scholarly journals Is Crop Growth model Able to Reproduce Drought Stress Caused by Rain-Out Shelters Above Winter Wheat?

Author(s):  
Markéta Wimmerová ◽  
Petr Hlavinka ◽  
Eva Pohanková ◽  
Kurt Christian Kersebaum ◽  
Miroslav Trnka ◽  
...  

This study evaluates drought stress effect on winter wheat. Simultaneously, the ability of the HERMES crop growth model to reproduce the process correctly was tested. The field experiment was conducted at Domanínek station in 2014 and 2015, where mobile rain-out shelters were installed on plots of winter wheat (May 2015). Precipitation was reduced in three replications and the findings were compared with results from control plots with ambient precipitation. A precipitation reduction of 93 mm led to reduced growth and decrease in grain yields. The results of this study showed that the model was able to reproduce soil moisture content well and reproduce the drought stress for crop yields of winter wheat to a certain extent. When rain-out shelters were used, real winter wheat yields were reduced by 1.7 t/ha. The model underestimated the yields for the sheltered variant by 0.67 t/ha on average against observed yields and overestimated development of leaf area for both unsheltered and sheltered variants. This overestimation was partly explained by the effect of excluded UV radiation. The outcome of this paper may help to reduce uncertainty within simulated yields of winter wheat under extreme weather conditions through a better understanding of model behavior.

1997 ◽  
Vol 102 (2-3) ◽  
pp. 301-314 ◽  
Author(s):  
Thomas Kätterer ◽  
Henrik Eckersten ◽  
Olof Andrén ◽  
Roger Pettersson

2021 ◽  
Author(s):  
Sabina Thaler ◽  
Josef Eitzinger ◽  
Gerhard Kubu

<p>Weather-related risks can affect crop growth and yield potentials directly (e.g. heat, frost, drought) and indirectly (e.g. through biotic factors such as pests). Due to climate change, severe shifts of cropping risks may occur, where farmers need to adapt effectively and in time to increase the resilience of existing cropping systems. For example, since the early 21st century, Europe has experienced a series of exceptionally dry and warmer than usual weather conditions (2003, 2012, 2013, 2015, 2018) which led to severe droughts with devastating impacts in agriculture on crop yields and pasture productivity.</p><p>Austria has experienced above-average warming in the period since 1880. While the global average surface temperature has increased by almost 1°C, the warming in Austria during this period was nearly 2°C. Higher temperatures, changing precipitation patterns and more severe and frequent extreme weather events will significantly affect weather-sensitive sectors, especially agriculture. Therefore, the development of sound adaptation and mitigation strategies towards a "climate-intelligent agriculture" is crucial to improve the resilience of agricultural systems to climate change and increased climate variability. Within the project AGROFORECAST a set of weather-related risk indicators and tailored recommendations for optimizing crop management options are developed and tested for various forecast or prediction lead times (short term management: 10 days - 6 months; long term strategic planning: climate scenarios) to better inform farmers of upcoming weather and climate challenges.</p><p>Here we present trends of various types of long-term weather-related impacts on Austrian crop production under past (1980-2020) and future periods (2035-2065). For that purpose, agro-climatic risk indicators and crop production indicators are determined in selected case study regions with the help of models. We use for the past period Austrian gridded weather data set (INCA) as well as different regionalized climate scenarios of the Austrian Climate Change Projections ÖKS15. The calculation of the agro-climatic indicators is carried out by the existing AGRICLIM model and the GIS-based ARIS software, which was developed for estimating the impact of adverse weather conditions on crops. The crop growth model AQUACROP is used for analysing soil-crop water balance parameters, crop yields and future crop water demand.</p><p>Depending on the climatic region, a more or less clear shift in the various agro-climatic indices can be expected towards 2050, e.g. the number of "heat-stress-days" for winter wheat increases significantly in eastern Austria. Furthermore, a decreasing trend in maize yield is simulated, whereas a mean increase in yield of spring barley and winter wheat can be expected under selected scenarios. Other agro-climatic risk indicators analysed include pest algorithms, risks from frost occurrence, overwintering conditions, climatic crop growing conditions, field workability and others, which can add additional impacts on crop yield variability, not considered by crop models.</p>


