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Author Kopacz, M.; Twardy, S.
Title (down) Water and sewage management in the upper Dunajec river basin compared to the socio-structural transformations and surface water quality Type Report
Year 2012 Publication Woda Srodowisko Obszary Wiejskie Abbreviated Journal
Volume 123 Issue Pages 103-122
Keywords CropM; LiveM
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Series Editor ITP Falenty Series Title Abbreviated Series Title
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Notes Approved no
Call Number MA @ admin @ Serial 2067
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Author Fereres, A.
Title (down) Virus-vector-host plant interactions: Factors that influence the spread of hemipteran-borne plant viruses Type Conference Article
Year 2013 Publication Abbreviated Journal
Volume Issue Pages
Keywords CropM
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Area Expedition Conference American Phytopathological Society Conference Austin,Texas, 2013-08-10 to 2013-08-14
Notes Approved no
Call Number MA @ admin @ Serial 2434
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Author Dumont, B.; Ferrandis, V., S.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F.
Title (down) Vers un système de prédiction du rendement en temps réel Type Conference Article
Year 2012 Publication Abbreviated Journal
Volume Issue Pages
Keywords CropM
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Area Expedition Conference IXeme Seminaire STICS, Orléans, Sainte Montaine (France)., 2012-10-16 to 2012-10-16
Notes Approved no
Call Number MA @ admin @ Serial 2405
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Author Heinemann, A.B.; Barrios-Perez, C.; Ramirez-Villegas, J.; Arango-Londoño, D.; Bonilla-Findji, O.; Medeiros, J.C.; Jarvis, A.
Title (down) Variation and impact of drought-stress patterns across upland rice target population of environments in Brazil Type Journal Article
Year 2015 Publication Journal of Experimental Botany Abbreviated Journal J. Experim. Bot.
Volume 66 Issue 12 Pages 3625-3638
Keywords Brazil; Climate; Computer Simulation; Crops, Agricultural/physiology; *Droughts; *Environment; Geography; Oryza/*physiology; Plant Transpiration; *Stress, Physiological; Water; Breeding; Oryza sativa; environment classification; modelling; water deficit.
Abstract The upland rice (UR) cropped area in Brazil has decreased in the last decade. Importantly, a portion of this decrease can be attributed to the current UR breeding programme strategy, according to which direct grain yield selection is targeted primarily to the most favourable areas. New strategies for more-efficient crop breeding under non-optimal conditions are needed for Brazil’s UR regions. Such strategies should include a classification of spatio-temporal yield variations in environmental groups, as well as a determination of prevalent drought types and their characteristics (duration, intensity, phenological timing, and physiological effects) within those environmental groups. This study used a process-based crop model to support the Brazilian UR breeding programme in their efforts to adopt a new strategy that accounts for the varying range of environments where UR is currently cultivated. Crop simulations based on a commonly grown cultivar (BRS Primavera) and statistical analyses of simulated yield suggested that the target population of environments can be divided into three groups of environments: a highly favorable environment (HFE, 19% of area), a favorable environment (FE, 44%), and least favourable environment (LFE, 37%). Stress-free conditions dominated the HFE group (69% likelihood) and reproductive stress dominated the LFE group (68% likelihood), whereas reproductive and terminal drought stress were found to be almost equally likely to occur in the FE group. For the best and worst environments, we propose specific adaptation focused on the representative stress, while for the FE, wide adaptation to drought is suggested. ‘Weighted selection’ is also a possible strategy for the FE and LFE environment groups.
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ISSN 0022-0957 1460-2431 ISBN Medium Article
Area Expedition Conference
Notes CropM Approved no
Call Number MA @ admin @ Serial 4560
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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 (down) 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 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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ISSN 0936-577x 1616-1572 ISBN Medium Article
Area Expedition Conference
Notes CropM, ft_macsur Approved no
Call Number MA @ admin @ Serial 4694
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