Siebert, Stefan

[["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.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.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.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"]]

[["dc.bibliographiccitation.artnumber","4049"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Kamali, Bahareh"],["dc.contributor.author","Lorite, Ignacio J."],["dc.contributor.author","Webber, Heidi A."],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.contributor.author","Gabaldon-Leal, Clara"],["dc.contributor.author","Nendel, Claas"],["dc.contributor.author","Siebert, Stefan"],["dc.contributor.author","Ramirez-Cuesta, Juan Miguel"],["dc.contributor.author","Ewert, Frank"],["dc.contributor.author","Ojeda, Jonathan J."],["dc.date.accessioned","2022-04-01T10:01:43Z"],["dc.date.available","2022-04-01T10:01:43Z"],["dc.date.issued","2022"],["dc.description.abstract","Abstract This study investigates the main drivers of uncertainties in simulated irrigated maize yield under historical conditions as well as scenarios of increased temperatures and altered irrigation water availability. Using APSIM, MONICA, and SIMPLACE crop models, we quantified the relative contributions of three irrigation water allocation strategies, three sowing dates, and three maize cultivars to the uncertainty in simulated yields. The water allocation strategies were derived from historical records of farmer’s allocation patterns in drip-irrigation scheme of the Genil-Cabra region, Spain (2014–2017). By considering combinations of allocation strategies, the adjusted R 2 values (showing the degree of agreement between simulated and observed yields) increased by 29% compared to unrealistic assumptions of considering only near optimal or deficit irrigation scheduling. The factor decomposition analysis based on historic climate showed that irrigation strategies was the main driver of uncertainty in simulated yields (66%). However, under temperature increase scenarios, the contribution of crop model and cultivar choice to uncertainty in simulated yields were as important as irrigation strategy. This was partially due to different model structure in processes related to the temperature responses. Our study calls for including information on irrigation strategies conducted by farmers to reduce the uncertainty in simulated yields at field scale."],["dc.identifier.doi","10.1038/s41598-022-08056-9"],["dc.identifier.pii","8056"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/105736"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-530"],["dc.relation.eissn","2045-2322"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Uncertainty in climate change impact studies for irrigated maize cropping systems in southern Spain"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","135589"],["dc.bibliographiccitation.journal","Science of The Total Environment"],["dc.bibliographiccitation.volume","710"],["dc.contributor.author","Ojeda, Jonathan J."],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.contributor.author","Remenyi, Tomas A."],["dc.contributor.author","Webb, Mathew A."],["dc.contributor.author","Webber, Heidi A."],["dc.contributor.author","Kamali, Bahareh"],["dc.contributor.author","Harris, Rebecca M.B."],["dc.contributor.author","Brown, Jaclyn N."],["dc.contributor.author","Kidd, Darren B."],["dc.contributor.author","Mohammed, Caroline L."],["dc.contributor.author","Siebert, Stefan"],["dc.contributor.author","Ewert, Frank"],["dc.contributor.author","Meinke, Holger"],["dc.date.accessioned","2020-12-10T15:21:15Z"],["dc.date.available","2020-12-10T15:21:15Z"],["dc.date.issued","2020"],["dc.description.abstract","Input data aggregation affects crop model estimates at the regional level. Previous studies have focused on the impact of aggregating climate data used to compute crop yields. However, little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) on irrigation water requirement (IWR) in cool-temperate and spatially heterogeneous environments. The aims of this study were to quantify DAEc and DAEs of model input data and their combined impacts for simulated irrigated and rainfed yield and IWR. The Agricultural Production Systems sIMulator Next Generation model was applied for the period 1998–2017 across areas suitable for potato (Solanum tuberosum L.) in Tasmania, Australia, using data at 5, 15, 25 and 40 km resolution. Spatial variances of inputs and outputs were evaluated by the relative absolute difference () between the aggregated grids and the 5 km grids. Climate data aggregation resulted in a of 0.7–12.1%, with high values especially for areas with pronounced differences in elevation. The of soil data was higher (5.6–26.3%) than of climate data and was mainly affected by aggregation of organic carbon and maximum plant available water capacity (i.e. the difference between field capacity and wilting point in the effective root zone). For yield estimates, the difference among resolutions (5 km vs. 40 km) was more pronounced for rainfed ( = 14.5%) than irrigated conditions ( = 3.0%). The of IWR was 15.7% when using input data at 40 km resolution. Therefore, reliable simulations of rainfed yield require a higher spatial resolution than simulation of irrigated yields. This needs to be considered when conducting regional modelling studies across Tasmania. This study also highlights the need to separately quantify the impact of input data aggregation on model outputs to inform about data aggregation errors and identify those variables that explain these errors."],["dc.identifier.doi","10.1016/j.scitotenv.2019.135589"],["dc.identifier.issn","0048-9697"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/72961"],["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","Effects of soil- and climate data aggregation on simulated potato yield and irrigation water requirement"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","081001"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","Environmental Research Letters"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Siebert, Stefan"],["dc.contributor.author","Webber, Heidi"],["dc.contributor.author","Rezaei, Ehsan Eyshi"],["dc.date.accessioned","2021-06-14T19:21:27Z"],["dc.date.available","2021-06-14T19:21:27Z"],["dc.date.issued","2017"],["dc.identifier.doi","10.1088/1748-9326/aa7f15"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/87208"],["dc.language.iso","en"],["dc.relation.issn","1748-9326"],["dc.title","Weather impacts on crop yields - searching for simple answers to a complex problem"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]