CL2.16

Apricot (Prunus armeniaca L.) is one of the most important export crops in Turkey and Turkey is the leader for both fresh and dried apricots production in the world. Apricot can be grown in all regions of Turkey with climate and vegetation diversities, except in the Eastern Black Sea Region and in the high plateaus of the East Anatolian Region. Malatya is the main producer province, which has good ecological and soil conditions in terms of apricot cultivation with the highest quality in Turkey. However, it is possible to talk about irregularities and decreases in apricot yield due to climate change in the region. Therefore, this study aims to observe climate change impacts on apricot yield in the main producer city, Malatya. Hereunder, climate projections were made at a 10 km horizontal resolution for the future period of 2021-2050 under the “worst-case” scenario (RCP8.5) using a regional climate model (RegCM4.4) for Malatya province considering 13 sub-regions. A statistical model, panel data method-multiple regression model, is designed to observe the effect of climate change and variability on the yield. Results indicate that adverse impacts of climate change on biological development of apricot lead to production irregularities and significant losses in yield in Malatya.

Acknowledgement: This research has been supported by Boğaziçi University Research Fund Grant Number 16763.

How to cite: Turp, M. T., An, N., Özertan, G., and Kurnaz, M. L.: Impact of Climate Change on Apricot Yield in Turkey in the Near Future, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5261, https://doi.org/10.5194/egusphere-egu21-5261, 2021.

Climate is a crucial factor for agricultural production and productivity. Foreseeing climate change in the future means predicting the possible effects on agriculture, and such studies observing year-dependent variability and predicting the effects of the near and mid-future climate change are valuable for both food security and economic value especially for countries which have commercial agricultural products like Turkey, as a Mediterranean Basin country with significant agricultural diversification. Apricot (Prunus armeniaca L.), which is one of Turkey's most important export products are expected to be affected significantly by climate change. Therefore, it is very important to see whether it will grow in the same regions in the future due to climatic changes for apricot. Hereunder, high resolution climate data, as an input for the membership function to be applied for classification of the climate suitability index, were obtained from RegCM4.4 under the RCP8.5 scenario for the period of 2021-2050 v.s. 1991-2018 for different phenological periods. Briefly, results indicate that adverse changes in climate suitability conditions for current apricot growing locations, 48 locations in the study, and the number of climate suitable locations for apricot will significantly decreases in the near and mid-future.  

Acknowledgement: This research has been supported by Bogazici University Research Fund Grant Number 17601.

How to cite: An, N., Turp, M. T., and Kurnaz, M. L.: Assessment of Climate Suitability for Prunus armeniaca L. in Turkey in a Changing Climate, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5382, https://doi.org/10.5194/egusphere-egu21-5382, 2021.

Rainfall timing is a key parameter that farmers rely on to match the cropping season with the time window over which seasonal precipitation provides adequate soil moisture to meet plant growth demand. The unpredictability of rainfall timing affects the selection of an optimal growing season, and hence crop production in regions where rainfed agriculture (RFA) is practiced. In this study, we (a) map rainfall timing, and its interannual variability and changes over RFA areas across Ethiopia for the period 1981-2010, and (b) explore the impact of variability in rainfall timing on cereal crop production in the period 1995-2010.

For the mapping of rainfall timing, we used the quasi-global CHIRPS precipitation dataset over Ethiopia. We use information entropy on monthly rainfall to define the rainfall seasonality metrics, i.e. the relative entropy and dimensionless seasonality index, and map them in space. For rainfall timing attributes, we determine the onset, cessation, and length of the wet season from LOESS-smoothed cumulative pentad rainfall anomalies for each hydrological year. Changes in seasonality metrics and rainfall timing attributes are analyzed using non-parametric methods. We show that high seasonality (unimodal rainfall regime) is located in the northern part of the Ethiopian RFA area where high annual rainfall and high relative entropy are coincident, and where the onset of the rainfall season varies between mid-April to late-June and cessation occurs between mid-September to late-October. Low seasonality in the southern part of the Ethiopian RFA area shows low relative entropy regardless of the annual rainfall total. We observed a considerable interannual variability both in seasonality and rainfall timing over the study period, especially in the onset and length of the wet season. The length of the wet season and magnitude of seasonal rainfall are predominantly controlled by the timing of rainfall onset.

For the impacts of rainfall timing on crop production, we used cereal crop production data from the Central Statistical Agency of Ethiopia for the period 1995-2010 in 45 administrative zones. We carried out a parametric correlation analysis between rainfall timing and rescaled and de-trended crop production anomalies. We observe that anomalies in seasonal cereal crop production in RFA areas are significantly correlated with anomalies in rainfall onset (negatively) and the length of the wet season (positively), with a regional average production loss of 3% per pentad of late rainfall onset, and 2.7% per pentad of shorter length of the wet season. Seasonal rainfall is less strongly correlated with cereal crop production anomalies compared to the rainfall onset. These results show that the interannual variability in rainfall timing (start of the rainy season) even under present climate has strong impacts on crop yields in RFA areas in Ethiopia, and this may be exacerbated in a future climate.

