CL3.2.5

As many studies shows, temperature is key element that affects grapevine growth. Global warming and temperature rise shifted grapevine phenology in many vineyard regions worldwide. Also, temperature and precipitation extremes can effect wine quality and yield. In Croatia, viticulture represents one of the most important branches of agriculture in the economical and traditional sense. Therefore, it is important to study changes in vine growth, as well as impact of meteorological parameters on it, so that it can be used to predict suitability and growth under future climate.

In this study, dates of beginning of 3 phenological phases (budburst, flowering and veraison), as well as harvest dates, collected from wineries across country, were analyzed. Results show earlier appearances of almost all phases, regardless of variety. With rise of temperature, the duration between two phases is shorted and that leads to an earlier harvest.

Also, 5 agrometeorlogical indices (Growing degree day, Winkler index, Huglin index, Cool night index and Dryness index) in two 30 year long period (1961-1990, 1991-2020) were calculated from meteorological data collected from 74 meteorological stations in Croatia. A spatial interpolation was applied on results and maps were made at 1-km resolution. Maps show significant changes in temperature indices between two periods. This raises the possibility of growing new varieties in certain regions, but also hints that grapevine could be cultivated in new regions.

In addition, temperature impact on the phenological phases is studied. Relationship between begging of the phases and mean daily temperate averaged over period that precede is calculated. Also, multi-linear regression between start dates of phenological phases and monthly averages of minimum, maximum and mean temperature is calculated, so that the best correlation could be determined. Results show a good correlation between dates and temperature, which can be valuable indicator for even more significant changes in vine growth in future.

How to cite: Omazić, B., Blašković, L., Telišman Prtenjak, M., Prša, I., and Karoglan, M.: Temperature impact on viticulture phenological stages in Croatia under present climate condition, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2183, https://doi.org/10.5194/egusphere-egu22-2183, 2022.

Adjusting the farming practices in West Africa (WA) to changing climate conditions is of utmost importance when considering that a quarter of the population in Sub-Saharan Africa lacks access to sufficient food (FAO et al., 2019). Climate change-induced effects such as delays in the start of the rainy season or intermittent dry spells at the beginning of the season also known as false start have devastating consequences on crop response and compromise food security in the region (Laux et al., 2008). Failing to plant at the right moment has led farmers to experience water stresses and yield reduction/failure at the end of the season.

To address the issue, several definitions have been proposed in order to identify a safe planting date. However, some of the assumptions on which the definitions were built are no longer valid under the current climate conditions leading to frequent cases of crop failure. In this study, we evaluated the most commonly used definition called the local onset, which is defined as one or two consecutive rainy days followed by 30 days without dry spells of 7 days or more (Marteau et al., 2009). We used a set of stations from the TAHMO network (https://tahmo.org/) across the Semi-Arid Zone of  WA to compute the local onset date. This onset date is then compared to the optimal sowing date derived from computed yields based on local rainfall patterns measured by the TAHMO stations, using the crop model AquaCrop (https://www.fao.org/aquacrop/en/).

The results indicate that the local onset generally leads to planting early in the season while that period is risky and characterized by intermittent dry spells. On the contrary, delaying the planting until later in the season reduces largely the risk of harvest failure and higher yield can be achieved. These outcomes highlight the necessity to update the coupling between rainfall onset detection and planting date, which will contribute to improving food security.

References:

• FAO, IFAD, UNICEF, WFP, WHO, 2019. The State of Food Security and Nutrition in the World 2019. Safeguarding against economic slowdowns and downturns. Food and Agriculture Organization of the United Nations, Rome, Italy.
• Laux, P., Kunstmann, H., Bardossy, A., 2008. Predicting the regional onset of the rainy season in West Africa. Int. J. Climatol. 28, 329-342. doi:10.1002/joc.1542.
• Marteau, R., Moron, V., Philippon, N., 2009. Spatial coherence of monsoon onset over western and central Sahel (1950-2000). J. Climate 22, 1313-1324. doi:10.1175/2008JCLI2383.1.

