Siebert, Stefan

[["dc.bibliographiccitation.artnumber","014003"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Environmental Research Letters"],["dc.bibliographiccitation.volume","16"],["dc.contributor.affiliation","Ahrends, Hella Ellen;"],["dc.contributor.affiliation","Siebert, Stefan;"],["dc.contributor.affiliation","Rezaei, Ehsan Eyshi;"],["dc.contributor.affiliation","Seidel, Sabine Julia;"],["dc.contributor.affiliation","Hüging, Hubert;"],["dc.contributor.affiliation","Ewert, Frank;"],["dc.contributor.affiliation","Döring, Thomas;"],["dc.contributor.affiliation","Rueda-Ayala, Victor;"],["dc.contributor.affiliation","Eugster, Werner;"],["dc.contributor.affiliation","Gaiser, Thomas;"],["dc.contributor.author","Ahrends, Hella Ellen"],["dc.contributor.author","Siebert, Stefan"],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.contributor.author","Seidel, Sabine Julia"],["dc.contributor.author","Hüging, Hubert"],["dc.contributor.author","Ewert, Frank"],["dc.contributor.author","Döring, Thomas"],["dc.contributor.author","Rueda-Ayala, Victor"],["dc.contributor.author","Eugster, Werner"],["dc.contributor.author","Gaiser, Thomas"],["dc.date.accessioned","2021-04-14T08:30:10Z"],["dc.date.available","2021-04-14T08:30:10Z"],["dc.date.issued","2021"],["dc.date.updated","2022-02-09T13:18:57Z"],["dc.description.abstract","Yield stability is important for food security and a sustainable crop production, especially under changing climatic conditions. It is well known that the variability of yields is linked to changes in meteorological conditions. However, little is known about the long-term effects of agronomic management strategies, such as the supply of important nutrients. We analysed the stability of four major European crops grown between 1955 and 2008 at a long-term fertilization experiment located in Germany. Six fertilizer treatments ranged from no fertilization over the omission of individual macronutrients to complete mineral fertilization with all major macronutrients (nitrogen, phosphorus, potassium and calcium). Yield stability was estimated for each crop × treatment combination using the relative yield deviation in each year from the corresponding (nonlinear) trend value (relative yield anomalies (RYA)). Stability was lowest for potato, followed by sugar beet and winter wheat and highest for winter rye. Stability was highest when soils had received all nutrients with the standard deviation of RYA being two to three times lower than for unfertilized plots. The omission of nitrogen and potassium was associated with a decrease in yield stability and a decrease in the number of simultaneous positive and negative yield anomalies among treatments. Especially in root crops nutrient supply strongly influenced both annual yield anomalies and changes in anomalies over time. During the second half of the observation period yield stability decreased for sugar beet and increased for winter wheat. Potato yields were more stable during the second period, but only under complete nutrient supply. The critical role of potassium supply for yield stability suggests potential links to changes in the water balance during the last decades. Results demonstrate the need to explicitly consider the response of crops to long-term nutrient supply for understanding and predicting changes in yield stability."],["dc.identifier.doi","10.1088/1748-9326/abc849"],["dc.identifier.eissn","1748-9326"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83134"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.publisher","IOP Publishing"],["dc.relation.eissn","1748-9326"],["dc.relation.orgunit","Department für Nutzpflanzenwissenschaften"],["dc.relation.orgunit","Abteilung Pflanzenbau"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0"],["dc.title","Nutrient supply affects the yield stability of major European crops—a 50 year study"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","108213"],["dc.bibliographiccitation.journal","Field Crops Research"],["dc.bibliographiccitation.volume","270"],["dc.contributor.author","Ojeda, Jonathan J."],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.contributor.author","Kamali, Bahareh"],["dc.contributor.author","McPhee, John"],["dc.contributor.author","Meinke, Holger"],["dc.contributor.author","Siebert, Stefan"],["dc.contributor.author","Webb, Mathew