Irrigated agriculture is a central socio-economic sector in many countries, particularly in the Mediterranean area, but often associated with relevant environmental issues, such as the high demand for natural resources (water, soil, energy). Irrigated agriculture is also increasingly threatened by multiple stresses, which include the rising demand for food, the lack of resources (as a consequence e.g., of climate change) and the conflicting needs and uses of those resources.

The recent scientific literature highlighted the need to support understanding and operationalizing the concept of resilience for irrigated agroecosystems, i.e. the capability of such systems to absorb stresses and adapt to changing conditions. The present work, developed within the ERASMUS project (within the PRIN 2022 call, funded by the European Union, Next Generation EU), mainly focuses on the role of water resources management in irrigated areas, yet considering a ‘Nexus’ approach that highlights the interconnections and interdependencies among resources. The aim is to identify management practices and technological measures that may support irrigated agriculture in the face of a multiplicity of environmental and anthropogenic stresses, ultimately suggesting sustainable development pathways for areas under stress. Particular attention is given to the rational use of water resources and to the role that can be played by the introduction of cutting-edge technologies and network modernization processes to increase the resilience, the long-term sustainability and the performance (in terms of distribution equality and efficiency) of pressurized irrigation systems.

Two main modelling approaches are the backbone of the ERASMUS approach. On the one hand, System Dynamics Modelling tools are used to describe the complexity of irrigated agroecosystems, the interdependencies among sectors (water, energy, land, food, climate) and to characterize their resilience. The main objective is to effectively describe (using also innovative sets of indicators) the system state and potential evolution as an effect of the different modernization strategies of networks along with different models/strategies for better managing water resources. Second, numerical modelling approaches are used to test the potential of innovative devices (mainly smart valves) and management criteria to improve the performance of irrigation networks, ultimately increasing the resilience of the system as a whole. Specific attention will be given to new technological solutions that may guarantee multiple joint benefits, ranging from a reduction of resource consumption (water, energy), while providing an increasing control and management of networks. Such an ambitious objective is being put into practice in two pilot sites located in Southern Italy (i.e., two irrigation consortia located in Puglia and Campania) where two Communities of Innovation are being developed and will support modelling activities throughout the project duration.

How to cite: Pagano, A., Ferrarese, G., Fontana, N., Portoghese, I., Fratino, U., Coletta, V. R., Lamaddalena, N., Mambretti, S., and Malavasi, S.: Analyzing the resilience of complex irrigation systems: the ERASMUS approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16726, https://doi.org/10.5194/egusphere-egu24-16726, 2024.

Due to climate change, managing irrigation systems requires adapting existing scheduling strategies based on monitoring meteorological, biophysical, and soil physical variables. For monitoring, there are many combinations of sensors, starting from low-cost IoT-based systems and ranging to scientific high-precision devices that offer a specific quality of measurements at a particular price. By explicitly modeling the value gained by more precise monitoring, the value of information (VOI) theory can determine whether additional information provided by site-specific monitoring setups is worth employing to manage the considered irrigation systems. Different levels of information about meteorological conditions are provided by (i) on-site systems (energy balance station, low-cost climate station, and a spatial grid of low-cost LoRaWAN temperature and humidity sensor), (ii) available public weather data, e.g., from a close climate station of the German weather service (DWD), and (iii) latest reanalysis data from the ERA5-Land product. To estimate the additional VOI of the different site-specific monitoring setups related to the reference defined by the DWD data, evapotranspiration, biomass, and yield data simulated by the Aquacrop model are compared. In addition, adapted scheduling strategies are derived using the Deficit Irrigation Toolbox (DIT).   This contribution presents the application of VOI theory for decision-making in the monitoring design of an irrigated apple farm in Werder (Germany) in 2023 and 2024.

How to cite: Schuetze, N., Kuhnert, L., and Lennartz, F.: Assessing the value of information: a comparative analysis of meteorological observation setups in an irrigated German apple orchard, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11134, https://doi.org/10.5194/egusphere-egu24-11134, 2024.

