Ludwig Riedesel’s research while affiliated with Julius Kühn-Institut and other places

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Publications (7)


So wird Biodiversität zum Geschäftsmodell
  • Article

November 2024

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14 Reads

Sönke Beckmann

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Axel Wirz

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Florian Tietjens

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Die Zukunft der Landwirtschaft liegt in einer nachhaltigen Bewirtschaftung, die sowohl ökologische als auch wirtschaftliche Ziele verfolgt. Innovative Konzepte bieten vielversprechende Ansätze, um die Biodiversität zu schützen und gleichzeitig die wirtschaftlichen Interessen der Landwirte zu berücksichtigen. Durch eine Kombination verschiedener Förderinstrumente und innovativer Konzepte kann dieses Ziel erreicht werden. Unabhängig dessen heben die verschiedenen Modelle die Bedeutung von Kooperationen und der Zusammenarbeit zwischen öffentlichen und privaten Akteuren hervor. Diese Zusammenarbeit ist entscheidend, um die komplexen Herausforderungen der Biodiversitätsförderung in Agrarökosystemen zu meistern und langfristige Erfolge zu erzielen.


Die Vorteile kooperativer Modelle

November 2024

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35 Reads

Kooperative Modelle bieten einen Lösungsansatz zur Planung, Beantragung und Umsetzung von gemeinschaftlichen Maßnahmen zum Schutz und der Förderung von Biodiversität. Erfolgreich etablierte Kooperativen aus Brandenburg zeigen die Vorteile: Flexibilität, Risikominderung, Wissenstransfer und die Berücksichtigung von naturschutzfachlichen Anforderungen. Landwirte sind in der Regel bereit, Maßnahmen umzusetzen, wenn die Rahmenbedingungen stimmen. Insofern liegt es an der Politik, die entsprechenden Rahmenbedingungen der Fördermechanismen auch in anderen Bundesländern zu schaffen, um überbetriebliche Modelle zu fördern. Eine Zusammenarbeit zwischen allen Beteiligten ist der Schlüssel, um die Herausforderungen der Zukunft zu bewältigen.



Ertragsveränderungen vor dem Hintergrund der Klimakrise und Auswirkungen auf die Flächennutzung Jahresbericht 2023 des Julius Kühn-Instituts und des Thünen-Instituts
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  • Full-text available

August 2024

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87 Reads

Download

Overview of the study design and respective four working steps from ‘Step 1’ listing the data sets used, ‘Step 2’ defining phenological growing periods from different data sets, ‘Step 3’ aggregation of spatiotemporal weather indices (WIs), ‘Step 4’ integration of all data into one comprehensive dataset and ‘Step 5’ statistical analysis.
Overview of phenological stages with BBCH code from the PHASE model (stem elongation), the trial dataset (heading; yellow ripening) and GDD calculations (booting; anthesis; milk ripening; dough ripening) as well as respective stage-to-stage and cross-stage growing periods for WI calculations. Stage-to-stage growing periods are displayed as: stem elongation—booting (SEB; BBCH: 31–39); booting—heading (BH; BBCH: 41–49); heading-anthesis (HA; BBCH: 51–59); anthesis—milk ripening (AMR; BBCH: 60–69); milk ripening—dough ripening (MRDR; BBCH: 71–79); dough ripening—yellow ripening (DRYR; BBCH: 81–87). Cross-stage growing periods are displayed as: stem elongation—anthesis (SEA; BBCH: 31–59); heading—milk ripening (HMR; BBCH: 51–69); heading—dough ripening (HDR; BBCH: 51–79).
Development of the absolute number of days above the threshold for the WI H27D50 (top); H29D30 (middle); H31D10 (bottom) over the growing periods SEB (BBCH 31–39); BH (BBCH 41–49); HA (BBCH 51–59); AMR (BBCH 60–69); MRDR (BBCH 71–79); DRYR (BBCH 81–87) and in the observation period (1993–2021) for wheat (blue) and rye (red). Absolute trend (annual means) is displayed as solid line and statistic trend (linear regression) is displayed as dashed line. Values above the curves display the delta (Δ) in trend development and p values of the trend regression curves are given in brackets with p < .001 = (***); p < .01 = (**); p < .05 = (*); p < .1 = (.); p > .1 = (n.s.). Wheat and rye varieties were mainly grown on different trial sites and are therefore not directly comparable in this study.
Entry date (day of year, DOY) of the phenological stages stem elongation (blue), heading (green), and yellow ripening (yellow) (A) distribution of observed stage entry (DOY) for winter wheat (top) and winter rye (bottom) over the whole observation period (1993–2021). Dashed lines represent median DOY values. (B) Trend development of stage entry (DOY) from 1993 to 2021 for winter wheat (left) and winter rye (right). Absolute trend (annual means) is displayed as solid line and statistic trend (linear regression) is displayed as dashed line. Values display delta (Δ) in absolute trend development and p values of the trend regression curves are given in brackets with p < .001 = (***); p < .01 = (**); p < .05 = (*); p < .1 = (.); p > .1 = (n.s.). Values for trend development of stage entry (4B) are listed in table C in the appendix.
Lengths of the stage-to stage growing periods in the observation period 1993–2021 for wheat (blue) and rye (red). Investigated growing periods are: stem elongation—booting (SEB; BBCH 31–39); booting—heading (BH; BBCH 41–49); heading—anthesis (HA; BBCH 51–59); anthesis—milk ripening (AMR; BBCH 60–69); milk ripening—dough ripening (MRDR; BBCH 71–79); dough ripening—yellow ripening (DRYR; BBCH 81–87). Absolute trend (annual means) is displayed as solid line and statistic trend (linear regression) is displayed as dashed line. Values display delta (Δ) in absolute trend development and p values of the trend regression curves are given in brackets with p < .001 = (***); p < .01 = (**); p < .05 = (*); p < .1 = (.); p > .1 = (n.s.). Wheat and rye varieties were mainly grown on different trial sites and are therefore not directly comparable in this study.