A."],["dc.contributor.author","Ara, Iffat"],["dc.contributor.author","Mulcahy, Frank"],["dc.contributor.author","Ewert, Frank"],["dc.date.accessioned","2021-06-29T14:44:58Z"],["dc.date.available","2021-06-29T14:44:58Z"],["dc.date.issued","2021"],["dc.description.abstract","The uncertainties associated with crop model inputs can affect the spatio-temporal variance of simulated yields, particularly under suboptimal irrigation. The aim of this study was to determine and quantify the main drivers of irrigated potato yield variance; as influenced by crop management practices as well as climate and soil factors. Using a locally calibrated crop model (APSIM), three planting dates × three irrigation strategies (plus a non-water limited treatment) were simulated using 30 years of historical weather data across potato production areas in Tasmania, Australia. We used (i) correlation analysis, (ii) variance decomposition and (iii) maps to visualise the spatial decomposition of variance of potato yield. Our results showed that the implementation of potential irrigation compensated for the impact of planting date on climate drivers of simulated yield and changed the most important yield driving factors from irrigation and planting date to global solar radiation (r = 0.69−0.81). Under early-planting, we found positive correlations of simulated yield vs. global solar radiation (r up to 0.75 under high and medium irrigation) and between the simulated yield and rainfall (r up to 0.55 under low irrigation). In general, a mix of negative and positive correlations were found for minimum and maximum temperature depending on soil type. Using variance decomposition analysis, we found that crop management factors explained the greatest yield variance depending on soil type related to plant available water capacity (PAWC). For soils with high PAWC (>200 mm), most variance was explained by global solar radiation (56–62 %) followed by planting date (43–47 %). However, when PAWC values decreased from 242 mm to 94 mm, the contribution of global solar radiation and planting date were reduced from 62 % to 5.8 % and from 47 % to 4.4 %, respectively, and the contribution of irrigation strategy increased from 0.4%–32.5%. We identified a need to quantify and differentiate the variance contribution of environmental and crop management factors on crop yield and within these factors, discriminate the key drivers of yield variance at regional scale."],["dc.identifier.doi","10.1016/j.fcr.2021.108213"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/87408"],["dc.relation.issn","0378-4290"],["dc.relation.orgunit","Abteilung Pflanzenbau"],["dc.relation.orgunit","Department für Nutzpflanzenwissenschaften"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.title","Impact of crop management and environment on the spatio-temporal variance of potato yield at regional scale"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","108582"],["dc.bibliographiccitation.journal","Field Crops Research"],["dc.bibliographiccitation.volume","284"],["dc.contributor.author","Zhu, Wanxue"],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.contributor.author","Nouri, Hamideh"],["dc.contributor.author","Sun, Zhigang"],["dc.contributor.author","Li, Jing"],["dc.contributor.author","Yu, Danyang"],["dc.contributor.author","Siebert, Stefan"],["dc.date.accessioned","2022-07-20T16:36:02Z"],["dc.date.available","2022-07-20T16:36:02Z"],["dc.date.issued","2022-08"],["dc.description.abstract","Unmanned aerial vehicle (UAV) remote sensing and machine learning have emerged as a practical approach with ultra-high temporal and spatial resolutions to overcome the limitations of ground-based sampling for continuous crop monitoring. However, little is known on the suitability of distinct sensing indices for different crop management and distinct crop development phases. In this study, we assessed the potential of the UAV-based modeling to monitor field-scale crop growth under different water and nutrient supply considering distinct phenological phases of maize. UAV multispectral observations were deployed over two long-term experimental sites in three growing seasons. Calibration and validation of the random forest model took place at the Nutrient Balance Experimental Site (NBES) and the Water Nitrogen Crop Relation Site (WNCR), respectively. Leaf area index, leaf chlorophyll concentration, and aboveground dry matter were measured at the jointing, heading, and grain filling phases of maize in 2018–2020. Our results revealed that the suitability of sensing indicators differed at distinct maize phenological phases. Overall, red edge, red edge reflectance ratio, and chlorophyll index green are the most appropriate UAV indicators for estimating maize growth variables. The random forest model developed and calibrated at NBES with nutrient supply detected the signal of nitrogen × irrigation interactions at the other experimental site (WNCR) in different development phases and years very well, suggesting that random forest models developed by UAV images of same spatial and spectral attributes could be transferred across sites with the same cultivar while different irrigation and fertilizer management. We conclude that the selected number of UAV detected indicators processed with a random forest model could be used for robustly estimating environment × management (fertilizer and irrigation) interactions on maize growth variables."],["dc.identifier.doi","10.1016/j.fcr.2022.108582"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112529"],["dc.language.iso","en"],["dc.relation.doi","10.1016/j.fcr.2022.108582"],["dc.relation.issn","0378-4290"],["dc.relation.orgunit","Department für Nutzpflanzenwissenschaften"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.relation.orgunit","Abteilung Pflanzenbau"],["dc.title","UAV-based indicators of crop growth are robust for distinct water and nutrient management but vary between crop development phases"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Environmental Research Letters"],["dc.bibliographiccitation.volume","17"],["dc.contributor.affiliation","Guarin, Jose Rafael;"],["dc.contributor.affiliation","Martre, Pierre;"],["dc.contributor.affiliation","Ewert, Frank;"],["dc.contributor.affiliation","Webber, Heidi;"],["dc.contributor.affiliation","Dueri, Sibylle;"],["dc.contributor.affiliation","Calderini, Daniel;"],["dc.contributor.affiliation","Reynolds, Matthew;"],["dc.contributor.affiliation","Molero, Gemma;"],["dc.contributor.affiliation","Miralles, Daniel;"],["dc.contributor.affiliation","Garcia, Guillermo;"],["dc.contributor.affiliation","Slafer, Gustavo;"],["dc.contributor.affiliation","Giunta, Francesco;"],["dc.contributor.affiliation","Pequeno, Diego N L;"],["dc.contributor.affiliation","Stella, Tommaso;"],["dc.contributor.affiliation","Ahmed, Mukhtar;"],["dc.contributor.affiliation","Alderman, Phillip D;"],["dc.contributor.affiliation","Basso, Bruno;"],["dc.contributor.affiliation","Berger, Andres G;"],["dc.contributor.affiliation","Bindi, Marco;"],["dc.contributor.affiliation","Bracho-Mujica, Gennady;"],["dc.contributor.affiliation","Cammarano, Davide;"],["dc.contributor.affiliation","Chen, Yi;"],["dc.contributor.affiliation","Dumont, Benjamin;"],["dc.contributor.affiliation","Rezaei, Ehsan Eyshi;"],["dc.contributor.affiliation","Fereres, Elias;"],["dc.contributor.affiliation","Ferrise, Roberto;"],["dc.contributor.affiliation","Gaiser, Thomas;"],["dc.contributor.affiliation","Gao, Yujing;"],["dc.contributor.affiliation","Garcia-Vila, Margarita;"],["dc.contributor.affiliation","Gayler, Sebastian;"],["dc.contributor.affiliation","Hochman, Zvi;"],["dc.contributor.affiliation","Hoogenboom, Gerrit;"],["dc.contributor.affiliation","Hunt, Leslie A;"],["dc.contributor.affiliation","Kersebaum, Kurt C;"],["dc.contributor.affiliation","Nendel, Claas;"],["dc.contributor.affiliation","Olesen, Jørgen E;"],["dc.contributor.affiliation","Palosuo, Taru;"],["dc.contributor.affiliation","Priesack, Eckart;"],["dc.contributor.affiliation","Pullens, Johannes W M;"],["dc.contributor.affiliation","Rodríguez, Alfredo;"],["dc.contributor.affiliation","Rötter, Reimund P;"],["dc.contributor.affiliation","Ramos, Margarita Ruiz;"],["dc.contributor.affiliation","Semenov, Mikhail A;"],["dc.contributor.affiliation","Senapati, Nimai;"],["dc.contributor.affiliation","Siebert, Stefan;"],["dc.contributor.affiliation","Srivastava, Amit Kumar;"],["dc.contributor.affiliation","Stöckle, Claudio;"],["dc.contributor.affiliation","Supit, Iwan;"],["dc.contributor.affiliation","Tao, Fulu;"],["dc.contributor.affiliation","Thorburn, Peter;"],["dc.contributor.affiliation","Wang, Enli;"],["dc.contributor.affiliation","Weber, Tobias