In the Mediterranean area, agriculture is subject to numerous demands caused by the interplay of climate change, population growth, changing food production patterns and the increasing need for nature conservation measures, enforcing an efficient usage of resources and the creation of resilient production systems. To ensure a more sustainable water use, water policies have been adopted in the European Union as well as in Northern Africa countries, such as Morocco and Egypt as irrigation is the largest water user in the Mediterranean region. Small farmers make up to two thirds of the agricultural areas and are therefore an important part of areas agricultural community. Estimates see up 35% possible water savings could be achieved by more efficient irrigation systems. New technologies and practices are currently adopted mostly by large farms. The challenge is therefore to increase the usage of efficient irrigation techniques by small farmers. We present a concept of data handling in a data chain, from the collection in the field towards calculated irrigation recommendations that are provided via mobile application. The idea behind it is to provide an irrigation management tool that aims to overcome barriers in adapting new technologies for smallholders. It is designed to provide irrigation recommendations for orange and olive orchards based on a bottom-up approach. The derived irrigation recommendations are dependent on the available input data based on sensor systems: the FAO-56 approach based on climate data, or a soil water balance model relying on soil moisture data. As the calculation of irrigation recommendations is based on the collected climate and soil moisture data, we are focusing on the possibilities of automated data quality control and the methods and obstacles of the data handling when providing the recommendations. The final product is derived in form of an application for mobile devices that is intuitive and easy to use. The data handling is hereby done using the python programming language and RESTful application programming interfaces, and the transfers are executed periodically using dockerized applications. The main advantage of the proposed workflow is the possibility to integrate data from a variety of sensors and platforms and the access for smallholders can be done via mobile phones. This way, the currently measured data on the agricultural fields and up-to-date irrigation needs are easily accessible. The system is currently under validation. We present the whole framework, starting at measured values by sensors and ending in the irrigation recommendation for the farmers available in the App.

How to cite: Thomas, F., Pérez Pérez, J. G., Bonet Pérez de León, L., Martínez-Gimeno, A., Vanella, D., Consoli, S., Ramírez Cuesta, J. M., Elomari, H., Farag, A., and Werban, U.: From sensors to decisions: Data flows to enhance irrigation efficiency for smallholder orchards, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1578, https://doi.org/10.5194/egusphere-egu24-1578, 2024.

Soil texture influences many other soil attributes, including its physical, chemical, and biological characteristics. Soil texture dictates vital factors such as aeration, nutrient, water availability, and heat retention. These aspects collectively impact various aspects of plant life, encompassing growth, development, productivity, and quality. Agricultural soils are commonly classified into several categories based on their texture to facilitate effective agricultural practices like tillage, irrigation, fertilization, and pesticide applications.

A growing call has recently been made for integrating machine learning (ML) techniques to enhance comprehension and insight into soil behaviour. However, it is essential to note that real-world datasets often exhibit inherent imbalances. In such cases, ML models tend to overemphasize the majority classes while simultaneously underestimating the minority ones. This study aimed to investigate the effects of imbalance in training data on the performance of a random forest model (RF).

The original data used in this work was from La Chimenea farm station near Aranjuez (Madrid, Spain). The variables included were Electrical conductivity (EC), EC shape, EC depth, EC ratio, slope, curve, and NDVI derived from Sentinel-2. Clay and sand percentages were obtained with the exact spatial resolution, and the USDA classification was applied based on them. A descriptive statistics analysis was conducted to analyze the data. Then, Pearson's coefficient (r) of linear correlation was calculated to verify possible relations between the different variables. Then, a synthetic resampling approach using the Synthetic Minority Oversampling TEchnique (SMOTE) was employed to make a balanced dataset from the original data.

The imbalance and balance data classification were compared to see SMOTE's benefits in better-classifying soil texture.

Keywords: digital soil mapping; machine learning; soil texture; imbalance classification; data resampling

 Acknowledgements

This work has received support from projects PID2021-124041OB-C22 and PID2021-122711NB-C21, funded by the Ministerio de Ciencia e Innovación (Ministry of Science and Innovation).

How to cite: Ksantini, F., Tarquis, A. M., Almeida-Ñauñay, A., Sanz, E., and Quemada, M.: Establishing management zones for irrigation using soil properties and Remote Sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4157, https://doi.org/10.5194/egusphere-egu24-4157, 2024.

The water systems management plays a pivotal role in environmental conservation and disaster mitigation. As climate change intensifies, the ecological interactions of our ecosystems are modified, decreasing biodiversity and increasing extreme events. Therefore, accurate hydrological modelling tools are crucial for predicting rainfall-runoff processes. Hydrological processes, in general, are complex due to the interaction between multiple variables and spatial and time scales. Therefore, the development of hydrological models has evolved from simple models with few parameters to complex models aiming to model all notable processes within the study area. However, some researchers affirm that increasing the number of free parameters does not necessarily improve the model performance, and retaining only necessary data can ensure that the model’s components are positively represented. In this work, we show a set of geographical information system-based methodologies to set a limited optimal number of parameters to improve the hydrological modelisation.

To achieve our purpose, we collected terrain information, land use and soil properties data to model the water balance based on historical precipitation and gauging data. The same model was replicated in 47 small watersheds north of the Iberian Peninsula to ensure reliability. The rainfall and water flow data were downloaded from the automatic hydrology information system of the Ebro Water Confederation (SAIHEbro). We obtained a 15-minute rainfall and water flow time series, and each of them started at different years, continuing to current times up to a length of 27 years (more than 35,000 records per year).

