Performance of process-based models for simulation of grain N in crop rotations across Europe

Yin, Xiaohong; Kersebaum, Kurt Christian; Kollas, Chris; Manevski, Kiril; Baby, Sanmohan; Beaudoin, Nicolas; Öztürk, Işık; Gaiser, Thomas; Wu, Lianhai; Hoffmann, Munir P.; Charfeddine, Monia; Conradt, Tobias; Constantin, Julie; Ewert, Frank; Cortázar-Atauri, Iñaki García de; Giglio, Luisa; Hlavinka, Petr; Hoffmann, Holger; Launay, Marie; Louarn, Gaëtan; Manderscheid, Remigius; Mary, Bruno; Mirschel, Wilfried; Nendel, Claas; Pacholski, Andreas; Palosuo, Taru; Ripoche-Wachter, Dominique; Rötter, Reimund P.; Ruget, Françoise; Sharif, Behzad; Trnka, Miroslav; Ventrella, Domenico; Weigel, Hans Joachim; Olesen, Jørgen E.

doi:10.1016/j.agsy.2017.03.005

Cited by 41 publications

(20 citation statements)

References 72 publications

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“…LINTUL5 has been widely used in various studies at the field, country, and continental scale [17][18][19][20]. Additionally, the crop model used in the current study has been used in earlier studies, showing its ability to simulate the growth and development of Groundnut (variety "Fleur11") [21] and the crops in rotations [22,23]. The effect of legume crops on the subsequent crops in the rotation is simulated as a fraction of the crop N uptake by biological fixation which was fixed as a parameter in the crop parameter file [21].…”

Section: Model Setup and Descriptionmentioning

confidence: 99%

“…The same holds for nitrogen stress, that is, when the crop available nitrogen in the rooted soil profile is lower than crop nitrogen demand. To simulate a continuous cropping system, the model was embedded into a general modeling framework, SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management) [22]. The SIMPLACE<LINTUL5-SLIM-SoilCN> solution of the modeling platform was used in this study.…”

Section: Model Setup and Descriptionmentioning

confidence: 99%

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Options for Sustainable Intensification of Maize Production in Ethiopia

et al. 2019

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The agricultural intensification of farming systems in sub-Saharan Africa is a prerequisite to alleviate rural poverty and to improve livelihood. In this modelling exercise, we identified sustainable intensification scenarios for maize-based cropping systems in Ethiopia. We evaluated Conventional Intensification (CI) as continuous maize monocropping using higher Mineral Fertilizer (MF) rates with and without the incorporation of Crop Residues (CR) in the soil. We also evaluated the effect of groundnut in rotation with the maize-based cropping system with the current Farmer’s Practice + Rotation (FP + Rotation) and increased MF application rates (CI + Rotation) combined with CR incorporation. The results suggest that, under CI, there was a positive effect of MF and CR. The incorporation of only CR in the field increased the maize yield by 45.3% compared to the farmer’s yield under current MF rates. CR combined with higher MF (60 kg N ha−1 + 20 kg P ha−1) increased the yield by 134.6%. Incorporating CR and MF was also beneficial under rotation with groundnut. The maize yields increased up to 110.1% depending upon the scenarios tested. In the scenario where CR was not incorporated in the field, the maize yield declined by 21.9%. The Gross Economic Profit suggests that groundnut in rotation with maize is advantageous across Ethiopia in terms of the net return with a few exceptions.

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Section: Model Setup and Descriptionmentioning

confidence: 99%

Section: Model Setup and Descriptionmentioning

confidence: 99%

Options for Sustainable Intensification of Maize Production in Ethiopia

et al. 2019

Self Cite

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“…The models were calibrated using different methodologies and averaging their results aimed to minimise variation (or error) due to model and/or modeler (Confalonieri et al, 2016). Yet, averaging simulation outputs from different models that used identical calibration data is not of statistical importance, despite recent studies reporting that mean/median of models ensemble compensates for individual model errors (Martre et al, 2015;Yin et al, 2017a;Yin et al, 2017b). This is because models using the same data for their optimisation are not statistically independent variables.…”

Section: Simulated Water Balance and Modelling Uncertaintiesmentioning

confidence: 99%

Effect of poplar trees on nitrogen and water balance in outdoor pig production – A case study in Denmark