How to cite: Agoungbome, S. M. D., van de Giesen, N., and ten Veldhuis, M.-C.: Rainfall onset no longer a starting signal for planting crops?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2866, https://doi.org/10.5194/egusphere-egu22-2866, 2022.

Italy is a world leader for viticulture and wine business. According to the 2021 National Wine Market Forum, promoted by National Wine Union (Uiv), the wine business in Italy is expected to have an annual turnover of 11 billion euros in 2022, keeping Italy at second place in the world trade market ranking. 

Our study aims to understand the impact of climate change on wine production in Italy to provide useful information to winegrowers and stakeholders involved in the wine business to make their activities more sustainable and more resilient to climate change. The climate variables that most influence grape growth are: temperature, precipitation, and evapotranspiration. Starting from these variables, we calculate a range of bioclimatic indices, selected following the International Organisation of Vine and Wine Guidelines (OIV), to be correlated with grape yield data. 

Using observations from the E-OBS gridded dataset, we investigate how the bioclimatic indices changed in the last 39 years (1980-2019), and the impact of these changes on grape productivity aggregated at the regional (NUTS2) scale. The Italian Statistic Institute (ISTAT) provides yearly grape yield data for each region, which allows us to account for specific regional grape characteristics and wine production policies. 

Our results show low and not statistically significant correlations between individual bioclimatic indices and yields in most of the NUTS2 aggregations. Climate is not the only factor that influences wine productivity. In fact, vineyard management, policies, and markets can play a major role and those data are not included in the ISTAT dataset. The study highlights the need for higher quality data, including their metadata, and the active involvement of local businesses in this type of impact study.

How to cite: Massano, L., Fosser, G., and Gaetani, M.: Italian viticulture and climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3640, https://doi.org/10.5194/egusphere-egu22-3640, 2022.

As global warming is projected to intensify according to model simulations, a large range of resulting impacts and stressors is expected during the 21st century. Severe impacts are particularly projected in vulnerable regions such as West Africa, where local populations largely rely on livestock systems as their main food production and income source. As climate change threatens livestock systems in various ways, here we assess how regional livestock could be exposed to cumulated and cross-sectoral climate stressors during the upcoming decades. A set of eight major risk indicators that may affect livestock is assessed and illustrate changes in food availability, heat stress, flood and drought risks. Corresponding simulations are analysed from the largest multi-model climate-related impact simulations database ISIMIP.

Under the RCP8.5 scenario, we find that a large part of West Africa will experience at least 5 to 6 cumulated cross-sectoral climate stressors before the 2030s, including amplified severe heat stress conditions and flood risks. Consequently, about 30% of total west african livestock will be affected by these cumulated stressors, with highest exposures shown for sheeps and cattles (respectively 39% and 38% of their total regional density). Multi-model means show that these species will be first exposed to significant intensification of severe heat stress conditions from early 2020s, then to more flood risks from 2030s. This study brings new quantifications that could help policy makers to prioritize decisions to prepare local populations to face multiple climate-related impacts.

How to cite: Brouillet, A. and Sultan, B.: Livestock exposure to future cumulated climate-related stressors in West Africa, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4486, https://doi.org/10.5194/egusphere-egu22-4486, 2022.