As a result, we developed a database including the watershed limits, the most extended stream segment, rainfall and flow for each catchment. Furthermore, elevation, land use, soil classes, bulk density, clay, sand, and silt content (Hengl et al., 2017) at different depths were obtained. All data were transformed to a raster format to homogenise, and then their spatial resolution was harmonised to 2m for all spatial layers. The main shortcomings were found in matching the different spatial scales available in all the studied datasets. The lack of data or gaps in 2m DEM needed to be filled. Therefore, a nearest neighbour interpolation method combined with patching technique was performed by SAGA software and using 5m DEM as an input. Furthermore, differences in land use characterisation among regional and national datasets arose in some of the study catchments.

By processing these datasets, we obtained essential parameters for hydrological modelling. Altogether, the gathered information was useful to simulate the evolution of the water-related processes, paying particular attention to the relationships between precipitation, soil water content and land use.

Acknowledgements: The authors acknowledge the support of the Project “Fusión de modelos de base física y basados en datos para la modelización de fenómenos precipitación-flujo HYDER”, from Universidad Politécnica de Madrid (project number: TED2021-131520B-C21).

References

Hengl, T., De Jesus, J.M., Heuvelink, G.B.M., Gonzalez, M.R., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M.N., Geng, X., Bauer-Marschallinger, B., Guevara, M.A., Vargas, R., MacMillan, R.A., Batjes, N.H., Leenaars, J.G.B., Ribeiro, E., Wheeler, I., Mantel, S., Kempen, B., 2017. SoilGrids250m: Global gridded soil information based on machine learning, PLoS ONE. https://doi.org/10.1371/journal.pone.0169748

How to cite: Almeida-Ñauñay, A. F., Sanz, E., Tarquis, A. M., Martín-Sotoca, J. J., and Zubelzu, S.: Hydrological parameters modelling in catchments based on a geographical database., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8407, https://doi.org/10.5194/egusphere-egu24-8407, 2024.

The amount of open data offered by different numerical weather prediction (NWP) systems is growing due to the increase in the capacity of computing systems. This rise has enabled the development of improved and user-tailored forecasting services and products. However, one key variable in agricultural systems not usually provided by the forecasting services is the reference crop evapotranspiration (ETo), which requires ad-hoc computation and proper identification of the factors that condition it.

This work develops a spatially-distributed ETo forecast in the Jucar river basin (Eastern Spain), to support crop management in agricultural plots. ETo was determined from forecasted meteorological variables using the Penman-Monteith methodology described in FAO56. Specific ETo value maps at the AP scale were generated considering the spatial variation of the meteorological parameters that drive ETo: daily average, maximum, minimum and dewpoint temperatures, net solar radiation and wind speed at 2 meters. Calculations were downscaled using an interpolation technique based on linear regression from daily weather predictions of temperatures and wind. The procedure was tested using forecasts from the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP) belonging to the U.S. National Oceanic and Atmospheric Administration (NOAA), for the year 2022. Raw GFS forecasts were post-processed against the ERA5 reanalysis data, available through the Copernicus Climate Change Service (CS3), with a spatial resolution of 0.25^o; and against observed data from the meteorological stations of the Agroclimatic Information System for Irrigation (SIAR) of Spain. In both cases, post-processing was done using artificial intelligence (AI), in particular Fuzzy Logic. Inputs for interpolation were the geographical characteristics at each GFS location within the Jucar river basin: longitude, latitude, distance to the Mediterranean Sea, mean solar radiation, mean solar radiation at a distance of 2.5, 5 and 25km from each GFS location, elevation, elevation at a distance of 2.5, 5 and 10km from each GFS location, slope, and orientation with respect to the north. Solar radiation is obtained using the Area Solar Radiation module of ArcGIS.

Once the forecasts and solar radiation maps were generated, the difference between the interpolated and the predicted values was calculated. This difference generated a cloud of points which, which together with a Digital Elevation Model, allowed for surface interpolation (SI) using the Splines with the Tension methodology integrated in Grass (QGIS). These SI are subtracted from the forecast’s maps obtained by interpolation, already having corrected forecasts with which the ETo is determined using the Penman-Monteith methodology described in the FAO56. The difference between the interpolated ETo and the predicted ETo is also calculated by subtracting this SI from the obtained ETo, generating a corrected ETo. Furthermore, post-processed forecasts and ETo was compared with 41 meteorological stations and evaluated using the Mean Absolute Error (MAE).

Acknowledgements:

This study has received funding from the European Union’s Horizon Europe research and innovation programme under the SOS-WATER project (GA no. 101059264); and from the subvencions del Programa per a la promoció de la investigación científica, el desenvolupament tecnològic i la innovació a la Comunitat Valenciana (PROMETEO) under the WATER4CAST project.

How to cite: Carricondo-Anton, J. M., Garcia-Prats, A., Macian-Sorribes, H., Avila-Velasquez, D. I., Jimenez-Bello, M. A., Lopez-Perez, E., Manzano-Juarez, J., and Pulido-Velazquez, M.: Spatial determination of ETo supported by weather forecasts and artificial intelligence., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7992, https://doi.org/10.5194/egusphere-egu24-7992, 2024.