Manevski

Jakobsen

Kongsted

et al. 2019

Science of The Total Environment

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Nitrate leaching from outdoor pig production is a long-standing environmental problem for surface and groundwater pollution. In this study, the effects of inclusion of poplar trees in paddocks for lactating sows on nitrogen (N) balances were studied for an organic pig farm in Denmark. Vegetation conditions, soil water and nitrate dynamics were measured in poplar and grass zones of paddocks belonging to main treatments: access to trees (AT), no access to trees (NAT) and a control without trees (NT), during the hydrological year April 2015 to April 2016. Soil water drainage for each zone, simulated by two simulation models (CoupModel and Daisy), was used to estimate nitrate leaching from the zones in each paddock. N balances (input minus output) for the treatments were computed and compared. The results showed that, in terms of annual water balance and regardless of treatment, simulated evapotranspiration of poplar was 560-569 and 489-498 mm for CoupModel and Daisy, respectively, and corresponding evapotranspiration of grass-clover was 250 and 400 mm, against precipitation of 1076 mm. Simulated drainage below the root zone varied as 620-723 mm for Daisy and 568-958 mm for CoupModel, the higher end of the latter being probably overestimated. Annual nitrate leaching ranged from 32 kg N ha in the poplar zone of NAT up to 289 kg N ha in the control grass zone of NT. The poplar zone showed significantly lower nitrate leaching, by 75-80%, compared to the grass zone. For the control NT treatment, nitrate leaching was approximately 50% higher in the grass zone closest to the hut compared to the grass zone further away. NT treatment also had the largest surface N balance of 468 kg N ha compared to 436 and 397 kg N ha for AT and NAT, respectively. When N losses by leaching and volatilisation were included, soil N balances were 118, 157 and 113 kg N ha for AT, NAT and NT, respectively. Overall, the two simulation models were found useful tools for analyses of water balance for complex agroforestry systems. The findings collectively suggest that it is possible to decrease nitrate leaching from outdoor pig production on sandy soils by inclusion of poplar trees. Additional measures are nevertheless needed to reduce N losses on a mean area basis in paddocks with 20% tree cover.

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“…Yin et al. () utilized a multi‐grain crop model ensemble to simulate grain nitrogen content and concluded that site‐specific calibration results were considerably more accurate and precise as opposed to applying site‐specific results to different locations. They indicated that more observed data from contrasting sites, similar to our R2 process, was essential for improving model simulation for site, climate, and genetic combinations, although it should be noted that this study was focused on grain N concentration and not on yield specifically.…”

Section: Discussionmentioning

confidence: 99%

“…In a multi‐model study examining grain nitrogen concentration in several crops, Yin et al. () observed good accuracy when model simulations were applied at those sites at which they were calibrated for, but deficiencies when this same calibration was extrapolated to other locations. These latter two findings suggest some issues with calibration stability across multiple locations of interest in the studies.…”

Section: Introductionmentioning

confidence: 99%

Cultivar coefficient stability and effects on yield projections in the SPUDSIM model

2020

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Crop model calibration refers to the process where values are obtained for a subset of model parameters that represent cultivar traits. Calibrated parameters, however, are just as likely to compensate for limitations in model structure as well as reflect true phenotypic characteristics. This confounding of genetics with production environment limits model accuracy particularly for climate assessments and can result in crop parameter values that are location dependent. We evaluated the calibration stability of the potato (Solanum tuberosum L.) model SPUDSIM for two cultivars grown in two sites in the United States sites using a location specific (R1) and cross-location (R2) methodology. Differences between R1 coefficient values ranged from 4 to 17% between the two sites depending on cultivar. This likely reflected over-coupling of canopy expansion rate within the model structure as well as an over-sensitivity to temperature and photoperiod. Broader site differences were observed for growth coefficients, particularly when compared with the R2 method. Validation year end-ofseason yields (17% error or less) and in-season RMSE (<36.1 g plant −1 ) were similar for both methods. The R2 calibration overpredicted yields by 5% or less as compared with R1, with a maximum discrepancy of 0.35 Mg ha −1 in climate forecasted impacts. Consistent differences between R1 and R2 calibrated models in rates of yield decline per decade were not observed for cultivar, location, or climate scenario, and averaged 0.034 Mg ha −1 yr −1 for R1 and 0.038 for R2. Thus, calibration for these cultivars and locations was relatively stable in SPUDSIM for the temperate climate evaluated.

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Performance of process-based models for simulation of grain N in crop rotations across Europe

Cited by 41 publications

References 72 publications

Options for Sustainable Intensification of Maize Production in Ethiopia

Options for Sustainable Intensification of Maize Production in Ethiopia

Effect of poplar trees on nitrogen and water balance in outdoor pig production – A case study in Denmark

Cultivar coefficient stability and effects on yield projections in the SPUDSIM model

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