Can Agrometeorological Indices of Adverse Weather Conditions Help to Improve Yield Prediction by Crop Models?

Atmosphere

Branislava Lalić⁴  Josef Eitzinger³  Sabina Thaler³  Višnjica Vuპtić²  Pavol Nejedlik⁵  Henrik Eckersten¹  Goran Jaćimović⁴  Emilija Nikolić-Djorić⁴ 

[1] Department of Crop Production Ecology, Swedish University of Agricultural Sciences, Almas Allé 10, Uppsala 750 07, Sweden; E-Mail:;Meteorological and Hydrological Service, Gric 3, Zagreb 10000, Croatia; E-Mail:;Institute of Meteorology, University of Natural Resources and Life Sciences, Gregor Mendel Straße 33, Vienna 1180, Austria; E-Mails:;Department of Field and Vegetable Crops, Faculty of Agriculture, University of Novi Sad, Sq. Dositej Obradovic 8, Novi Sad 21000, Serbia; E-Mails:;Slovak Hydrometeorological Institute, Jeseniova 17, Bratislava 83315, Slovak Republic; E-Mail:

关键词: adverse weather conditions;    crop models;    climate-yield correlations;    yield prediction;    yield simulations;   

DOI  :  10.3390/atmos5041020

来源: mdpi

【 摘 要 】

The impact of adverse weather conditions (AWCs) on crop production is random in both time and space and depends on factors such as severity, previous agrometeorological conditions, and plant vulnerability at a specific crop development stage. Any exclusion or improper treatment of any of these factors can cause crop models to produce significant under- or overestimates of yield. The analysis presented in this paper focuses on a range of agrometeorological indices (AMI) related to AWCs that might affect real yield as well as simulated yield. For this purpose, the analysis addressed four indicators of extreme temperatures and three indicators of dry conditions during the growth period of maize and winter wheat in Austria, Croatia, Serbia, Slovakia, and Sweden. It is shown that increases in the number and intensity of AWCs cannot be unambiguously associated with increased deviations in simulated yields. The identified correlations indicate an increase in modeling uncertainty. This finding represents important information for the crop modeling community. Additionally, it opens a window of opportunity for a statistical (“event scenario”) approach based on correlations between agrometeorological indices of AWCs and crop yield data series. This approach can provide scenarios for certain locations, crop types, and AWC patterns and, therefore, improve yield forecasting in the presence of AWCs.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.