Karl David;"],["dc.contributor.affiliation","Xiao, Liujun;"],["dc.contributor.affiliation","Zhang, Zhao;"],["dc.contributor.affiliation","Zhao, Chuang;"],["dc.contributor.affiliation","Zhao, Jin;"],["dc.contributor.affiliation","Zhao, Zhigan;"],["dc.contributor.affiliation","Zhu, Yan;"],["dc.contributor.affiliation","Asseng, Senthold;"],["dc.contributor.author","Guarin, Jose Rafael"],["dc.contributor.author","Martre, Pierre"],["dc.contributor.author","Ewert, Frank"],["dc.contributor.author","Webber, Heidi"],["dc.contributor.author","Dueri, Sibylle"],["dc.contributor.author","Calderini, Daniel"],["dc.contributor.author","Reynolds, Matthew"],["dc.contributor.author","Molero, Gemma"],["dc.contributor.author","Miralles, Daniel"],["dc.contributor.author","Garcia, Guillermo"],["dc.contributor.author","Slafer, Gustavo"],["dc.contributor.author","Giunta, Francesco"],["dc.contributor.author","Pequeno, Diego N. L."],["dc.contributor.author","Stella, Tommaso"],["dc.contributor.author","Ahmed, Mukhtar"],["dc.contributor.author","Alderman, Phillip D."],["dc.contributor.author","Basso, Bruno"],["dc.contributor.author","Berger, Andres G."],["dc.contributor.author","Bindi, Marco"],["dc.contributor.author","Bracho-Mujica, Gennady"],["dc.contributor.author","Cammarano, Davide"],["dc.contributor.author","Chen, Yi"],["dc.contributor.author","Dumont, Benjamin"],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.contributor.author","Fereres, Elias"],["dc.contributor.author","Ferrise, Roberto"],["dc.contributor.author","Gaiser, Thomas"],["dc.contributor.author","Gao, Yujing"],["dc.contributor.author","Garcia-Vila, Margarita"],["dc.contributor.author","Gayler, Sebastian"],["dc.contributor.author","Hochman, Zvi"],["dc.contributor.author","Hoogenboom, Gerrit"],["dc.contributor.author","Hunt, Leslie A."],["dc.contributor.author","Kersebaum, Kurt C."],["dc.contributor.author","Nendel, Claas"],["dc.contributor.author","Olesen, Jørgen E."],["dc.contributor.author","Palosuo, Taru"],["dc.contributor.author","Priesack, Eckart"],["dc.contributor.author","Pullens, Johannes W. M."],["dc.contributor.author","Rodríguez, Alfredo"],["dc.contributor.author","Rötter, Reimund P."],["dc.contributor.author","Ramos, Margarita Ruiz"],["dc.contributor.author","Semenov, Mikhail A."],["dc.contributor.author","Senapati, Nimai"],["dc.contributor.author","Siebert, Stefan"],["dc.contributor.author","Srivastava, Amit Kumar"],["dc.contributor.author","Stöckle, Claudio"],["dc.contributor.author","Supit, Iwan"],["dc.contributor.author","Tao, Fulu"],["dc.contributor.author","Thorburn, Peter"],["dc.contributor.author","Wang, Enli"],["dc.contributor.author","Weber, Tobias Karl David"],["dc.contributor.author","Xiao, Liujun"],["dc.contributor.author","Zhang, Zhao"],["dc.contributor.author","Zhao, Chuang"],["dc.contributor.author","Zhao, Jin"],["dc.contributor.author","Zhao, Zhigan"],["dc.contributor.author","Zhu, Yan"],["dc.contributor.author","Asseng, Senthold"],["dc.date.accessioned","2022-12-14T07:22:09Z"],["dc.date.available","2022-12-14T07:22:09Z"],["dc.date.issued","2022-12-12"],["dc.date.updated","2022-12-14T05:58:37Z"],["dc.description.abstract","AbstractWheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges."],["dc.description.sponsorship","Ministry of Education, Youth and Sports of Czech Republic SustES"],["dc.description.sponsorship","International Wheat Yield Partnership (IWYP)"],["dc.description.sponsorship","Biotechnology and Biological Sciences Research Council (BBSRC)"],["dc.description.sponsorship","National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809"],["dc.description.sponsorship","Chilean Technical and Scientific Research Council (CONICYT) FONDECYT Project"],["dc.description.sponsorship","International Maize and Wheat Improvement Center (CIMMYT)"],["dc.identifier.doi","10.1088/1748-9326/aca77c"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/118610"],["dc.language.iso","en"],["dc.relation.eissn","1748-9326"],["dc.rights","CC BY 4.0"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0"],["dc.title","Evidence for increasing global wheat yield potential"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","565"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","International Journal of