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Site conditions determine heat and drought induced yield losses in wheat and rye in Germany

February 2024

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476 Reads

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6 Citations

Heat and drought are major abiotic stressors threatening cereal yields, but little is known regarding the spatio-temporal development of their yield-effects. In this study, we assess genotype (G) × environment (E) × management (M) specific weather-yield relations utilizing spatially explicit weather indices (WIs) and variety trial yield data of winter wheat (Triticum aestivum) and winter rye (Secale cereale) for all German cereal growing regions and the period 1993-2021. The objectives of this study are to determine the explanatory power of different heat and drought WIs in wheat and rye, to quantify their site-specific yield effects, and to examine the development of stress tolerance from old to new varieties. We use mixed linear models with G × E × M specific covariates as fixed and random factors. We find for both crops that combined heat and drought WIs have the strongest explanatory power during the reproductive phase. Furthermore, our results strongly emphasize the importance of site conditions regarding climate resilience, where poor sites reveal two to three times higher yield losses than sites with high soil quality and high annual precipitation in both crops. Finally, our analysis reveals significantly higher stress-induced absolute yield losses in modern vs. older varieties for both crops, while relative losses also significantly increased in wheat but did not change in rye. Our findings highlight the importance of site conditions and the value of high-yielding locations for global food security. They further underscore the need to integrate site-specific considerations more effectively into agricultural strategies and breeding programs.


Timing and intensity of heat and drought stress determine wheat yield losses in Germany

July 2023

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617 Reads

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15 Citations

Crop yields are increasingly affected by climate change-induced weather extremes in Germany. However, there is still little knowledge of the specific crop-climate relations and respective heat and drought stress-induced yield losses. Therefore, we configure weather indices (WIs) that differ in the timing and intensity of heat and drought stress in wheat (Triticum aestivum L.). We construct these WIs using gridded weather and phenology time series data from 1995 to 2019 and aggregate them with Germany-wide municipality level on-farm wheat yield data. We statistically analyze the WI’s explanatory power and region-specific effect size for wheat yield using linear mixed models. We found the highest explanatory power during the stem elongation and booting phase under moderate drought stress and during the reproductive phase under moderate heat stress. Furthermore, we observed the highest average yield losses due to moderate and extreme heat stress during the reproductive phase. The highest heat and drought stress-induced yield losses were observed in Brandenburg, Saxony-Anhalt, and northern Bavaria, while similar heat and drought stresses cause much lower yield losses in other regions of Germany.


Citations (3)


... The APSIM model simulations predict that temperature rises and changes in precipitation patterns will result in modified growing conditions, particularly in regions with shorter growing seasons, such as the early-maturing areas. These regions are more susceptible to adverse climate effects, such as drought and heat stress, which may reduce crop yields (Zhu, Liu, Qiao et al. 2022;Sun et al. 2023;Riedesel et al. 2024). Photosynthesis is fundamental to crop (Tian et al. 2024). ...

Reference:

Sustainability of Maize‐Soybean Rotation for Future Climate Change Scenarios in Northeast China
Climate change induced heat and drought stress hamper climate change mitigation in German cereal production
  • Citing Article
  • October 2024

Field Crops Research

... To calculate RYL in each rice variety, the yield of non-inoculated plants (NinAMF) in optimal conditions (at the recommended dose of NPK (150:60:60 kg NPK/ha) under continuous flooding (CF)) was taken as yield reference. For each treatment, the following formula was used to estimate the RYL 53 : ...

Site conditions determine heat and drought induced yield losses in wheat and rye in Germany

... The higher volatility of combined crop yields in North-Eastern and Eastern Germany compared with other regions agrees with other studies (Albers et al 2017, Lüttger andFeike 2018). It has been related to comparatively high levels of drought and heat stress and their interaction with regional soil characteristics, i.e. the dominance of light sandy soils with low water holding capacity (Webber et al 2020, Schmitt et al 2022, Riedesel et al 2023. The relatively high area share of rapeseed (supplementary material figure A3(C)), which had a higher yield variance compared to wheat or barley (supplementary material figure A1(C)), likely contributed to the higher yield volatilities (Rondanini et al 2012). ...

Timing and intensity of heat and drought stress determine wheat yield losses in Germany