The agricultural sector is the biggest and least efficient water user, accounting for around 80% of total water use in Northern Africa, which is already strongly impacted by climate change with prolonged drought periods, imposing limitation to irrigation water availability. The objective of this study was to develop a procedure for the monitoring of anthropogenic irrigation water use for the irrigation districts of Doukkala in Morocco, from 2013 to 2022.

The system is based on the energy-water balance model FEST-EWB, which is an agro-hydrologic pixel wise model that computes continuously in time the main processes of the hydrological cycle where evapotranspiration and soil moisture behaviour in agricultural soil layer are modelled solving the energy and water mass balance model (EWB).

Firstly, the model has been calibrated and validated over non-irrigated areas, against satellite land surface temperature from LANDSAT and downscaled Sentinel3 data at 30m of spatial resolution, and evapotranspiration from MOD16, GLEAM and FAOWapor. The model has been run using as input the past observed meteorological forcings (ECMWF ERA5-Land) and vegetation data. From the pixel-by-pixel comparison between modelled and observed LST, a mean absolute difference of 3.5 °C is obtained over the period 2017-2022 for the whole Doukkala area.

The second step refers to the historical estimates of the actual irrigation volumes through the calibrated model implementing three different irrigation strategy, at hourly scale and at 30m of spatial resolution: the FAO approach based on soil moisture (SM) and crop stress thresholds (Allen et al., 1998), the separate and joint assimilation of satellite land surface temperature (downscaled Sentinel3 data) and of satellite soil moisture (1km SMAP-Sentinel1) to update the modeled fluxes and estimates irrigation volume. Overall, the results suggested that the yearly total irrigation volumes modeled with the FAO approach are quite in agreement with the observed water allocations; and similar outcomes are obtained when the joint assimilation of satellite LST and SM is implemented which allows to overcome the problems related to the number of available satellite images, which could lead to missing irrigation events.

How to cite: Corbari, C., Paciolla, N., Dos Santos Araujo, D. C., Labbassi, K., Sheffield, J., Berendsen, S., Al Bitar, A., and Szantoi, Z.: Monitoring Anthropogenic Irrigation Water Use by assimilating satellite land surface temperature and soil moisture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17455, https://doi.org/10.5194/egusphere-egu24-17455, 2024.

Irrigation is the most significant human water withdrawal globally, playing a pivotal role in ensuring food security. However, the lack of detail irrigation datasets across spatial and temporal dimensions limits our comprehensive understanding of how historical irrigation water supply has responded to demand fluctuations and, consequently, its effect on agricultural yields. In this study, we employed a combination of remote sensing products, meteorological data, and various statistical datasets to estimate gridded monthly irrigation water demand and supply in China at a spatial resolution of 0.1° during the period 2000-2019. The results indicate that the national annual irrigation water demand is 122.23 km³, with rice accounting for the highest share (39.25%), followed by wheat (36%) and maize (24.75%). While the annual irrigation water supply is measured at 317.42 km³, with rice (62%) dominating, trailed by maize (21.13%), and wheat (16.87%) contributing the least. The mismatch in the distribution of irrigation water supply and demand among crops underscores variations in irrigation systems and the availability of water sources for irrigation. Notably, in the downstream of the Yellow River Basin and the Huaihe River Basin, the irrigation water supply falls short of demand when not accounting for irrigation efficiency, primarily attributed to a scarcity of water during the wheat growing season in spring (Mar. to May), indicating a potential water stress on wheat yield in this region. This study enhances our understanding of the intricate relationship between irrigation water supply and demand in China, offering valuable insights to support regional water resources management and allocation strategies.

How to cite: Hou, C.: Response of irrigation water supply to demand in China and its effects on yields, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13904, https://doi.org/10.5194/egusphere-egu24-13904, 2024.

The viticultural sector is one of the agricultural sectors most challenged by Climate change(CC), needing specific adaptation and mitigation actions to make local farming communities and production resilient. In this context, it is important to guarantee not only the achievement of production but also, above all, the achievement of a cultivar-specific grape quality able to support the oenological goal and, thus, the expression of terroir.

In viticulture, the plant's water stress is therefore important, representing, unlike other crops, a necessary condition for achieving the quality and typicality of the wine. This is because the vine water status represents the main regulator of the hormonal balance of grapevines, affecting berries' characteristics such as sugar, anthocyanins, flavonoid concentration, and acidity.

For this reason, under climate change, the introduction of irrigation represents a complex issue. In fact, it is not only important to guarantee water to the plants, but to maintain a specific water stress during the ripening phase of the grapes.

From this perspective, the aim of this contribution is to show the first results of a task of Spoke 3 of the National Research Center for "Agriculture Technologies - Agritech" (NextGenerationEU European program) on the identification of procedures for the optimized management of the water resource in vineyards.