Biometeorology"],["dc.bibliographiccitation.lastpage","576"],["dc.bibliographiccitation.volume","65"],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.contributor.author","Ghazaryan, Gohar"],["dc.contributor.author","González, Javier"],["dc.contributor.author","Cornish, Natalie"],["dc.contributor.author","Dubovyk, Olena"],["dc.contributor.author","Siebert, Stefan"],["dc.date.accessioned","2021-04-14T08:30:48Z"],["dc.date.available","2021-04-14T08:30:48Z"],["dc.date.issued","2021"],["dc.description.abstract","One of the major sources of uncertainty in large-scale crop modeling is the lack of information capturing the spatiotemporal variability of crop sowing dates. Remote sensing can contribute to reducing such uncertainties by providing essential spatial and temporal information to crop models and improving the accuracy of yield predictions. However, little is known about the impacts of the differences in crop sowing dates estimated by using remote sensing (RS) and other established methods, the uncertainties introduced by the thresholds used in these methods, and the sensitivity of simulated crop yields to these uncertainties in crop sowing dates. In the present study, we performed a systematic sensitivity analysis using various scenarios. The LINTUL-5 crop model implemented in the SIMPLACE modeling platform was applied during the period 2001–2016 to simulate maize yields across four provinces in South Africa using previously defined scenarios of sowing dates. As expected, the selected methodology and the selected threshold considerably influenced the estimated sowing dates (up to 51 days) and resulted in differences in the long-term mean maize yield reaching up to 1.7 t ha−1 (48% of the mean yield) at the province level. Using RS-derived sowing date estimations resulted in a better representation of the yield variability in space and time since the use of RS information not only relies on precipitation but also captures the impacts of socioeconomic factors on the sowing decision, particularly for smallholder farmers. The model was not able to reproduce the observed yield anomalies in Free State (Pearson correlation coefficient: 0.16 to 0.23) and Mpumalanga (Pearson correlation coefficient: 0.11 to 0.18) in South Africa when using fixed and precipitation rule-based sowing date estimations. Further research with high-resolution climate and soil data and ground-based observations is required to better understand the sources of the uncertainties in RS information and to test whether the results presented herein can be generalized among crop models with different levels of complexity and across distinct field crops."],["dc.identifier.doi","10.1007/s00484-020-02050-4"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83376"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1432-1254"],["dc.relation.issn","0020-7128"],["dc.relation.orgunit","Department für Nutzpflanzenwissenschaften"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.relation.orgunit","Abteilung Pflanzenbau"],["dc.title","The use of remote sensing to derive maize sowing dates for large-scale crop yield simulations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","4030"],["dc.bibliographiccitation.issue","24"],["dc.bibliographiccitation.journal","Remote Sensing"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Ghazaryan, Gohar"],["dc.contributor.author","König, Simon"],["dc.contributor.author","Siebert, Stefan"],["dc.contributor.author","Dubovyk, Olena"],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.date.accessioned","2021-04-14T08:22:52Z"],["dc.date.available","2021-04-14T08:22:52Z"],["dc.date.issued","2020"],["dc.description.abstract","Drought is one of the extreme climatic events that has a severe impact on crop production and food supply. Our main goal is to test the suitability of remote sensing-based indices to detect drought impacts on crop production from a global to regional scale. Moderate resolution imaging spectroradiometer (MODIS) based imagery, spanning from 2001 to 2017 was used for this task. This includes the normalized difference vegetation index (NDVI), land surface temperature (LST), and the evaporative stress index (ESI), which is based on the ratio of actual to potential evapotranspiration. These indices were used as indicators of drought-induced vegetation conditions for three main crops: maize, wheat, and