NH1.7

Recent adverse weather events in Europe have questioned the stability of crop production systems. We assessed the vulnerability of eleven major crops in France between 1959 and 2018 as a function of climate, crafting a novel hazard framework that combines exposure and sensitivity to weather-related hazards. Exposure was defined as the frequency of hazardous climate conditions, while sensitivity of crops was estimated by the yield response to single and compound hazards. We used reported yields available at departement (county) level. Vulnerability was computed as the exposure-weighted average of crop sensitivities. Our results do not reveal any historical evidence for an increased vulnerability of French crop production. Rather, the sensitivity to adverse weather events, and thus the overall vulnerability, has significantly decreased for six of the eleven crops between 1959 and 2018, and shown no significant decline or remained stable for the other five. Yet compound hazards can induce yield losses of 30% or more for several crops. Moreover, as heat-related hazards are projected to become more frequent with climate change, crop vulnerability may rise again in the future. Our results may support insurance design by identifying single and compound hazards that can severely affect yields.

How to cite: Schauberger, B., Makowski, D., Ben-Ari, T., Boé, J., and Ciais, P.: The decreasing vulnerability of French crop production to climatic hazards, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1498, https://doi.org/10.5194/egusphere-egu22-1498, 2022.

Objective

Diverse cropping systems are associated with multiple ecosystem services and are suggested to alleviate the effects of drought and heat stress. The magnitude and frequency of extreme weather events are projected to increase in the future due to climate change, and diverse cropping systems might therefore become key for food security. However, there is still limited information on the spatiotemporal variation of crop species diversity and how it relates to differences in climate or soil type. We aimed at quantifying how crop diversity developed over time at the national and regional scale in Sweden between 1965 and 2019, and how crop species diversity is related to climatic factors and soil texture.

Methods

Sweden is an interesting case study due to the large range in latitude from north to south. The analyses were conducted using national databases containing historical records of crop production and climate data, as well information on soil texture. To quantify crop species diversity, species richness and a crop diversity index that reflects the area-weighted equivalent number of crops were used.

Results

Crop species richness and crop diversity index increased from north to south in Sweden, and our results showed a positive relationship between mean annual temperature and latitude to crop diversity at national level. The positive relationship shows how mean annual temperature and length of the vegetation period control crop diversity across the country. There were no significant relationships between crop diversity and mean annual precipitation and soil texture, respectively. Crop species richness did not change over time at national level while crop diversity index experienced a temporal decrease. At the county level, different temporal trends were observed among counties: in some counties an increase in crop diversity and species richness occurred, while other counties had no change or a temporal decrease.

Conclusions

The differences in the results between national and county level show the importance to include different scales in the examination of temporal developments of crop diversity. Although crop diversity index decreased over time at national level, the temporal increase observed in almost half of the counties suggest that it is possible to increase crop diversity in Sweden. The different temporal changes between counties imply that crop diversity is affected by an interplay between natural and socioeconomic factors. Natural factors constrain which crops can be grown, but to promote diversification of agricultural crops in the future, socioeconomic factors need to be considered.

How to cite: Sjulgård, H., Colombi, T., and Keller, T.: Spatiotemporal patterns of crop diversity reveal potential for diversification in Swedish agriculture, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2330, https://doi.org/10.5194/egusphere-egu22-2330, 2022.