The research adopts multidisciplinary approaches and methods to support irrigation optimization in the vineyard. It has been based on two main steps: (i) the identification of the functional homogeneous zones (fHZs) present in the vineyard through an environmental analysis based on the determination of the soil spatial variability, the micro-morphology of the vineyard (LIDAR) and the spatial variability of the crop response at different resolutions (UAV); (ii) use and test of field sensors to monitor plant and soil water status in the fHZs in order to define the optimal timing and volume of irrigation to achieve the desired field oenological goals while preserving the water resource.

The experiment has been realized in an Aglianico vineyard (2 ha) of Tenuta Donna Elvira winery (Montemiletto – AV), where climate, plant, and soil are monitored through the use of commercial and non-commercial sensors. In particular, two weather stations and seven monitoring nodes (soil TDR probes at three soil depths) have been distributed within the irrigated and non-irrigated long plots. The plants were monitored continuously (hourly time step) by means of a new in vivo sensor developed by IMEM CNR institute, Bioristor, (applied to 16 plants to monitor the plant status) and discontinuously (weekly or two-weekly time step) plant measurements (e.g., UAV multispectral measurements, LWP, yield production, grapes quality,..etc..).

The irrigation supply was realized through an automated irrigation system (MySOLEM) and defined according to the leaf water potential (LWP) measured in the field, maintaining its value between 1.2 and 1.4 bar during the ripening period.

At the end of the first year, the analysis of collected data to develop a vineyard water management model able to support achieving oenological goals and facing climate change has been realized.

How to cite: Vitale, A., Janni, M., Buonanno, M., Erbaggio, A., Albrizio, R., Giorio, P., De Micco, V., Cirillo, C., Petracca, F., Giaccone, M., Vurro, F., Palermo, N., Bettelli, M., and Bonfante, A.: How can we support irrigation management in viticulture to preserve grape quality in southern Italy?: the case study of Aglianico grapevine., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18792, https://doi.org/10.5194/egusphere-egu24-18792, 2024.

The evapotranspiration of vegetation (ET) is a key component of the hydrological balance. Various tools and models have been proposed to estimate evapotranspiration in fruit crops. Among them, the most widely used approach is that proposed by the Food and Agriculture Organization (FAO), which considers climatic variables included in reference evapotranspiration (ETo), as well as the type of crop and its characteristics represented by a single crop coefficient (Kc). However, there is evidence that in tall and discontinuous canopies, such as citrus orchards, with a high degree of interaction with the environment, Kc can change depending on local environmental conditions and the amount of vegetation.

Other methods, such as measurements of stem water potential, sap flow sensors, and moisture probes, allow for determining the water status of the crop, but only for a limited number of trees, and uncertainties arise when extrapolating values. Remote sensing fills this gap if spatial and temporal resolutions suit the monitored crop. A successful approach in water management is using models that calculate latent heat as a residue of the surface energy balance (SEB).

This study applied an energy balance to calculate ET in an irrigation district. The study site is located in the Valencia region (Spain; 39º22'43'' N, 0º28'20'' W) with localized irrigation, where most crops are citrus. A total of 182 images from the Landsat satellite constellation for the period 2013-2018 were used to estimate instantaneous ET by extrapolating daily actual ET (ETSEBAL) values using climatic data.

These climatic data correspond to predictions the Global Forecast System (GFS) provides. This way, climatic predictions are used for scheduling instead of the classical methodology that uses past data to estimate evapotranspiration. The study's objective is to analyze the results using a dynamic Kc obtained from the actual state of the crops and climatic predictions for each plot, compared to a generic Kc obtained for standard conditions and past climatic data.

The results suggest that, for the studied plots, the relationship between drained water and the actual volume provided by irrigators would be reduced by 20% to -30 %. A point agrohydrological model calibrated with capacitive moisture probes was used to monitor soil water balance.

In the same way, the methodology allows for determining the stress level of crops and maintaining it within recommended limits.

How to cite: Jiménez Bello, M. A., Carricondo-Antón, J. M., García-Prats, A., López-Pérez, E., Manzano-Juarez, J., Pulido-Velazquez, M., and Martínez-Alzamora, F.: Analysis of the use of actual evapotranspiration calculated with Landsat imagery and climate forecasts, assessed with an agrohydrologic model for irrigation scheduling in fruit crops, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11989, https://doi.org/10.5194/egusphere-egu24-11989, 2024.

Spain counts roughly 941.000 hectares of vineyards, of which 41% are grown under irrigation systems. Water status is a relevant parameter in grapevines as it affects yield, fruit composition, and wine quality. Water stress reduces photosynthetic activity and vegetative growth and limits berry ripening. Mapping the crop's water status is essential for adjusting irrigation doses based on the specific water demands of different agroclimatic zones [2]. Thus, maps can be generated based on water status level ranges. Remote sensing through thermal and multispectral sensors onboard Unmanned Aerial Systems (UASs) can provide such maps with sufficient detail and rapidity. This tool allows obtaining high-resolution images that aid in assessing crop heterogeneity [3].

