| Records |
| Author |
Zhao, G.; Hoffmann, H.; Yeluripati, J.; Xenia, S.; Nendel, C.; Coucheney, E.; Kuhnert, M.; Tao, F.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Kiese, R.; Eckersten, H.; Haas, E.; Cammarano, D.; Kassie, B.; Moriondo, M.; Trombi, G.; Bindi, M.; Biernath, C.; Heinlein, F.; Klein, C.; Priesack, E.; Lewan, E.; Kersebaum, K.-C.; Rötter, R.; Roggero, P.P.; Wallach, D.; Asseng, S.; Siebert, S.; Gaiser, T.; Ewert, F. |
| Title |
Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops |
Type  |
Journal Article |
| Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
| Volume |
80 |
Issue |
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Pages |
100-112 |
| Keywords |
Crop model; Stratified random sampling; Simple random sampling; Clustering; Up-scaling; Model comparison; Precision gain; species distribution models; systems simulation; weather data; large-scale; design; soil; optimization; growth; apsim; autocorrelation |
| Abstract |
We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known. |
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English |
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Edition |
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| ISSN |
1364-8152 |
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Article |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4724 |
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| Author |
Hoffmann, H.; Zhao, G.; Asseng, S.; Bindi, M.; Biernath, C.; Constantin, J.; Coucheney, E.; Dechow, R.; Doro, L.; Eckersten, H.; Gaiser, T.; Grosz, B.; Heinlein, F.; Kassie, B.T.; Kersebaum, K.-C.; Klein, C.; Kuhnert, M.; Lewan, E.; Moriondo, M.; Nendel, C.; Priesack, E.; Raynal, H.; Roggero, P.P.; Rötter, R.P.; Siebert, S.; Specka, X.; Tao, F.; Teixeira, E.; Trombi, G.; Wallach, D.; Weihermüller, L.; Yeluripati, J.; Ewert, F. |
| Title |
Impact of spatial soil and climate input data aggregation on regional yield simulations |
Type |
Journal Article |
| Year |
2016 |
Publication |
PLoS One |
Abbreviated Journal |
PLoS One |
| Volume |
11 |
Issue |
4 |
Pages |
e0151782 |
| Keywords |
systems simulation; nitrogen dynamics; winter-wheat; crop models; data resolution; scale; water; variability; calibration; weather |
| Abstract |
We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations. |
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| ISSN |
1932-6203 |
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Article |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4725 |
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| Author |
Ebrahimi, E.; Manschadi, A.M.; Neugschwandtner, R.W.; Eitzinger, J.; Thaler, S.; Kaul, H.-P. |
| Title |
Assessing the impact of climate change on crop management in winter wheat – a case study for Eastern Austria |
Type |
Journal Article |
| Year |
2016 |
Publication |
Journal of Agricultural Science |
Abbreviated Journal |
J. Agric. Sci. |
| Volume |
154 |
Issue |
07 |
Pages |
1153-1170 |
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| Abstract |
Climate change is expected to affect optimum agricultural management practices for autumn-sown wheat, especially those related to sowing date and nitrogen (N) fertilization. To assess the direction and quantity of these changes for an important production region in eastern Austria, the agricultural production systems simulator was parameterized, evaluated and subsequently used to predict yield production and grain protein content under current and future conditions. Besides a baseline climate (BL, 1981–2010), climate change scenarios for the period 2035–65 were derived from three Global Circulation Models (GCMs), namely CGMR, IPCM4 and MPEH5, with two emission scenarios, A1B and B1. Crop management scenarios included a combination of three sowing dates (20 September, 20 October, 20 November) with four N fertilizer application rates (60, 120, 160, 200 kg/ha). Each management scenario was run for 100 years of stochastically generated daily weather data. The model satisfactorily simulated productivity as well as water and N use of autumn- and spring-sown wheat crops grown under different N supply levels in the 2010/11 and 2011/12 experimental seasons. Simulated wheat yields under climate change scenarios varied substantially among the three GCMs. While wheat yields for the CGMR model increased slightly above the BL scenario, under IPCM4 projections they were reduced by 29 and 32% with low or high emissions, respectively. Wheat protein appears to increase with highest increments in the climate scenarios causing the largest reductions in grain yield (IPCM4 and MPEH-A1B). Under future climatic conditions, maximum wheat yields were predicted for early sowing (September 20) with 160 kg N/ha applied at earlier dates than the current practice. |
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English |
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| ISSN |
0021-8596 |
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Article |
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| Notes |
TradeM |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4723 |
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| Author |
Brylińska, M.; Sobkowiak, S.; Stefańczyk, E.; Śliwka, J. |
| Title |
Potato cultivation system affects population structure of Phytophthora infestans |
Type |
Journal Article |
| Year |
2016 |
Publication |
Fungal Ecology |
Abbreviated Journal |
Fungal Ecology |
| Volume |
20 |
Issue |
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Pages |
132-143 |
| Keywords |
SSR; Population genetic structure; Late blight; Potato; late blight resistance; mating-type; microsatellite markers; phenotypic diversity; sexual reproduction; genotypic diversity; nordic countries; severe outbreaks; sarpo mira; pathogenicity |
| Abstract |
Phytophthora infestans is one of the most destructive potato pathogens. Many factors influence the population structure of P. infestans, including migration, climate and type of potato cultivation. Here, we analyse 365 P. infestans isolates collected from three regions of Poland over three years. We determined mating type, mitochondrial haplotype, resistance to metalaxyl, virulence and polymorphism at 14 simple sequence repeat (SSR) loci. Analysis of SSR markers showed high genetic diversity associated with this population. Model-based structure analysis grouped 299 unique genotypes into four main clusters. The P. infestans isolates collected from the Mlochow region, which has the most intensive level of potato cultivation, formed a distinct cluster, indicating a strong effect of the cultivation system on pathogen population structure. Three clusters contained isolates with frequent presence of three alleles at one locus, which may affect their capacity for sexual reproduction and preserve groups of fit genotypes that propagate asexually. (C) 2016 Elsevier Ltd and The British Mycological Society. |
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English |
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Series Issue |
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Edition |
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| ISSN |
1754-5048 |
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Article |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4720 |
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| Author |
Liu, X.; Lehtonen, H.; Purola, T.; Pavlova, Y.; Rötter, R.; Palosuo, T. |
| Title |
Dynamic economic modelling of crop rotations with farm management practices under future pest pressure |
Type |
Journal Article |
| Year |
2016 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agricultural Systems |
| Volume |
144 |
Issue |
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Pages |
65-76 |
| Keywords |
Farm management; Dynamic optimization; Crop rotation; Risk aversion; Climate change; Prices; climate-change; sequester carbon; changing climate; food security; challenge; Finland; ensembles; systems; europe; tool |
| Abstract |
Agricultural practice is facing multiple challenges under volatile commodity markets, inevitable climate change, mounting pest pressure and various other environment-related constraints. The objective of this research is to present a dynamic optimization model of crop rotations and farm management and show its suitability for economic analysis over a 30 year time period. In this model, we include management practices such as fertilization, fungicide treatment and liming, and apply it in a region in Southwestern Finland. Results show that (i) growing pest pressure favours the cultivation of wheat-oats and wheat-oilseeds combinations, while (ii) market prices largely determine the crops in the rotation plan and the specific management practices adopted. The flexibility of our model can also be utilized in evaluating the value of other management options such as new cultivars under different projections of future climate and market conditions. |
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English |
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0308521x |
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| Notes |
CropM, TradeM, ftnotmacsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4719 |
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