Permanent grasslands are a very relevant cropping system in the North of Spain and support the main dairy farms in the country. Adaptation to climate change will be required given the projected changes of regional precipitation. To support such adaptation, modelling of these systems to generate high quality projections of the system performance is required. In the region to be simulated, grasslands are managed with a mixture of cuts and grazing. Several issues hinder the modelling of this type of systems: 1) the available data of grazing intensity presents large uncertainties; 2) there are few grassland models that allows flexibility to define a variable combination of cuts and grazing; 3) soil heterogeneity. It follows, and expands to grazing, the exercise performed by Gómara et al. (2020), who used the Pasture Simulation model (PaSim) to simulate a mown permanent grassland in the French Massif Central.
The model was calibrated using data from Villaviciosa (Asturias, Spain, 5º 26' 27" W, 43° 28' 50" N, 10 m a.s.l.), located at northern Spain with a temperate climate. This calibration was used to simulate several grassland locations distributed along the Cantabrian Sea. The soil information was obtained from Trueba et al. (2000). The model was configured for the optimum management for mowing and nitrogen fertilization. The 1976-2005 period and the 2030-2059 period were selected. For the future period two representative concentration pathway emission scenarios (RCP, van Vuuren et al., 2011) were selected (i.e. RCP4.5 and RCP8.5). An ensemble of climate models will be used from the Coordinated Regional Climate Downscaling Experiment (CORDEX, Giorgi and Gutowski, 2015) bias-adjusted by using the European observational database EOBS (Haylock et al., 2008) with the empirical quantile mapping method included in the climate4R R package (Iturbide et al., 2019). 
Modelling was challenging due to a combination of complexity (many processes involved) and uncertainty (observed data are difficult to generate). The results of the simulation exercise allow for assessing PaSim skill to reproduce the performance of these complex systems, as well as to determine the main weaknesses of the model and the observational/experimental required to improve the modelling work.

References
Giorgi, F. and Gutowski, W.J., 2015. Annual Review of Environment and Resources, 40(1): 467-490.
Gómara I, Bellocchi G, Martin R, Rodríguez-Fonseca B, Ruiz-Ramos M, 2020. Agricultural and Forest Meteorology, 280, 107768.
Haylock, M.R., Hofstra, N., Klein Tank, A.M.G., Klok, E.J., Jones, P. and New, M., 2008.  J. Geophys. Res., 113: D20119.
Iturbide, M., Bedia, J., Herrera, S., Baño-Medina, J., Fernández, J., Frías, M.D., Manzanas, R., San-Martín, D., Cimadevilla, E., Cofiño, A.S. and Gutiérrez, J.M., 2019. Environ. Modell. Softw., 111: 42-54.
Trueba, C., Millán, R., Schimd, T, Lago, (2000). CIEMAT. ISBN: 84-7834-370-9. Madrid.
van Vuuren, D.P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G.C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S.J. and Rose, S.K., 2011. Clim. Change, 109: 5–31.

How to cite: Rodríguez, A., Gómara, I., Bellocchi, G., Martin, R., Martínez-Fernández, A., Carballal, A., Doltra, J., del Prado, A., and Ruiz-Ramos, M.: The challenges of modelling mixed management grasslands in North Spain under climate change, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12103, https://doi.org/10.5194/egusphere-egu22-12103, 2022.

Rangelands ecosystems contain more than a third of the global land surface, sustaining key ecosystem services and livelihoods. Unfortunately, they suffer from severe degradation on a global scale. Normalized Differenced Vegetation Index (NDVI) has been used to monitor low ground cover vegetation, especially relevant for arid and semiarid regions.

MODIS data are commonly used to calculate NDVI to monitor rangelands. In this study, we used time series metrics and Hurst Exponent from multifractal detrended fluctuation analysis to cluster different rangeland types to monitor and classify temporally and spatially diverse rangelands.

The Northwest (Noroeste) agricultural region in the province of Murcia was selected from the Southeast of Spain. We selected approximately 20.000 pixels to cover different areas that include land uses that are utilized for grazing. The selection aimed to collect pixels where other land uses were kept to a minimum, given the great spatial variability in Spain. We collected the time series using satellite data of MODIS (MOD09Q1.006) from 2000 to 2020. The pixels have a spatial resolution of 250 x 250 m² and a temporal resolution of 8 days. This selected area represents a mix of cereal croplands, tree croplands, grasslands, scrublands, and forested areas; all of them with an arid climate.

We used unsupervised random forest and compared the produced clusters with the classification from the Spanish parcels classification systems to test our model. Our goal is to study the ability of unsupervised clustering using NDVI time series and their multifractal character to categorize and monitor their vegetation status, key information for farmers and managers to adapt to a changing situation due to climate change. This information can be used in other arid areas with similar geophysical conditions.

Acknowledgments: The authors acknowledge the support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades of Spain, “Garantía Juvenil” scholarship from Comunidad de Madrid, and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020EU, DT-SPACE-01-EO-2018-2020.

References:

Sanz, E.; Saa-Requejo, A.; Díaz-Ambrona, C.H.; Ruiz-Ramos, M.; Rodríguez, A.; Iglesias, E.; Esteve, P.; Soriano, B.; Tarquis, A.M. (2021). Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study. Entropy, 23, 576.