In a commercial vineyard located in the central region of Spain, we developed models to obtain values of stem water potential (SWP) based on canopy temperature estimated from high-resolution aerial images of a thermal sensor (Tc) [1] and multivariable linear regression models based on combinations of multispectral bands [4].

These models were developed using measurements and data from two previous irrigation seasons (2021 and 2022) on experimental vines in different plots with different management practices, irrigation, and climatic conditions. The modelled values of SWP were validated with measurements in the same vines for the 2023 season.

The application of the two developed models allows for spatial and temporal analysis of the water status of vines, aiding in the on-field characterization of water stress. This dynamic spatial mapping improves irrigation management through climatological information and high-resolution sensors.

ACKNOWLEDGEMENTS

The authors thank Bodegas y Viñas Casa del Valle for allowing us to work in their vineyards and the company UTW for supplying the drone images. Comunidad de Madrid provided financial support through calls for grants to complete Doctorado Industrial IND2020/AMB-17341, which was greatly appreciated. M.G. was supported by a "María Zambrano" contract for the Universidad Politécnica de Madrid, financed by the Spanish Ministerio de Universidades and by "European Union NextGenerationEU/PRTR".

REFERENCES

[1] Atencia, L. K., del Campo, M. V., Nowack Yruretagoyena, J. C., Tarquis Alfonso, A. M., and Hermoso Peralo, R.: Detection of plant water stress in Merlot vineyard using thermal sensors onboard UAVs , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-16111, https://doi.org/10.5194/egusphere-egu23-16111, 2023.

[2] Atencia Payares LK, Tarquis AM, Hermoso Peralo R, Cano J, Cámara J, Nowack J, Gómez del Campo M. Multispectral and Thermal Sensors Onboard UAVs for Heterogeneity in Merlot Vineyard Detection: Contribution to Zoning Maps. Remote Sensing. 2023; 15(16):4024. https://doi.org/10.3390/rs15164024.

[3] Atencia Payares LK, Tarquis AM, Hermoso Peralo R, Cano J, Cámara J, Nowack J, Gómez del Campo M. Soil vineyard variability evaluated with multispectral sensors on board of UAVs. X International Symposium on Irrigation of Horticultural Crops, Stellenbosch, South Africa, 29th January to 2nd February 2023.

[4] Nowack, J. C., Atencia, L. K., Gómez del Campo, M., and Tarquis, A. M.: Assessing plant water status in Merlot vineyards using Worldview-3 multispectral images in central Spain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16082, https://doi.org/10.5194/egusphere-egu23-16082, 2023.

How to cite: Atencia Payares, L. K., Nowack, J., Tarquis, A. M., Garcia, M., and Gómez del Campo, M.: Thermal and multispectral sensors model for determining the water status in a commercial vineyard in semiarid conditions., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17294, https://doi.org/10.5194/egusphere-egu24-17294, 2024.

In recent years, tremendous progress has been made in making the information gathered by sensors located on agricultural fields available almost immediately. Transferring the data directly to the cloud and rapidly presenting it to researchers, decision-makers, and farmers assist in optimally determining the timing, amount, and composition of fertigation. There have been ongoing efforts to reduce the technological and economic barriers to the efficient and reliable use of sensors that continuously monitor the root system’s heterogeneous and dynamic nature. Despite this, there are still many open questions related to determining the structure and installation locations of the sensors, the optimal algorithm with which the scheduling is determined, and how different sensing methods are combined to make optimal decisions.

Sensor development is usually done using in situ experiments. These complex and expensive experiments ultimately result in a long development time. Using numerical models may accelerate the development of sensing methods and the selection of the optimal algorithm for fertigation. Numerical models are used as a research tool for understanding, quantifying, and predicting phenomena and processes in the soil-plant-atmosphere system and for planning and managing water resources and their quality, including irrigation and drainage. Despite their complexity, numerical models are increasingly used thanks to a better understanding of water flow and solute transport processes, the development and improvement of mathematical methods for solving governing equations, and the accelerated development of computers capable of calculating different processes simultaneously in small intervals of time and space.

The presentation will review three sensing methods and present a combination of models that solve the water status and the fertilizer concentration in the root zone. The methods that will be reviewed are a) a tensiometer for measuring soil pressure heads, b) a suction cup for inferring soil solution concentrations, and c) a minirhizotron for evaluating the root system structure.

Determining optimal fertigation undoubtedly requires a multidisciplinary approach that considers the root zone’s physical, chemical, and biological characteristics. The combination of continuous measurements and numerical models may improve decision-making regarding resource application, thus optimizing the use of water and fertilizers while increasing economic profit and reducing environmental impacts.

