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Dumont, B.; Leemans, V.; Mansouri, M.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Title |
Parameter identification of the STICS crop model, using an accelerated formal MCMC approach |
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Journal Article |
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Year |
2014 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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52 |
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121-135 |
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Keywords |
crop model; parameter estimation; bayes; stics; dream; global sensitivity-analysis; simulation-model; nitrogen balances; bayesian-approach; generic model; wheat; prediction; water; optimization; algorithm |
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Abstract  |
This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modelling. (C) 2013 Elsevier Ltd. All rights reserved. |
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1364-8152 |
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CropM |
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MA @ admin @ |
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4520 |
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Webber, H.; Ewert, F.; Olesen, J.E.; Müller, C.; Fronzek, S.; Ruane, A.C.; Bourgault, M.; Martre, P.; Ababaei, B.; Bindi, M.; Ferrise, R.; Finger, R.; Fodor, N.; Gabaldón-Leal, C.; Gaiser, T.; Jabloun, M.; Kersebaum, K.-C.; Lizaso, J.I.; Lorite, I.J.; Manceau, L.; Moriondo, M.; Nendel, C.; Rodríguez, A.; Ruiz-Ramos, M.; Semenov, M.A.; Siebert, S.; Stella, T.; Stratonovitch, P.; Trombi, G.; Wallach, D. |
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Title |
Diverging importance of drought stress for maize and winter wheat in Europe |
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Journal Article |
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Year |
2018 |
Publication |
Nature Communications |
Abbreviated Journal |
Nat. Comm. |
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Volume |
9 |
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Pages |
4249 |
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Keywords |
Climate-Change Impacts; Air CO2 Enrichment; Food Security; Heat-Stress; Nitrogen Dynamics; Semiarid Environments; Canopy Temperature; Simulation-Model; Crop Production; Elevated CO2 |
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Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years. |
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2018-10-25 |
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2041-1723 |
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CropM, ft_macsur |
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MA @ admin @ |
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5211 |
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Sanz-Cobena, A.; Sánchez-Martín, L.; García-Torres, L.; Vallejo, A. |
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Gaseous emissions of N2O and NO and NO3 − leaching from urea applied with urease and nitrification inhibitors to a maize (Zea mays) crop |
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Journal Article |
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2012 |
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Agriculture, Ecosystems and Environment |
Abbreviated Journal |
Agric. Ecosyst. Environ. |
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149 |
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64-73 |
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Urease inhibitor; Nitrogen losses; Irrigation; Nitrification |
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Urea has become the predominant source of synthetic nitrogen (N) fertilizer used throughout the world. Among the various available mitigation tools, urease inhibitors like NBPT have the most potential to improve efficiency of urea by reducing N losses, mainly via ammonia volatilization. However, there is a lack of information on the effect of N-(n-butyl) thiophosphoric triamide (NBPT) on other N losses such as gaseous emissions of N2O and NO and NO3− leaching. A two-year field experiment using irrigated maize (Zea mays) crop was carried out under Mediterranean conditions to evaluate the effectiveness of urea coated with NBPT (0.4%, w/w) alone and with both NBPT and nitrification inhibitor dicyandiamide (DCD) (0.4 and 3%, w/w, respectively) to mitigate N2O–N, NO–N and NO3−–N losses. The different treatments of U, U+NBPT and U+NBPT+DCD were applied to the maize crop in 2009 and then in 2010. The 2010 maize crop followed a fallow period, during which the 2009 crop residues were incorporated into the soil. Two different irrigation regimes were followed each year. In 2009, irrigation was controlled for the first 2 weeks following urea fertilization; whereas, the 2010 crop period was characterized by increased irrigation in the same period. After each treatment application, measurements of the changes in soil mineral N, gaseous emissions of N2O and NO, nitrate leaching and biomass production were made. N2O emissions were effectively abated by NBPT and NBPT+DCD and were reduced by 54 and 24%, respectively, in 2009. A reduction in nitrification rate by the inhibitors was also observed during 2009. In 2010 cropping period, NBPT reduced N2O emissions