2013 ◽  
Vol 33 (6) ◽  
pp. 1762-1769 ◽  
Author(s):  
张建平 ZHANG Jianping ◽  
赵艳霞 ZHAO Yanxia ◽  
王春乙 WANG Chunyi ◽  
杨晓光 YANG Xiaoguang ◽  
王靖 WANG Jing

2012 ◽  
Vol 31 (5) ◽  
pp. 889-901 ◽  
Author(s):  
Cecilia M. Tojo Soler ◽  
Ayman Suleiman ◽  
Jakarat Anothai ◽  
Ian Flitcroft ◽  
Gerrit Hoogenboom

2020 ◽  
Vol 12 (18) ◽  
pp. 2896
Author(s):  
Wen Zhuo ◽  
Jianxi Huang ◽  
Xinran Gao ◽  
Hongyuan Ma ◽  
Hai Huang ◽  
...  

Predicting crop maturity dates is important for improving crop harvest planning and grain quality. The prediction of crop maturity dates by assimilating remote sensing information into crop growth model has not been fully explored. In this study, a data assimilation framework incorporating the leaf area index (LAI) product from Moderate Resolution Imaging Spectroradiometer (MODIS) into a World Food Studies (WOFOST) model was proposed to predict the maturity dates of winter wheat in Henan province, China. Minimization of normalized cost function was used to obtain the input parameters of the WOFOST model. The WOFOST model was run with the re-initialized parameter to forecast the maturity dates of winter wheat grid by grid, and THORPEX Interactive Grand Global Ensemble (TIGGE) was used as forecasting period weather input in the future 15 days (d) for the WOFOST model. The results demonstrated a promising regional maturity date prediction with determination coefficient (R2) of 0.94 and the root mean square error (RMSE) of 1.86 d. The outcomes also showed that the optimal forecasting starting time for Henan was 30 April, corresponding to a stage from anthesis to grain filling. Our study indicated great potential of using data assimilation approaches in winter wheat maturity date prediction.


2012 ◽  
Vol 151 (6) ◽  
pp. 813-835 ◽  
Author(s):  
J. EITZINGER ◽  
S. THALER ◽  
E. SCHMID ◽  
F. STRAUSS ◽  
R. FERRISE ◽  
...  

SUMMARYThe objective of the present study was to compare the performance of seven different, widely applied crop models in predicting heat and drought stress effects. The study was part of a recent suite of model inter-comparisons initiated at European level and constitutes a component that has been lacking in the analysis of sources of uncertainties in crop models used to study the impacts of climate change. There was a specific focus on the sensitivity of models for winter wheat and maize to extreme weather conditions (heat and drought) during the short but critical period of 2 weeks after the start of flowering. Two locations in Austria, representing different agro-climatic zones and soil conditions, were included in the simulations over 2 years, 2003 and 2004, exhibiting contrasting weather conditions. In addition, soil management was modified at both sites by following either ploughing or minimum tillage. Since no comprehensive field experimental data sets were available, a relative comparison of simulated grain yields and soil moisture contents under defined weather scenarios with modified temperatures and precipitation was performed for a 2-week period after flowering. The results may help to reduce the uncertainty of simulated crop yields to extreme weather conditions through better understanding of the models’ behaviour. Although the crop models considered (DSSAT, EPIC, WOFOST, AQUACROP, FASSET, HERMES and CROPSYST) mostly showed similar trends in simulated grain yields for the different weather scenarios, it was obvious that heat and drought stress caused by changes in temperature and/or precipitation for a short period of 2 weeks resulted in different grain yields simulated by different models. The present study also revealed that the models responded differently to changes in soil tillage practices, which affected soil water storage capacity.


Sign in / Sign up

Export Citation Format

Share Document