soybean. The start and end of the growing season, as observed at 500 m resolution, were used to exclude the time steps that are outside of the growing season. Based on the three indicators, monthly standardized anomalies were estimated, which were used for both analyses of spatiotemporal patterns of drought and the relationship with yield anomalies. Anomalies in the ESI had higher correlations with maize and wheat yield anomalies than other indices, indicating that prolonged periods of low ESI during the growing season are highly correlated with reduced crop yields. All indices could identify past drought events, such as the drought in the USA in 2012, Eastern Africa in 2016–2017, and South Africa in 2015–2016. The results of this study highlight the potential of the use of moderate resolution remote sensing-based indicators combined with phenometrics for drought-induced crop impact monitoring. For several regions, droughts identified using the ESI and LST were more intense than the NDVI-based results. We showed that these indices are relevant for agricultural drought monitoring at both global and regional scales. They can be integrated into drought early warning systems, process-based crop models, as well as can be used for risk assessment and included in advanced decision-support frameworks."],["dc.description.sponsorship","Bundesministerium für Bildung und Forschung"],["dc.identifier.doi","10.3390/rs12244030"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/80721"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.publisher","MDPI"],["dc.relation.eissn","2072-4292"],["dc.relation.orgunit","Department für Nutzpflanzenwissenschaften"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.relation.orgunit","Abteilung Pflanzenbau"],["dc.rights","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Analysis of Drought Impact on Croplands from Global to Regional Scale: A Remote Sensing Approach"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","156"],["dc.bibliographiccitation.journal","Agricultural and Forest Meteorology"],["dc.bibliographiccitation.lastpage","171"],["dc.bibliographiccitation.volume","200"],["dc.contributor.author","Zhao, Gang"],["dc.contributor.author","Siebert, Stefan"],["dc.contributor.author","Enders, Andreas"],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.contributor.author","Yan, Changqing"],["dc.contributor.author","Ewert, Frank"],["dc.date.accessioned","2021-06-29T15:14:00Z"],["dc.date.available","2021-06-29T15:14:00Z"],["dc.date.issued","2015"],["dc.description.abstract","A spatial resolution needs to be determined prior to using models to simulate crop yields at a regional scale, but a dilemma exists in compromising between different demands. A fine spatial resolution demands extensive computation load for input data assembly, model runs, and output analysis. A coarse spatial resolution could result in loss of spatial detail in variability. This paper studied the impact of spatial resolution, data aggregation and spatial heterogeneity of weather data on simulations of crop yields, thus providing guidelines for choosing a proper spatial resolution for simulations of crop yields at regional scale. Using a process-based crop model SIMPLACE 〈LINTUL2〉 and daily weather data at 1 km resolution we simulated a continuous rainfed winter wheat cropping system at the national scale of Germany. Then we aggregated the weather data to four resolutions from 10 to 100 km, repeated the simulation, compared them with the 1 km results, and correlated the difference with the intra-pixel heterogeneity quantified by an ensemble of four semivariogram models. Aggregation of weather data had small effects over regions with a flat terrain located in northern Germany, but large effects over southern regions with a complex topography. The spatial distribution of yield bias at different spatial resolutions was consistent with the intra-pixel spatial heterogeneity of the terrain and a log–log linear relationship between them was established. By using this relationship we demonstrated the way to optimize the model resolution to minimize both the number of simulation runs and the expected loss of spatial detail in variability due to aggregation effects. We concluded that a high spatial resolution is desired for regions with high spatial environmental