by 4%; while the combination of NBPT and DCD treatment reduced N2O emission by 43%. Yield-scaled N2O emissions were reduced by 50 and 18% by NBPT and the mixture of NBPT+DCD, respectively, in 2009. Applying inhibitors did not have any significant effect on yield-scaled N2O emissions in the 2010 crop period. Total NO losses from urea were 2.25 kg NO–N ha−1 in the 2009 crop period and 5 times lower in the following year; this may provide an indicator of the prevalence of nitrification as the main process in the production of N2O in the 2009 maize crop. Most of the NO3− was lost within the fallow period (i.e. 92, 81 and 75% of the total NO3− leached for U, U+NBPT and U+NBPT+DCD, respectively), so the incorporation of crop residues was not as effective as expected at reducing these N losses. Our study suggests that the effectiveness of NBPT and combination of NBPT+DCD in reducing N losses from applied urea is influenced by management practices, such as irrigation, and climatic conditions. |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4593 |
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Bai, H.; Tao, F.; Xiao, D.; Liu, F.; Zhang, H. |
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Attribution of yield change for rice-wheat rotation system in China to climate change, cultivars and agronomic management in the past three decades |
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Journal Article |
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Year |
2016 |
Publication |
Climatic Change |
Abbreviated Journal |
Clim. Change |
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Volume |
135 |
Issue |
3-4 |
Pages |
539-553 |
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Keywords |
nitrogen-use efficiency; crop yields; winter-wheat; temperature; responses; impacts; decline; models; trends; plain |
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Using the detailed field experiment data from 1981 to 2009 at four representative agro-meteorological experiment stations in China, along with the Agricultural Production System Simulator (APSIM) rice-wheat model, we evaluated the impact of sowing/transplanting date on phenology and yield of rice-wheat rotation system (RWRS). We also disentangled the contributions of climate change, modern cultivars, sowing/transplanting density and fertilization management, as well as changes in each climate variables, to yield change in RWRS, in the past three decades. We found that change in sowing/transplanting date did not significantly affect rice and wheat yield in RWRS, although alleviated the negative impact of climate change to some extent. From 1981 to 2009, climate change jointly caused rice and wheat yield change by -17.4 to 1.5 %, of which increase in temperature reduced yield by 0.0-5.8 % and decrease in solar radiation reduced it by 1.5-8.7 %. Cultivars renewal, modern sowing/transplanting density and fertilization management contributed to yield change by 14.4-27.2, -4.7- -0.1 and 2.3-22.2 %, respectively. Our findings highlight that modern cultivars and agronomic management compensated the negative impacts of climate change and played key roles in yield increase in the past three decades. |
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2016-06-01 |
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0165-0009 |
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CropM, ft_macsur |
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MA @ admin @ |
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4736 |
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Ruane, A.C.; Hudson, N.I.; Asseng, S.; Camarrano, D.; Ewert, F.; Martre, P.; Boote, K.J.; Thorburn, P.J.; Aggarwal, P.K.; Angulo, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Brisson, N.; Challinor, &rew J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.F.; Heng, L.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Kersebaum, K.C.; Kumar, S.N.; Müller, C.; Nendel, C.; O’Leary, G.; Olesen, J.E.; Osborne, T.M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Rötter, R.P.; Semenov, M.A.; Shcherbak, I.; Steduto, P.; Stöckle, C.O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; Wallach, D.; White, J.W.; Wolf, J. |
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Title |
Multi-wheat-model ensemble responses to interannual climate variability |
Type |
Journal Article |
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Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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81 |
Issue |
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86-101 |
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Keywords |
Crop modeling; Uncertainty; Multi-model ensemble; Wheat; AgMIP; Climate; impacts; Temperature; Precipitation; lnterannual variability; simulation-model; crop model; nitrogen dynamics; winter-wheat; large-area; systems simulation; farming systems; yield response; growth; water |
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We compare 27 wheat models’ yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models’ climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R-2 <= 0.24) was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts. Published by Elsevier Ltd. |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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4769 |
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