the predictions obtained using both climatic inputs were found to be similar at the inter and intra-annual time-steps, with the root mean square and normalised deviation values below an acceptable level of 10% in 90% of the climatic situations. The predictive observed lead-times were also similar for both approaches. Given (i) the mathematical formulation of crop models, (ii) the applicability of the CLT and GLTC to the climatic inputs and model outputs, respectively, and (iii) the equivalence of the predictive abilities, it could be concluded that the two methodologies were equally valid in terms of yield prediction. These observations indicated that the Convergence in Law Theorem was applicable in this case study. For purely predictive purposes, the findings favoured an algorithm based on a mean climate approach, which needed far less time (by 300-fold) to run and converge on same predictive lead time than the stochastic approach. (C) 2015 Elsevier B.V. All rights reserved. |
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English |
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Series Issue |
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Edition |
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| ISSN |
0168-1923 |
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CropM |
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no |
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MA @ admin @ |
Serial |
4647 |
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