22 Ergebnisse
Sortierung:
In: Reproductive sciences: RS : the official journal of the Society for Reproductive Investigation, Band 31, Heft 11, S. 3428-3439
ISSN: 1933-7205
In: Computers and electronics in agriculture: COMPAG online ; an international journal, Band 189, S. 106414
In: Computers and electronics in agriculture: COMPAG online ; an international journal, Band 230, S. 109870
ISSN: 1872-7107
In: Ecotoxicology and environmental safety: EES ; official journal of the International Society of Ecotoxicology and Environmental safety, Band 248, S. 114292
ISSN: 1090-2414
In: Environmental science and pollution research: ESPR, Band 28, Heft 34, S. 46303-46318
ISSN: 1614-7499
In: Ecotoxicology and environmental safety: EES ; official journal of the International Society of Ecotoxicology and Environmental safety, Band 209, S. 111844
ISSN: 1090-2414
In: Computers and Electronics in Agriculture, Band 74, Heft 1, S. 34-38
In: JEMA-D-23-20428
SSRN
In: COMPAG-D-22-00408
SSRN
In: Computers and Electronics in Agriculture, Band 185, S. 106174
In: Natural hazards and earth system sciences: NHESS, Band 24, Heft 12, S. 4237-4265
ISSN: 1684-9981
Abstract. Adaptation to an increasingly dry regional climate requires spatially explicit information about current and future risks. Existing drought risk studies often rely on expert-weighted composite indicators, while empirical evidence on impact-relevant factors is still scarce. The aim of this study is to investigate to what extent hazard and vulnerability indicators can explain observed agricultural drought impacts via data-driven methods. We focus on the German federal state of Brandenburg, 2013–2022, including several consecutive drought years. As impact indicators we use thermal–spectral anomalies (land surface temperature (LST) and the normalized difference vegetation index (NDVI)) on the field level, and empirical yield gaps from reported statistics on the county level. Empirical associations to the impact indicators on both spatial levels are compared. Extreme gradient boosting (XGBoost) models explain up to about 60 % of the variance in the yield gap data (best R2 = 0.62). Model performance is more stable for the drought years and when using all crops for training rather than individual crops. Meteorological drought in June and soil quality are selected as the strongest impact-relevant factors. Rye is empirically found to be less vulnerable to drought than wheat, even on poorer soils. LST / NDVI only weakly relates to our empirical yield gaps. We recommend comparing different impact indicators on multiple scales to proceed with the development of empirically grounded risk maps.
In: Computers and electronics in agriculture: COMPAG online ; an international journal, Band 203, S. 107478
In: Computers and electronics in agriculture: COMPAG online ; an international journal, Band 214, S. 108297
In: Ecotoxicology and environmental safety: EES ; official journal of the International Society of Ecotoxicology and Environmental safety, Band 204, S. 111136
ISSN: 1090-2414
In: Computers and Electronics in Agriculture, Band 93, S. 55-59