Sanz, E.; Saa-Requejo, A.; Díaz-Ambrona, C.H.; Ruiz-Ramos, M.; Rodríguez, A.; Iglesias, E.; Esteve, P.; Soriano, B.; Tarquis, A.M. (2021). Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands. Remote Sens., 13(5), 840.

Kantelhardt, J.W., Zschiegner, S.A., Koscielny-Bunde, E., Havlin, S., Bunde, A., & Stanley, H. E. (2002). Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Statistical Mechanics and its Applications, 316(1-4), 87-114.

Almeida-Ñauñay, A.F., Benito, R.M., Quemada, M., Losada, J. C., & Tarquis, A. M.  (2021). The Vegetation–Climate System Complexity through Recurrence Analysis. Entropy, 23(5), 559.

Almeida-Ñauñay, A.F., Benito, R. M., Quemada, M., Losada, J. C., & Tarquis, A. M. (2022). Recurrence plots for quantifying the vegetation indices dynamics in a semi-arid grassland. Geoderma, 406, 115488.

How to cite: Sanz Sancho, E., Almeida-Ñauñay, A., Díaz-Ambrona, C. G., Saa-Requejo, A., Ruiz-Ramos, M., Rodríguez, A., and Tarquis, A. M.: Clustering arid rangeland pixels using NDVI series and fractal analysis to classify land uses. Case in Southeastern Spain., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-287, https://doi.org/10.5194/egusphere-egu22-287, 2022.

Mediterranean agriculture faces drought as one of the most challenging obstacles to overcome. Especially, in semiarid grasslands, where every year the biomass production suffers severe damage due to several factors, being one of them the lack of precipitation. For this reason, semiarid vegetation monitoring allows us to improve the management and conservation of these essential ecosystems. Meteorological drought is commonly monitored using indices such as the Standard Precipitation Index (SPI) and the Standard Precipitation Evapotranspiration Index (SPEI). On the other hand, agricultural drought is measured by the Vegetation Health Index (VHI). In this work, we present different methodologies to optimize the correlation between both droughts by standardizing the vegetation index and selecting the best time scale throughout the year.

First, we selected drought-vulnerable Mediterranean grasslands zones in the centre of Spain. By doing this, we pretend to evaluate the performance and the sensibility of the drought indices. MODIS data (MOD09Q1) was used to calculate the Normalized Difference Vegetation Index (NDVI), then it is standardised to define a standardized vegetation index (SVI). The meteorological indices SPI and SPEI were calculated using data collected from nearby weather stations. Overall, our results revealed that SPEI was better correlated with SVI and obtained better results in the critical seasons, in comparison to SPI. The quarterly scale was the most suitable, showing a higher relationship than the monthly scale. This fact suggest that vegetation growth phases should be considered in agricultural drought detection. The most sensitive time frame throughout the year was spring and autumn, implying that drought indices (SPI and SPEI) along with vegetation index (SVI) could offer an improvement in the monitoring during these periods.

References

Escribano Rodríguez, J.A., Díaz-Ambrona, C. H. y Tarquis Alfonso, A.M. (2014). Selección de índices de vegetación para la estimación de la producción herbácea en dehesas. Pastos, 44(2), 6-18.

Martín-Sotoca, J. J., Saa-Requejo, A., Moratiel, R., Dalezios, N., Faraslis, I., and Tarquis, A. M. (2019). Statistical analysis for satellite-index-based insurance to define damaged pasture thresholds, Nat. Hazards Earth Syst. Sci., 19, 1685–1702, https://doi.org/10.5194/nhess-19-1685-2019

Sanz, Ernesto Antonio Saa-Requejo, Carlos H. Díaz-Ambrona, Margarita Ruiz-Ramos, Alfredo Rodríguez, Eva Iglesias, Paloma Esteve, Bárbara Soriano and Ana M. Tarquis (2021). Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands. Remote Sens., 13(5), 840.

Andrés F. Almeida-Ñauñay, Rosa María Benito, Miguel Quemada, Juan Carlos Losada and Ana M. Tarquis (2021). The Vegetation–Climate System Complexity through Recurrence Analysis. Entropy, 23(5), 559.