How to cite: Lazarovitch, N. and Simunek, J.: Improving fertigation scheduling by combining continuous monitoring and numerical modeling of the root zone, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-158, https://doi.org/10.5194/egusphere-egu24-158, 2024.

Arid and semi-arid regions present significant challenges in efficient irrigation management and mitigation of soil salinity. To understand the dynamics of water and solute movement, such as salt transport in soil, software tools like HYDRUS are widely utilized. Hydrus-1D uses linear finite elements to numerically solve the Richards equation for saturated-unsaturated water flow, and has been widely applied in irrigation management to focus on solute and water movement.

This research focuses on estimating the hydraulic properties at field scale level using Kenyan soil data analyzed with soil spectroscopy and infiltration experiments. Saturated hydraulic conductivity (Ks) has been obtained by fitting data to infiltration obtained by the Green-Ampt (GA) model and Hydrus1D in three scenarios: with bounds on Ks and the product of front suction and effective porosity, assigning a uniform value to effective porosity and considering flow preferential paths. The results are compared with others pedotransfer functions (PTFs) and Hydrus-1D.

The Hydrus-1D software was used to study the water retention curve due to different Ks estimations. The findings show significant variations in the Ks estimations, highlighting the impact of salinity and preferential flows in heterogeneous soils. The comparison of the results provides valuable insights into the dynamics of water and salinity, essential for irrigation management in these regions.

This research emphasizes how crucial it is to choose and modify hydrological models for particular salinity situations and how important it is to take into account spatial variability and flow preferential paths when predicting and applying Ks through models. The results have significant implications for improving irrigation management and controlling soil salinity in semi-arid regions.

Keywords: Saturated hydraulic conductivity, Green-Ampt, HYDRUS-1D, irrigation management, soil salinity control.

"ACKNOWLEDGMENT

This article belongs to PCI2020-120694-2 Project funded by MCIN/AEI/10.13039/ 501100011033 and the European Union “NextGenerationEU”/PRTR.

We would like to thanks to One Planet Fellowship from African Women in Agricultural Research and Development (AWARD) and Agropolis Fondation for funding the analysis. “

How to cite: Matendo, S. E., Sánchez, R., Juana, L., and Zubelzu, S.: Determination of soil Hydraulic Properties using infiltration models and Hydrus 1D. Application to soils in Semi-Arid Regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19943, https://doi.org/10.5194/egusphere-egu24-19943, 2024.

The control of soil moisture is fundamental for optimizing water supply, plant performances and fruit quality. Traditional monitoring systems rely on a single sensor, or several sensors positioned along the soil profile not giving reliable information on soil water availability in the soil volume occupied by roots. In a 3-years field experiment we tested the effectiveness of PLUTO, an original approach able to define soil moisture profiles thanks to a bi- and tri-dimensional grid of sensors. The study was carried out, from 2021 to 2023, in northern Italy, on kiwifruit Zezy002 (A. chinensis var. chinensis) grafted, in 2012, onto micro-propagated Hayward (A. chinensis var. deliciosa) planted at a distance of 4.5 m x 2 m apart. During the experiment a traditional irrigation system (CONTROL) was compared to smart irrigation (PLUTO). Water management in the control treatment was carried out according to the advisory service, only based on daily evapotranspiration. On the other side, according to PLUTO water was applied taking into consideration the soil water content measured by potentiometric probes located according to the grid of sensors. Irrigation started when soil matric potential dropped below -0.1 MPa in more than 50% of the volume of soil explored by the root system and was aimed at returning the same amount of water lost the day before and estimated by evapotranspiration. During the experiment, compared to the CONTROL, PLUTO reduced the volume of water without impairing plant water status and yield. Fruit juice soluble solid concentration and fruit dry matter at harvest was increased by the smart irrigation system with a similar response also after 2 and 4 months of cold storage. PLUTO water management also induced a lower fruit firmness and yellow pulp color (defined by H angle) at harvest. In conclusion, the definition of irrigation volumes and timing according to smart irrigation system were able to reduce water consumption and increase fruit quality. Taking into consideration that the cost of sensors is progressively decreasing, PLUTO provides a cost-effective, operative, and precise solution to monitor soil water availability.

How to cite: Baldi, E., Quartieri, M., Golfarelli, M., Francia, M., Giovanelli, J., Mastroleo, M., Xilogiannis, E., and Toselli, M.: Precision soil moisture monitoring: use of a multi-sensor profiling for optimizing yield and fruit quality of yellow fleshed kiwifruit in northern Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16358, https://doi.org/10.5194/egusphere-egu24-16358, 2024.