The numerical crop growth model IVINE (Italian Vineyard Integrated Numerical model for Estimating physiological values) was developed at the dept. of Physics, Univ. of Torino to simulate grapevine phenological and physiological processes. The boundary conditions required by IVINE are hourly meteorological data related to: air temperature and relative humidity, atmospheric pressure, wind speed, downward radiation, soil temperature and volumetric water content and/or water moisture potential relating to the two soil layers considered (80 cm). Among those values, the more relevant are: air temperature and soil moisture. Other inputs required are initial conditions and parameters characterizing geography (longitude and latitude), soil texture, and grapevine variety, as well as information relating to cultivation interventions (main pruning, grape bunches thinning). Among the principal IVINE outputs, there are: the main philological stages (dormancy exit, bud-break, fruit set, veraison, and harvest), the leaf development, the yield, the berry sugar concentration, and the predawn leaf water potential. The IVINE requires to set some experimental parameters depending on the cultivar. IVINE model works including theoretical physically based equations for processes such as water balance and photosynthesis, and empirical equations for others. Seven main phenological phases can be identified: exit from dormancy period, bud-burst, flowering, fruit-set, beginning of ripening, veraison and harvest (the timing of this phase greatly depends on the variety of the grapevine, as well as on the choices of the winemaker). Exit from dormancy phase is evaluated using summed chilling units, while for bud burst summed growing degree hours are used, and for flowering and fruit set summed growing degree days (GDD), always starting from the previous phase. The phases of the beginning of ripening, veraison and harvest are determined by the model through a combination of GDD and critical thresholds on the sugar content of the berries, depending on the grapevine variety chosen. The leaf area index (LAI) is calculated from the bud-burst to the veraison in function of some parameters, the temperature, and the soil water content. The quantity of sugar in the berries (in °Brix), which is an excellent indicator of berry maturity and quality, depends on some parameters and uses double sigmoid with values depending on air temperature. The yield is regulated by photosynthesis, using some empirical parameters. Regarding the model sensitivity to the boundary conditions, the phenological phases are almost linearly anticipated by an increase of temperature, while the sugar content of the berries increases non-linearly with temperature, stabilizing around its maximum value; the LAI and the yield show non-linear increases with soil moisture. Long-term simulations carried out in Italian territory show, in the period 1980-2010, significant trends in almost all physio-phenological variables, as well as a reduction in interannual variability, correlated with the climate change still ongoing.

IVINE model performances depend on the quality of input data: the use of experimental data measured not far from the vineyard could improve the quality of the simulation, even if the model seems able to account for the interannual variability of the meteorological conditions, which reflects in the pheno-physiological trends interannual variability.

How to cite: Cassardo, C. and Andreoli, V.: Parametrization of vineyard's physiology and phenology with the crop model IVINE, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5962, https://doi.org/10.5194/egusphere-egu22-5962, 2022.

Field experiments based on the manipulation of the crops' environment are crucial to determine the response of crops to the expected climatic conditions in near future. The number of droughts in Central Europe is expected to increase and it is crucial to investigate how this will affect agriculture. An experimental site in Domanínek, Czech Republic, is located at 49°31'42 "N, 16°14'13 "E, at an altitude of 560m. The climatic conditions are described as cool and dry, the average annual precipitation was 609.3 mm and the mean annual temperature was 7.2 °C between 1981 and 2010. This area is characterised by low soil quality and the potential risk of late frosts; the soil type is classified as dystric cambisol. The field experiment of rain guards to reduce soil water availability was carried out from 2015 to 2020. The main objective of this study was to evaluate the impact of different water availability (rain-out shelter vs. control) on the performance of selected field crops (spring barley, winter wheat, winter oilseed rape, rye, and silage maize). In addition to a weather station on the experimental field measuring air temperature, relative humidity, global radiation, precipitation and wind, soil moisture was also monitored in the different rain-out shelter and control plots with TDR sensors (0-30 cm). In this way, the reduction in the amount of precipitation during the rain-out shelter treatment could be confirmed by measuring the soil water content. To answer the research question, various descriptive statistical parameters such as mean, percentiles, minimum and maximum were used. For instance, the average yield reduction over the 6 years for maize was 16%, while for rape it reached a value of up to 32%. In addition, an analysis of variance (ANOVA) was applied to the yields of the different crops.

Acknowledgement: This study was conducted with support of SustES - Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797).