heterogeneity, and vice versa. This calls for the development of multi-scale approaches in regional and global crop modeling. The obtained results require substantiation for other production situations, crops, output variables and for different crop models."],["dc.identifier.doi","10.1016/j.agrformet.2014.09.026"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/87416"],["dc.language.iso","en"],["dc.relation.issn","0168-1923"],["dc.title","Demand for multi-scale weather data for regional crop modeling"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","93"],["dc.bibliographiccitation.journal","Field Crops Research"],["dc.bibliographiccitation.lastpage","103"],["dc.bibliographiccitation.volume","217"],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.contributor.author","Siebert, Stefan"],["dc.contributor.author","Manderscheid, Remy"],["dc.contributor.author","Müller, Johannes"],["dc.contributor.author","Mahrookashani, Amirhossein"],["dc.contributor.author","Ehrenpfordt, Brigitte"],["dc.contributor.author","Haensch, Josephine"],["dc.contributor.author","Weigel, Hans-Joachim"],["dc.contributor.author","Ewert, Frank"],["dc.date.accessioned","2020-12-10T14:24:02Z"],["dc.date.available","2020-12-10T14:24:02Z"],["dc.date.issued","2018"],["dc.description.abstract","Previous studies suggested a wide range of sensitivities of wheat yields to heat stress around anthesis. The aim of this study was to improve the understanding of the reasons of the disagreement by testing the response of wheat yield and yield components to differences in the method of heating, the temperature measurement point and soil substrate under sole heat and combined heat and drought stress around anthesis. Growth chamber experiments performed at different sites showed that increasing of the ambient air temperature at anthesis corresponding to a temperature sum of 12000 °C min above 31 °C resulted in a significant yield reduction of −24% for plants grown on sandy soil substrate but not for those grown on a soil with high soil water holding capacity. The grain yield of wheat also declined by −16% for sandy soil substrate but at a much lower level of heat stress when the temperature of the ears was increased by infrared heaters (a temperature sum of 1900 °C min above 31 °C). The yield reduction increased significantly under combined heat and drought compared to sole heat stress. Grain number significantly declined in all experiments with heat stress and combined heat and drought stress at anthesis. Single grain weight increased with heat stress around anthesis and partly compensated for lower grain numbers of pots containing a soil with high soil water holding capacity but not in experiments with sandy soil substrate. We demonstrate, based on data from previous heat stress studies, that statistical relationships between crop heat stress and yield loss become stronger when separating the data according to the soil used in the experiments. Our results suggest that the differences in the yield response to heat may be caused by additional drought stress which is difficult to avoid in heat stress experiments using sandy soil substrate. We conclude that differences in the experimental setup of heat stress experiments substantially influence the crop response to heat stress and need to be considered when using the data to calibrate crop models applied for climate change impact assessments."],["dc.identifier.doi","10.1016/j.fcr.2017.12.015"],["dc.identifier.issn","0378-4290"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/72111"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation.orgunit","Department für Nutzpflanzenwissenschaften"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.relation.orgunit","Abteilung Pflanzenbau"],["dc.title","Quantifying the response of wheat yields to heat stress: The role of the experimental setup"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.contributor.author","Siebert, Stefan"],["dc.contributor.author","Hüging, Hubert"],["dc.contributor.author","Ewert, Frank"],["dc.date.accessioned","2020-12-10T18:10:09Z"],["dc.date.available","2020-12-10T18:10:09Z"],["dc.date.issued","2018"],["dc.description.abstract","Changing crop phenology is considered an important bio-indicator of climate change, with the recent warming trend causing an advancement in crop phenology. Little