How to cite: Wakjira, M., Molnar, D., Peleg, N., Six, J., and Molnar, P.: Regularity of rainfall timing across Ethiopia: implications for crop production, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9279, https://doi.org/10.5194/egusphere-egu21-9279, 2021.

In the last decades, Europe has experienced an increase in the occurrence of extreme spring-to-summer heat and rainfall deficit. The compound impact of both hazardous weather events causes extreme drought conditions, which are likely to increase in frequency in the coming decades, with large impacts on agriculture and crop productivity. In this study, we analyze and attribute the effects of compound weather extremes on yield anomalies in Germany from 1989 to 2019.

To characterize the impact of compound events on irrigated and rainfed crops, statistical index-based approaches have widely been used, linking historic weather aggregates to yield records. To analyze and predict the impact of compound extreme events on crop yield, productivity and cultivation area at subnational level for Germany, we merged available yield data from multiple sources to create a consistent yield record of the last 30 years at county level. We then calculated indices on gridded meteorological data and records of phenological crop phases and agricultural practices, covering three decades, to analyze the effect of compound weather extremes on winter wheat yield.

We evaluated the SPEI (Standardized Precipitation Evaporation Index) for the 6-month period before winter wheat is harvested, to account for extremes in excess and lack of water availability. We further calculated the HMD (Heat Magnitude Day) index for the 3-month period before harvest, to assess the impact of heat stress conditions. Finally, a composite indicator the CSI (Combined Stress Index), based on a linear superposition of the standardized HMD and SPEI, is applied. The CSI is calibrated to local conditions by determination of coefficients that maximize the explanatory power of the index, using a bilinear ridge regression and county level yield observations.

The results of this study help to better understand the impacts of compound extremes on winter wheat in Germany and reveal regions that are especially threatened by yield losses from compound weather extremes.

How to cite: Ellsäßer, F., Xoplaki, E., Behr, L., Luterbacher, J., Toreti, A., and Zampieri, M.: Impact of compound weather extremes on winter wheat in Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11565, https://doi.org/10.5194/egusphere-egu21-11565, 2021.

The US agriculture system supplies more than one third of globally traded soybean, of which 90% is produced under rainfed agriculture. This makes the commodity particularly sensitive to weather and climate variability. Previous research has shown that annually averaged climate conditions explain about a third of global crop yield variability. Additionally, although less studied so far, crops are sensitive to specific short-term weather conditions, isolated or co-occurring at key moments throughout the growing season. Here we aim to identify key within-season weather and climate variables that can explain soybean yield variability in the US while exploring synergies between drivers that can have compounding impacts. The study combines weather data from reanalysis and satellite-based evapotranspiration and root-zone soil moisture with sub-national crop yield estimates using statistical methods that account for interaction effects. We also analyze the historic changes in identified key driving conditions in order to explore the effects of current climatic trends on yields. Our preliminary results indicate positive yield response to higher minimum temperature early and late in the season whereas the largest effect on soybeans is driven by the harmful co-occurrence of high temperature and low moisture levels during the summer flowering period significantly reducing yields on average in the US  by one standard deviation. The magnitude of the response to climate drivers varies across the spatial domain highlighting the need to focus on local and season specific management strategies. On the bright side, recent trends in temperature have not increased the likelihood of low yields. This is because the overall warming conditions reduce the risk of frost early and late in the season. Conversely, a peculiar cooling trend during the summer period attributed to agricultural land use is beneficial for yields when crops are most sensitive to high temperatures. Our study provides a detailed understanding of the current relationship between climate and soybean yields in the US. This is particularly relevant for adaptation and mitigation strategies aimed at avoiding low yields in a context of increasing food demand and climate change.

How to cite: Hamed, R., Van Loon, A., Aerts, J., and Coumou, D.: The impacts of hot-dry compound extremes on US Soybean yields, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12965, https://doi.org/10.5194/egusphere-egu21-12965, 2021.

Abstract

The National Map of Saline – Alkaline Soils of Greece was recently developed within the initiative of the European Soil Partnership (ESP) of FAO. The technique combines between other MODIS satellite imagery, spatial interpolation methods and ground surveying to derive at 1 km spatial resolution maps of soil’s salinity (SS) and soil organic carbon (SOC).

The present study investigates for the first time the development of higher resolution maps of these soil properties adopting the aforementioned methodology. Furthermore, this study attempted to estimate the Carbon sequestration (SOC) using Remote Sensing and geostatistic methods of spatial analysis, a concern that is eminent today due to its effect on climate change mitigation.