Sanz, E.; Saa-Requejo, A.; Díaz-Ambrona, C.H.; Ruiz-Ramos, M.; Rodríguez, A.; Iglesias, E.; Esteve, P.; Soriano, B.; Tarquis, A.M. (2021). Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study. Entropy, 23, 576.

Almeida-Ñauñay, A. F., Benito, R. M., Quemada, M., Losada, J. C., & Tarquis, A. M. (2022). Recurrence plots for quantifying the vegetation indices dynamics in a semi-arid grassland. Geoderma, 406, 115488. https://doi.org/10.1016/j.geoderma.2021.115488

How to cite: Almeida Ñauñay, A. F., Sanz, E., Villeta, M., Quemada, M., and Tarquis, A. M.: Meteorological and agricultural drought indices in semiarid grasslands monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-360, https://doi.org/10.5194/egusphere-egu22-360, 2022.

Rainfed agriculture in Kenya is approximately 98% and highly susceptible to climate variability. Humid climatic regions of Kenya are key to sustainability of agricultural sector. This study focused on influence of coffee on real evapotranspiration in Nyeri and Embu Counties of humid Mount Kenya Region. This is because the economy of Kenya relies mostly on Coffee as the fourth largest export earner. Quality controlled 9-year long dataset was sought from Nyeri and Embu synoptic stations. Site specific soil parameters and coffee coefficient were used in computations of estimates. Penman-Monteith standard equation was used to estimate daily values of reference evapotranspiration. Average daily, monthly ET₀ and annual total estimates were computed. The ET₀ estimates were modelled using 1D Palmer-type soil model to estimate real evapotranspiration using soil parameters for the station at 1 m arable depth. Results showed a very slight variation among the average annual estimates of ET₀ between the two humid regions. For instance, in Nyeri the average annual estimate ET₀ was 1488±52 mm/year while in Embu it was 1488±48 mm/year. Average annual ET depicted slightly higher variation with estimates of 813±216 mm/year in Nyeri and 830±166 mm/year in Embu. Average monthly estimates of ET₀ and ET were almost the same with estimates of 124±21 mm/month and 68±30 mm/month in Nyeri and 124±23 mm/month and 71±37 mm/month in Embu respectively. Results also indicated that daily average,  ET₀ , ET and ET estimates with application of Kc  varied insignificantly with  4.1±1 mm/day, 2.2±1 mm/day and 2.2±1 mm/day in Nyeri respectively while the estimates were nearly the same in Embu. Coffee coefficient (Kc) had slight influence on real evapotranspiration in humid climatic regions under study. This is because the Kc values were almost 1 with a range of between 0.9 to 0.95. In addition, the study area receives adequate precipitation hence no soil water stress. Further, the slight differences among the ET with and without application of Kc were due to the linear function of available soil moisture used in the computation of ET from reference evapotranspiration (ET₀). The study is important in investigating the role of 1D Palmer type soil model on ET₀ and coffee coefficient influence on real evapotranspiration in Kenya in these regimes of climate extremes.

How to cite: Musyimi, P. K., Székely, B., and Weidinger, T.: Reference evapotranspiration estimation and influence of coffee on real evapotranspiration in humid climatic regions of Kenya, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-388, https://doi.org/10.5194/egusphere-egu22-388, 2022.

Managing weather related risks in cropping systems includes strategies to share the risk such as insurance schemes. Advances in satellite sensor technology and interpolated regional weather data enable insights in the relationship between extreme weather events and yields losses which in turn offers possibilities for area-yield insurance schemes.

Extreme weather events  during sensitive phenological stages of the growing season differed significantly (p < 0.05) between low and high crop yields (Gobin, 2012; Gobin, 2018). Spatial return levels confirmed the exceptionality of 2016 and 2018 as extreme wet and dry years with high return periods and yield impacts (Gobin and Van de Vyver, 2021). We investigated the importance of weather data, satellite sensor-derived vegetation indices and water balance simulations in estimating crop yields of winter wheat, potato and sugar beet for the period 2016-2018. The water balance simulations were performed with the Aquacrop model, while the yield simulations were realised with the machine learning technique random forest regression. Results for winter wheat showed that NDVI series did not respond to crop yield affecting weather conditions (Vannoppen and Gobin, 2021). Weather and/or soil water depletion during sensitive phenological stages in combination with the NDVI integral during the growing season explained up to 57 of late potato, 66% of winter wheat, 68% of early potato and 84% of sugar beet yield variability.