is known about the contributions of changes in sowing dates and cultivars to long-term trends in crop phenology, particularly for winter crops such as winter wheat. Here, we analyze a long-term (1952–2013) dataset of phenological observations across western Germany and observations from a two-year field experiment to directly compare the phenologies of winter wheat cultivars released between 1950 and 2006. We found a 14–18% decline in the temperature sum required from emergence to flowering for the modern cultivars of winter wheat compared with the cultivars grown in the 1950s and 1960s. The trends in the flowering day obtained from a phenology model parameterized with the field observations showed that changes in the mean temperature and cultivar properties contributed similarly to the trends in the flowering day, whereas the effects of changes in the sowing day were negligible. We conclude that the single-cultivar concept commonly used in climate change impact assessments results in an overestimation of winter wheat sensitivity to increasing temperature, which suggests that studies on climate change effects should consider changes in cultivars."],["dc.identifier.doi","10.1038/s41598-018-23101-2"],["dc.identifier.eissn","2045-2322"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15420"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/73866"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Department für Nutzpflanzenwissenschaften"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.relation.orgunit","Abteilung Pflanzenbau"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Climate change effect on wheat phenology depends on cultivar change"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","024012"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Environmental Research Letters"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.contributor.author","Siebert, Stefan"],["dc.contributor.author","Ewert, Frank"],["dc.date.accessioned","2021-06-29T15:13:16Z"],["dc.date.available","2021-06-29T15:13:16Z"],["dc.date.issued","2015"],["dc.description.abstract","Higher temperatures during the growing season are likely to reduce crop yields with implications for crop production and food security. The negative impact of heat stress has also been predicted to increase even further for cereals such as wheat under climate change. Previous empirical modeling studies have focused on the magnitude and frequency of extreme events during the growth period but did not consider the effect of higher temperature on crop phenology. Based on an extensive set of climate and phenology observations for Germany and period 1951–2009, interpolated to 1 × 1 km resolution and provided as supplementary data to this article (available at stacks.iop.org/ERL/10/024012/mmedia), we demonstrate a strong relationship between the mean temperature in spring and the day of heading (DOH) of winter wheat. We show that the cooling effect due to the 14 days earlier DOH almost fully compensates for the adverse effect of global warming on frequency and magnitude of crop heat stress. Earlier heading caused by the warmer spring period can prevent exposure to extreme heat events around anthesis, which is the most sensitive growth stage to heat stress. Consequently, the intensity of heat stress around anthesis in winter crops cultivated in Germany may not increase under climate change even if the number and duration of extreme heat waves increase. However, this does not mean that global warning would not harm crop production because of other impacts, e.g. shortening of the grain filling period. Based on the trends for the last 34 years in Germany, heat stress (stress thermal time) around anthesis would be 59% higher in year 2009 if the effect of high temperatures on accelerating wheat phenology were ignored. We conclude that climate impact assessments need to consider both the effect of high temperature on grain set at anthesis but also on crop phenology."],["dc.identifier.doi","10.1088/1748-9326/10/2/024012"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/87411"],["dc.language.iso","en"],["dc.relation.issn","1748-9326"],["dc.title","Intensity of heat stress in winter wheat—phenology compensates for the adverse effect of global warming"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]