As a case study the island of Mytilene in Greece is used, for which detailed information on soil properties as well as climatic, geomorphological, geological and soil data was available from previous studies. An MCDA (Multiple Criteria Decision Analysis) method was applied in a GIS environment using Landsat satellite imagery for the composition of a Saline - Alkaline map. Between the key soil parameters estimated spatially included the Electrical Conductivity (EC), Exchangeable Sodium Percentage (ESP) and pH. Geospatial data analysis methods were implemented to visualize all the derived parameters related for the study area and to analyze the final products in the spatial domain.

Finding suggests that climate change and soil directly affect one another. The impact of environmental and climate change in addition to unsustainable agricultural practices seems to be linked to salinity increase, soil erosion and loss of organic matter.  In addition, when land degradation as well as erosion and loss of vegetation occur, SOC emissions increase. Under these conditions, soil cannot absorb enough amounts of CO2, especially when soil salinization and sodicity exists; inputs are further limited due to declines in vegetation health. The role of geoinformation technologies in support of sustainable agricultural production under the pressure of both climate change and anthropogenic activities is also discussed within the present study framework.  

KEYWORDS: geoinformation, soil, pH, salinity, soil organic carbon, geostatistics, earth observation, GIS, Greece

How to cite: Lekka, C., Petropoulos, G. P., Triantakonstantis, D., Detsikas, S., and Chalkias, C.: Geoinformation in support of sustainable soils’ management to strengthen resilience under the pressure of climate change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12973, https://doi.org/10.5194/egusphere-egu21-12973, 2021.

Agriculture is among the sectors that are most vulnerable to extreme weather conditions and climate change. In Germany, the subsequent dry and hot summers 2018, 2019, and 2020 have brought this into the focus of public attention. Agricultural actors like farmers, advisors or companies are concerned with such interannual variability and extremes. Yet, it often remains unclear what long-term adaptation options are most suitable in the context of climate change, mainly because climate projections have uncertainties and are usually not tailored to meet requirements, measures and scales of the individual practicioners. In the ADAPTER project, we explore regional and local change on the weather- and climate-related time scales and together with stakeholders (administration, plant breeders, educators, agricultural advisors), we co-design tailored climate change indices and usable products.

In this contribution, we provide a snapshot view of our stakeholders' requirements regarding information about climate change over the next decades. We then focus on the analysis of three groups of indices based on 85 regional climate model simulations from Coordinated Downscaling Experiments over Europe - EURO-CORDEX: (i) changes in daily temperature variability, (ii) occurrence of agricultural droughts in summer, (iii) compound events of combined dryness and elevated temperatures during the same events. We show that these user-oriented, newly constructed indices can capture relevant changes during important phenological development states of typical crops. Finally, we discuss first implications of our findings for different adaptation strategies in Mid-Europe, such as alternating crop rotations, irrigation strategies or plant breeding. The analysis products presented are interactively and publicly available through a product platform (www.adapter-projekt.de) for agricultural stakeholders.

How to cite: Bathiany, S., Rechid, D., Pfeifer, S., El Zohbi, J., Goergen, K., Wagner, N., Ney, P., and Belleflamme, A.: Simulation-based indices for a climate-resilient agriculture - insights from ADAPTER, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14408, https://doi.org/10.5194/egusphere-egu21-14408, 2021.

Intensification of studies of the agricultural water requirement is a main challenge in a globalized world, where food production is pushed to meet the needs of a growing population and the international trade network requires large-scale planning policies. Agriculture is the human activity that consumes most of the withdrawn freshwater and climate change can greatly influence the amount of irrigation required by crops. In recent years, the widespread availability of satellite images is providing an important contribution to water resources management, offering data at high spatio-temporal resolution over an interestingly long period of time.

This study deals with the temporal variability of global water requirement of the main crops, which is assessed through a comprehensive model, driven by climate forcings, that estimates the daily crop water requirement on a spatial resolution of 5 arc-min (or 0.0833°) from 1950 to 2020. The model computes a soil water balance using daily input data of precipitation and evapotranspiration, based on the high-resolution ERA5 reanalysis dataset from the Climate Change Service of the Copernicus Program, which combines satellite information and ground measurements. The distribution of harvested areas and the length of crop development phases are kept constant, to analyze the variability of crop water requirement strictly related to climate forcings, both in terms of precipitation (green water) and irrigation (blue water). The model considers the separation between irrigated and rainfed areas, in order to provide a consistent spatial distribution of irrigation requirements. Examining the spatio-temporal variability of the crop water requirement can support considerations on the effects of global warming in different areas in the world.

How to cite: Rolle, M., Tamea, S., and Claps, P.: Spatio-temporal variability of global crop water requirement, during 1950-2020, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15698, https://doi.org/10.5194/egusphere-egu21-15698, 2021.