Machine learning techniques proved valuable in estimating crop yields thereby elucidating the importance of weather conditions during sensitive crop stages. The crop yield models developed make use of commonly available remote sensing indicators and weather data, and are commensurate with regional scale decision making.

Gobin, A., 2012. Impact of heat and drought stress on arable crop production in Belgium. Natural Hazards and Earth System Sciences 12: 1911–1922. https://doi.org/10.5194/nhess-12-1911-2012

Gobin, A., 2018. Weather related risks in Belgian arable agriculture. Agricultural Systems 159: 225-236. https://doi.org/10.1016/j.agsy.2017.06.009

Gobin, A., Van de Vyver, H., 2021. Spatio-temporal variability of dry and wet spells and their influence on crop yields. Agricultural And Forest Meteorology, 308-309, Art.No. 108565. https://doi.org/10.1016/j.agrformet.2021.108565

Vannoppen, A., Gobin, A., 2021. Estimating Farm Wheat Yields from NDVI and Meteorological Data. Agronomy-Basel, 11 (5), Art.No. 946. https://doi.org/10.3390/agronomy11050946

How to cite: Gobin, A. and Vannoppen, A.: Modelling the impacts of extreme weather events on crop yields using water balance and satellite sensor data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10664, https://doi.org/10.5194/egusphere-egu22-10664, 2022.

Agricultural catchments are prone to high crop yield variability because of extreme weather events, and their impact destabilises agricultural income at different territorial scales. This study aims to use optimisation algorithms coupled with the Soil Water and Assessment Tool – SWAT to allocate specific agri-environmental measures to mitigate the impact of climate change in water fluxes availability at subbasin scale.

The SWAT tool as a semi-distributed model uses the hydrological response unit – HRU concept to split catchments into several territorial management units with similar soil properties, slopes, and land use. The HRUs were used as optimisation unit to change land use and crop management. The work was performed in a catchment located in the southern part of Cuenca city in Ecuador; the area delineates the Tarqui river. The primary land use of the area is grassland-livestock systems and seasonal cropping. Two steps were performed: first, a model run with calibration and validation was set as baseline model. A second step include an optimisation set of modeled scenarios derived from future stakeholder alternatives defined in a previous study as sustainable practices in the area.

Several SWAT model alternatives were optimised, changing crop sequences, fertilisation rates, and crop scheduling dates. As a result, stakeholders' perception majorly matches with scenarios results in optimising water availability during low flow periods increasing streamflow and soil water availability. However, several unexpected alternatives, coming from optimisation, hint at farmers and ranchers. These new options explore other uses and crop sequences that increase income and reduce fertilisation costs.

Acknowledgements

The authors acknowledge support from European Union NextGenerationEU and RD 289/2021 and the support of Project No. PGC2018-093854-B-I00 of the Ministerio de Ciencia, Innovación y Universidades of Spain

References

• David Rivas-Tabares, Ana M. Tarquis, Ángel de Miguel, Anne Gobin, Bárbara Willaarts. Enhancing LULC scenarios impact assessment in hydrological dynamics using participatory mapping protocols in semiarid regions. Sci. Total Environ., 803, 149906, 2022. https://doi.org/10.1016/j.scitotenv.2021.149906
• Rivas-Tabares, A. de Miguel, B. Willarts and A.M. Tarquis. Self-organising map of soil properties in the context of hydrological modeling. Applied Mathematical Modelling, 88,175-189, 2020. https://doi.org/10.1016/j.apm.2020.06.044

How to cite: Rivas-Tabares, D., Tarquis Alfonso, A. M., and Célleri, R.: Optimising agri-environmental measures at catchment scale through specific allocation with the SWAT model – A case study in southern Andes of Ecuador, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10790, https://doi.org/10.5194/egusphere-egu22-10790, 2022.

Water scarcity is increasingly recurring in irrigated agriculture in Mediterranean climate regions, and it is, therefore, necessary to establish alternatives to enable irrigators to deal with such problems, in a planned manner and in accordance with the technical and economic implications.  Although insurance schemes for droughts has long been standardized for rainfed crops, their application to irrigated crops is still under discussion. This study presents a new index-based drought insurance scheme, totally aligned with the river basin drought management procedures.