How to cite: Thaler, S., Eitzinger, J., Hlavinka, P., Pohankova, E., and Trnka, M.: Effects on soil water content and productivity of selected crops in a field experiment with rain-out shelter vs. control plot in the Czech Republic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6248, https://doi.org/10.5194/egusphere-egu22-6248, 2022.

Between 1990-2018, annual rice consumption in Africa has quadrupled to around 40 Mt. This surge can be attributed to an interplay between rapid population growth and dietary shifts. Despite recent yield advances, rice production increases are lagging behind the growing demand, making the region increasingly import-dependent. Even after the 2008 food crisis, succeeding which many African policymakers presented rice as a flagship for food security by boosting domestic rice-production capacity, Africa’s reliance on the international market remains large. Currently, increases in production capacity are driven by acreage extensification rather than intensification, putting pressure on land and jeopardizing sustainable development. How the production of rice will further develop in Africa, and how sustainable this development will be in terms of food security and available land under changing socio-economic and climatic conditions is uncertain.                    
This research assesses future developments of the African rice system under different socio-economic and climatic scenarios by combining biophysical crop model projections (EPIC) with a spatial economic partial-equilibrium model (GLOBIOM) through the Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP) frameworks.

Our results suggest that by 2050, socioeconomic pressures will have a larger impact on future production levels than long-term climate changes on the African rice system. This is mainly explained by strong differences in population estimates between SSP scenarios and by a limited effect of climate change on yields as negative climatic effects including heat- and water stress are projected to be largely outbalanced by CO₂-fertilization effects for rice in Africa. Our simulations do suggest that the expected increase in climatic variability will result in increasing fluctuations in annual yields and production levels in comparison to historical variability. Regions dominated by rainfed systems are particularly vulnerable to such climate shocks, leading not only to variations in production but also in import and consumption levels. Our results also show that disruptions in production have effects beyond the climate-affected region due to bilateral trade. Particularly production shocks in Southeast Asia could have a strong impact on rice availability in Africa because of the vast import dependency of Africa. The magnitude of the effect of a climate-induced production shock in Southeast Asia on rice consumption levels in some African regions is even comparable to the effect of a similar climate shock on domestic production. Since the robustness of the analysis is strongly linked to the performance of the biophysical crop model, we also present a comparison of these preliminary results to other existing biophysical global gridded crop models (GGCMs).          

In summary, we demonstrate that future socio-economic pathways have a more important impact on the African rice system than climate changes in the long term, but that increases in the short-term climate variability strongly affect production and consumption. While trade may partially offset a negative effect of a climate-induced production shock on rice consumption in Africa through increased imports, trade and Africa’s import dependence make the continent as vulnerable to climate-induced production shocks in Southeast Asia as in Africa itself.

How to cite: De Vos, K., Janssens, C., Jacobs, L., Campforts, B., Boere, E., Havlík, P., Maertens, M., and Govers, G.: Future scenarios of the African Rice System: Climatic and Socio-economics Pathways, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7469, https://doi.org/10.5194/egusphere-egu22-7469, 2022.