The south-east of Spain faces a complex water scarcity scenario. Even though those regions have a high agricultural activity due to the advanced production system that has been developed. As it is important to optimize the use of water, from an economy, environmental and social point of view in these regions, it is needed to combine all available tools, including the technological and agnomical ones. Regulated deficit irrigation (RDI) techniques have been proved to be an efficient method of saving water in woody crops. Our hypothesis is that, as these RDI would cause a higher water stress in horticultural crops, that could be faced by biostimulation, as one of biostimulants claims is to improve the plant tolerance to abiotic stress, leading them to obtain a higher yield. This study aims to evaluate the effect of different strategies that combine the application of seaweed and microbials biostimulants with deficit irrigation programmes on the production parameters and soil quality in pepper (Capsicum annum sp.) under commercial greenhouse conditions.

With this aim two trials were carried out in commercial greenhouses (U & V), each one with two treatments:  i) irrigation according to Farmer criteria and ii) a combined treatment of RDI and the same biostimulation programme, that consisted of two application of Bacillus paralicheniformis after transplant via fertigation and four biweekly applications of Ascophillum nodosum extracts via fertigation and foliar spray. In each greenhouse, RDI was applied in different phenological stages,  from the onset of blooming to harvest in U trial or during the harvest in V trial.

The irrigation was reduced approximately 600 m³ ha^-1, implying a 12% savings respect to the Farmer irrigation schedule. The pepper yield had not been negatively affected, increasing the water productivity when RDI is combined with biostimulation. It is worth noting that when a water stress was applied, flowering and fruit setting seems to be promoted in biostimulated treatment, leading to a higher yield that non-biostimulated. Globally, the yield improvement has been due to a higher harvest of 1^st quality fruits.

This combined treatment has also improved the soil enzymatic activity in both greenhouses, suggesting that nutrients in the soil will become more available to plants when those are biostimulated.

Thus, the combined action of biostimulation under different strategies of irrigation reduction have been proved to be a useful strategy to improve agricultural sustainability, as it has increased the water productivity of the crop and the microbiological activity in the soil.

How to cite: Zapata García, S., Temnani, A., Berrios, P., Pérez-López, R. D., Monllor, C., and Pérez-Pastor, A.: Improvement of irrigation water productivity through water deficit and biostimulation in pepper under greenhouse conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22334, https://doi.org/10.5194/egusphere-egu24-22334, 2024.

Agriculture is causing unprecedented pressure on water resources to meet a growing food demand. This determines the necessity of implementing innovative, sustainable, and measurable systems to improve water use efficiency while increasing crop yield. This study tested the use of biodegradable mulching (BM) film for irrigated lettuce and the FAO AquaCrop model was used to simulate a precision irrigation scheme. The trials were conducted in the middle Arno River Valley, Tuscany, in Farm 1 (F1) and Farm 2 (F2) during the cropping seasons 2021 and 2022. In 2021 the BM film was tested in late spring at F1 and mid-summer at F2. In 2022, BM was tested twice at F2, in July and September, and once at F1, in June. The AquaCrop model was used only for the F2 mid-summer lettuce trial. Water Productivity (WP_i) and ISO 14046 Water Footprint (WF) were measured, and a correlation analysis was performed. The study's outcome reported larger lettuce plants in the F2 BM July trial (0.806 kg plant^-1) and smaller ones in F1 trial (0.100 kg plant^-1), where the plant density was higher. The amount of irrigation water required was reduced in all the BM trials, ranging between 8%-50%, with the best performance in the F2 BM September trial where the amount was halved. In general, WF was always reduced in the BM trials and the best performance was with the F2 BM July trial (0.13 m³kg^-1). Moreover, F2 indirect WF for the BM film production has a major share of impact on water resources ranging from 0.07 m³kg^-1 to 0.17 m³kg^-1. The best WP was also reached by F2 BM September trial (40.8 kg m^-3). The Pearson coefficient (r) reported a strong negative correlation between WF and WP (-.73, p = .01), while, the determination coefficient ( R²) was 0.545. Hence, is confirmed how the reduction of WF is followed by the rise of WP. However, the low R² shows how the two indicators are not specular but arrays of different useful information. Finally, AquaCrop simulation measured a fall in irrigation requirement (-86%, - 95%) in both treatments, reflecting an overestimation of the farmer irrigation scheme. The results confirmed the positive effect of BM and how using the WF can help farmers track their hotspots on water resources. The production of the BM films presented has a significant impact on water resources due to limited reuse over multiple crop cycles. Longer lasting films should be tested to investigate the reduction of indirect WF.

This study was carried out within the FEASAR-PSR 2014/2020 GO PEI PSGO 40/2017 ORTI BLU fund and the Agritech National Research Center and received funding from the European Union Next-Generation EU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D.1032 17/06/2022, CN00000022). This manuscript reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them.

How to cite: Renzi, N., Pacetti, T., Lompi, M., Castelli, G., Caporali, E., Setti, A., and Bresci, E.: Water footprint and water productivity analysis of an alternative organic mulching technology for irrigated agriculture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8048, https://doi.org/10.5194/egusphere-egu24-8048, 2024.