When considering a highly regulated water system, where natural water availability is altered by the operation of water infrastructure, traditional drought indicators (e.g., SPI, SPEI, SRI) lose significance, and ad-hoc index formulations tailored to the basin characteristics are required to reflect both regulation effects and natural fluctuations in the basin. Spain provides a paradigmatic example of a practical and systematic policy for the identification and mitigation of operational droughts: the river basin authorities are bound by law to design basin-specific state indexes. The state indexes are monthly monitored and used to trigger water demand and supply measures when entering a drought period, according to the specifications of the drought management plan. The study was carried out in an irrigation district (90% citrus fruits) in the Jucar river basin in Spain, a highly regulated water system.

Three insurance scheme options were evaluated: 1, a variable premium and/or variable franchise based on the forecast of water availability for the insured irrigation campaign, 2, a multi-annual insurance contract, and 3, an advance contract with a constant premium. In each of them, the values of the fair risk premiums, the maximum compensation, and the deductible franchise were established for different state indexes based on different combinations of system state variables (such as reservoir storages and inflows) and precipitation. The design of the insurances was done under the preexisting drought system operating rules to reduce the issue of the moral hazard, which is one of the main problems for this kind of insurance index. The selection of the insurance scheme is based on the gross margin of citrus crops with and without insurance contracts, including the value of additional premium loads, in addition to a basis risk analysis.

To evaluate the performance of the insurance, synthetic hydrological time series were generated using an ARMA model and implemented in the basin-wide water resource simulation management developed in DSS Shell AQUATOOL. The premium-claim ratio was used to assess the performance of the insurance company, finding stable values that can generate a balance of the long-term insurance scheme.

How to cite: Valenzuela Mahecha, M. A., Pulido-Velazquez, M., and Macian-Sorribes, H.: Hydrological drought index insurance in irrigated agriculture in a highly regulated system: an economic instrument for risk mitigation for the Jucar River Basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11989, https://doi.org/10.5194/egusphere-egu22-11989, 2022.

Agriculture is facing the challenge of providing food for a growing world population in a context of climate change. The Mediterranean region is characterized for a semi-arid climate. Thus, water scarcity is coupled with the development of intensive crops that require irrigation, such as olive orchards. Recently, biochar –the solid aromatic carbonaceous product of the pyrolysis of residual biomasses– has been proposed as an amendment for reducing soil water loss [1] and increasing plant productivity [2].  The main objective of this study was to compare the effects of the application of biochar and green-compost (the organic amendment traditionally used) on soil properties and crop productivity at a super-intensive plantation of arbequina olive trees under deficit irrigation located at “La Hampa” field station (Coria del Río, Seville, Spain). Thus, soils were amended with 40 t ha^-1 of olive-waste biochar, green-compost or a biochar-compost mixture (50 % w/w). Un-amended plots were used as control. On a monthly basis, soil pH, water holding capacity, humidity and penetrability resistance, as well as TC and TN contents of soils were determined. Finally, the total weight of produced-olives per tree was measured.

Results showed that biochar application was the most effective amendment in increasing soil water holding capacity and moisture. All the organic amendments reduced the soil penetrability resistance. Olive production increased about 15 % at the biochar amended plots. Thus, the application of organic amendments, especially biochar, improved soil physical properties and led to a higher crop production.

Acknowledgements: The BBVA foundation is gratefully acknowledged for funding the scholarship Leonardo to “Investigadores y Creadores Culturales 2020”, what made this project possible.

References:

[1] Campos et al., 2021. Agronomy 11, 1394. https://doi.org/10.3390/agronomy11071394

[2]De la Rosa et al., 2014. Science of the Total Environment 499, 175-184. http://dx.doi.org/10.1016/j.scitotenv.2014.08.025

How to cite: Campos, P., Sánchez-Martín, Á., Santa-Olalla, A., Miller, A. Z., and de la Rosa, J. M.: Effects of biochar addition into intensive-olive orchard soils under deficit irrigation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1887, https://doi.org/10.5194/egusphere-egu22-1887, 2022.