Chestnut tree cultivation is largely spread worldwide, with approximately 596 × 103 ha devoted to fruit production, raising global production to approximately 2.5 million t, with an upward trend of 56 × 103 t per year. In the European Union, Portugal is the largest chestnut producer (38,870 ha). In  ecent years, the country has shown an increasing trend of 723 ha per year, and the production was 35,830 t in 2019, but largely concentrated in the northeast. In the present study, bioclimatic indices are applied to analyse the spatial distribution of chestnut trees in mainland Portugal, namely degree days (suitability interval: 1900–2400ºD), annual mean temperature (8–15ºC), monthly mean maximum temperature <32ºC, and annual precipitation (600–1600 mm). These indices are assessed for both historical (1989–2005, from IBERIA01) and future (from EURO-CORDEX) climates, within three sub periods: 2021–2041, 2041–2060, and 2061–2080, and under two anthropogenic radiative forcing pathways (RCP4.5 and RCP8.5). For the historical period, in terms of degree days, the suitability for chestnut tree cultivation (i.e., percentage of years fulfilling the predefined interval) is 10% in southern Portugal, whereas much higher values are found at high elevations in the north (50–90%). For the annual mean temperature, most of northern Portugal shows almost 100% suitability. Concerning the maximum temperature, the suitability reduces from the west (100–90%) to the east (40%). Regarding the annual precipitation, the suitability is heterogeneous throughout the territory, with areas under 50%. A compound index is also defined, revealing suitability from 100 to 75% over northern Portugal, while central and southern Portugal show values in the approximate range of 25–50%. For future climates, a progressive and significant reduction in suitability was found, particularly for RCP8.5 and in the long-term period. Therefore, climatic changes embody an important threat to chestnut tree cultivation in Portugal, potentially affecting the plant physiology and phenology, ultimately leading to a reduction of the cultivation areas and yield. Adaptation strategies are critical to mitigate climate change detrimental impacts. It is indeed essential to implement measures that promote chestnut orchards’ adaptive capacity, reducing vulnerability and risks of exposure to increasingly warm and dry climates, but also warranting the sustainability of the sector.

Acknowledgments: The work is financed by the CoaClimateRisk project (COA/CAC/0030/2019) financed by the Portuguese Foundation for Science and Technology (FCT).

How to cite: Freitas, T. R., Santos, J. A., Silva, A. P., and Fraga, H.: Impact of climate change on bioclimatic zoning of chestnut trees in Portugal, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7581, https://doi.org/10.5194/egusphere-egu22-7581, 2022.

Cherry (Prunus avium L.) is one of the most important export crops in Turkey and Turkey has a globally significant share in cherry production with 26%. Although cherry is mostly a temperate climate fruit, different types can be grown in the regions with climate and vegetation diversities. However, it is possible to talk about irregularities and decreases in yield due to climate variability in those regions. Provinces, which are Turkey's main cherry producers, are affected by average and extreme temperature changes from climate change, and the need for alternative areas for cherry production is gradually increasing. For this reason, it is very important to see whether the cherry with high commercial value will grow in the same regions in the future due to climatic changes or new alternative areas will emerge for this fruit. Therefore, this study aims to observe climate impacts on cherry growing regions in the main producer provinces. Hereunder, in the study, climate data with 10 km resolution was obtained using a regional climate model, i.e., RegCM4.4, under the RCP8.5 pessimistic scenario for the future period of 2021-2050 with respect to the period of 1991-2018 for different phenological periods and the climate suitability index was calculated. Although regional differences are observed in the model result, it indicates that biological development of cherry in Turkey may be affected by the increase in average temperature and extreme temperature changes due to climate change.

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

How to cite: An, N., Turp, M. T., Demiralay, Z., and Kurnaz, M. L.: Assessment of Climate Suitability for Cherry (Prunus avium L.) in Turkey in a Changing Climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10151, https://doi.org/10.5194/egusphere-egu22-10151, 2022.

The main objective of LOCOMOTION research is to increase the robustness, transparency, accessibility, usability and reliability of the MEDEAS set of the Integrated Assessment Models (IAMs), developing a new set of nested models. This structure allows for flexibly testing, improving and expanding each module without impairing the robustness of the models. One of the purposes is also to expand the geographical coverage and detail by creating a new multi-regional world model with 9 global regions and integrating the 27 EU countries.

The new approach includes an Environmental module covering the Land and Water modules and also a climate module. This module has been split into several submodules which are structured as: Diets, Water, Land management, Land Products Availability, Climate, Land Use and Land-Use Change and Forestry (LULUCF) and agriculture emissions. The six submodules related to Land (Forests, Wood Production, Croplands, Yields, Grasslands and Land Uses) calculate the availability of land products from forests and agricultural lands. In the land module, also agriculture and LULUCF emissions are endogenously calculated.

The water module is divided in two parts: demand and availability. The demand includes the use of water by economic sectors and households on the base of the water intensity. The water availability is defined at the country scale and depends on the volume of freshwater net of the share that must be kept ensuring basic environmental services and functions.

The climate module in WILIAM is divided in different parts. The first includes the links with the other modules: energy, industry, agriculture and LULUCF emissions, where the greenhouse gas (GHG) emissions not calculated endogenously are consistent with the RCP scenarios (Representative Concentration Pathways). Other important element of this module is Climate, which includes modelling of GHG cycles, climate variables and climate change impacts. The first computes the cycle of each GHG separately and the interactions between cycles, including carbon, methane and nitrous oxide.

The modelling of climate variables intends to calculate the total radiative forcing of each gas and their contribution to the global temperature change. The main outputs of the climate module are the total radiative forcing, the mean global temperature change (Capellán-Pérez et al., 2017), the sea level rise and the ocean acidification. Climate change impacts modelling includes the regionalization of climate variables at each climate zone to model impacts considering the heterogeneity of climate in different areas of the Earth, the modelling of impacts on forests (on Net Primary Productivity), on water availability, and in crop yields.

Preliminary results indicate, for example, that land for forest and irrigated crops will decrease in the all climate zones and future scenarios, for 2050. In climate module, temperature change will be larger in polar than in tropical climates.

References: Capellán-Pérez, I., De Blas, I., Nieto, J., Castro, C., Miguel, L.J., Mediavilla, M., Carpintero, Ó., Rodrigo, P., Frechoso, F., Cáceres, S., 2017. EU Framework Program for Research and Innovation actions (H2020 LCE-21-2015) Guiding European Policy toward a low-carbon economy . Modelling sustainable Energy system Development under Environmental And Socioeconomic constraints. Medeas-Ue 1, 1–254.

How to cite: Calheiros, T., Lourenço, T., Mediavilla, M., Alonso, N., Distefano, T., and Ramos-Diez, I.: Environmental Module of the Integrated Assessment Model WILIAM, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10310, https://doi.org/10.5194/egusphere-egu22-10310, 2022.

Coupling of leaf physiological models with leaf and canopy RT (Radiative Transfer) models allow evaluation and quantification of most influential variables using fluorescence signal emitted by the leaf and canopy. Using RTM output, one of the most successful method i.e. Sobol global sensitivity analysis was used to identify the most influential input variables through matrices such as first-order (S_i) and total-Order effect (S_Ti). The present study was conducted using the field-based and hyperspectral datasets in the agricultural site in Northern India. Ground bio-physical (Leaf water content, Leaf area Index) and bio-chemical (Chlorophyll) parameters were collected. RTM spectral outputs were generated for hyperspectral data within the spectral range of 350-2500 nm. To calculate the first order and total order sensitivity result of PROSPECT-4 (leaf) and PROSPECT+SAIL (canopy) RTMs were evaluated. Sobol results for PROSPECT-4 model reveal that the role of biochemical parameter chlorophyll content in the visible region and the influence of the other biophysical parameters such as Leaf structure and dry matter across the whole spectral range. Only Leaf water content reflectance was found around 1200 nm onwards. After coupling the leaf PROSPECT-4 model with SAIL (Scattering by Arbitrary Inclined Leaves) model, reference PROSAIL S_Ti results showed that the LAI variable shows 50% of the total variability, especially in the SWIR region. The present study is not only useful to know wavelength-dependent influential and non-influential RTM input variables but also for driving input variables of fluxes such as photosynthesis of the canopy and for the estimation of FPAR (Fraction of photosynthetically active radiation) values.  

How to cite: Singh, P., Srivastava, P. K., and Mall, R. K.: Sensitivity analysis of Radiative Transfer model towards leaf biophysical and biochemical parameter retrieval, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-69, https://doi.org/10.5194/egusphere-egu22-69, 2022.