• Efficiency and productivity of water irrigation
• Scale-dependent and driven resilience in irrigated landscapes
• Resilience in coupled natural and human systems where ground and surface water and land are limiting resources for irrigation
• Traditional, novel, and transitional technologies for irrigation management and improvement
• Pros and cons of marginal water use in irrigated agriculture
• Better agronomic and irrigation management practices for soil biodiversity and natural ecosystems improvements and recovery.
• Information technologies , complex system integration and proximal and remote sensing in irrigated agriculture as alternatives to tackle current irrigation problems
• Agro-hydrological models and decision support systems to improve decisions in irrigation management and in safe surface water-groundwater interactions.
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Due to climate change, extreme weather conditions such as droughts may have an increasing impact on the water demand and the productivity of irrigated agriculture. For the adaptation to changing climate conditions, the value of information about irrigation control strategies, future climate development, and soil conditions for the operation of deficit irrigation systems is evaluated. To treat climate and soil variability within one simulation-optimization framework for irrigation scheduling, we formulated a probabilistic framework that is based on Monte Carlo simulations. The framework can support decisions when full, deficit, and supplemental irrigation strategies are applied. For the analysis, the Deficit Irrigation Toolbox (DIT) is applied for locations in arid and semi-arid climates. It allows the analysis of the impact of information on (i) different scheduling methods (ii) different crop models, (iii) climate variability using recent and future climate scenarios, and (iv) soil variability. The provided results can serve as an easy-to-use support tool for decisions about the value of climate and soil data and/or a cost-benefit analysis of farm irrigation modernization on a local scale.
How to cite: Schuetze, N. and Mialyk, O.: The value of information for the management of deficit irrigation systems, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19219, https://doi.org/10.5194/egusphere-egu2020-19219, 2020.
Measurements of soil water content are particularly useful for irrigation scheduling. In optimal conditions, field data are obtained through a dense grid of soil moisture sensors. Most of the currently used sensors for soil water content measurements, measure relative permittivity, a variable which is mostly dependant on water content in the soil. Spatial variability of soil characteristics, such as soil texture and mineralogy, organic matter content, dry soil bulk density and electric conductivity can also alter measurements with dielectric sensors. So the question arises, whether there is a need for a soil specific calibration of such sensors and is it dependant on the type of sensor? This study evaluated the performance of three soil water content sensors (SM150T, Delta-T Devices Ltd, UK; TRIME-Pico 32, IMKO micromodultechnik GmbH, DE; MVZ 100, Eltratec trade, production and services d.o.o., SI) in nine different soil types in laboratory conditions. Our calibration approach was based on calibration procedure developed for undisturbed soil samples (Holzman et al., 2017). Due to possible micro location variability of soil properties, we used disturbed and homogenized soil samples, which were packed to its original dry soil bulk density. We developed soil specific calibration functions for each sensor and soil type. They ranged from linear to 5th order polynomial. We calculated relative and actual differences in sensor derived and gravimetrically determined volumetric soil water content, to evaluate the errors of soil water content measured by sensors which were not calibrated for soil specific characteristics. We observed differences in performance of different sensor types in various soil types. Our results showed measurements conducted with SM150T sensors were within the range of manufacturer specified measuring error in three soil types for which calibration is not necessary but still advisable for improving data quality. In all other cases, soil specific calibration is required to obtain relevant soil moisture data in the field.
How to cite: Pečan, U., Kastelec, D., and Pintar, M.: Laboratory calibration of different soil moisture sensors in various soil types , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5956, https://doi.org/10.5194/egusphere-egu2020-5956, 2020.
The increase of irrigation efficiency and crop productivity in agriculture is nowadays a general requirement at national and international level to mitigate planet food security problems due to: the freshwater supply variability and scarcity enhanced by climate change (FAO, 2018), the increasing water demand due to population growth and also due to using ancient and sometimes empirical agricultural techniques.
To test the effectiveness of these irrigation strategies, on-field surveys alone are not enough. In the laboratory environment, a higher degree of insight is accessible, with a number of measurements that would be difficult on site.
In this work, we have tested the water dynamics related to the particular irrigation strategy developed in the SIM project (Smart Irrigation from soil moisture forecast using satellite and hydro-meteorological Modelling). The basic principle behind the strategy is that the soil moisture in the root-zone should be kept between the plant stress threshold and reducing deep percolation at all times. In that way, the irrigation amount is always enough so that the crop does not suffer water stress, and any water loss is avoided. As a comparison, two more common irrigation strategies have been tested in the same conditions: potential and deficit irrigation.
The laboratory set-up involves a wide range of instruments and devices: a lysimeter, a high-resolution (2g tolerance) scale, a thermal camera, a spectrometer, an infrared and an ultraviolet lamp, a radiometer and a leaf porometer. To increase the accuracy of the measurements, instead of working directly on the lysimeter, the crops have been cultivated in separate boxes, placed directly above the lysimeter. Three boxes have been managed according to each irrigation strategy. As a reference, one box has been kept with bare soil throughout the whole testing period, one has been filled with water and, finally, dense grass has been cultivated in another, totalling 12 boxes. The subdivision in boxes allows weighing each separately, guaranteeing higher accuracy.
The laboratory routine consisted of daily measurements of the weight of the boxes, together with measurements of temperature and leaf irradiance spectra. The evapotranspiration and percolation from each box are derived from the weight difference, and the water mass balance is closed for every box. At the end of the experimental set-up, the productivity for each irrigation strategy has been computed by measuring the final crop yield of each box.
Positive results, in terms of crop health and water savings, have been obtained with the SIM strategy.
How to cite: Paciolla, N., Ben Charfi, I., Corbari, C., and Mancini, M.: Laboratory lysimeters and proximal sensing data for optimizing irrigation water needs, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7195, https://doi.org/10.5194/egusphere-egu2020-7195, 2020.
High-resolution soil moisture product is important for agriculture-related managements including irrigation. We have investigated the Change Detection (CD) method using Sentinel-1 data for 100 m resolution soil moisture retrieval and got a Root Mean Square Error (RMSE) about 0.6 m3/m3. However, the result of this approach is not accurate enough for high-density crops like corn. Another approach needs to be studied to get better accuracy over all types of crops. The artificial neural network (NN) technique, which involves nonlinear parameterized mapping from an input vector to an output vector, is an appropriate tool for retrieving geophysical parameters from remote sensing data. Many studies have explored the NN approach for processing remotely sensed data, including retrieving soil moisture, however, only a few studies [Notarnicola et al., 2010; Paloscia et al., 2013, etc.] had investigated NN for soil moisture estimation over vegetation-covered areas, especially in a large scale.
The objective of this study is to develop an approach based on neural networks to estimate soil moisture at high resolution over vegetation-covered areas from Sentinel-1 C-band SAR data. The quality of the output results depends directly on the quality of the input data used to train the NN and the reference data for the training, therefore, we performed our study over Catalonia, where we have many auxiliary data. The study is performed using both VV and VH polarization over the whole Catalonia. Apart from Sentinel-1 SAR data, auxiliary data including Sentinel-2 NDVI, SMAP soil moisture, CCI (ESA Climate Change Initiative) land cover, SIGPAC (Sistema de Información Geográfica de Parcelas Agrícolas) land cover, irrigation index and crop type information from SIGPAC, and DEM (Digital elevation model) are also used for approach development. DISPATCH (Disaggregation based on Physical and Theoretical scale Change) soil moisture product at 1 km resolution is considered as the target in the Neural Network training, adding great value to our study. To prepare the Neural Network training, all data sets are co-registered at 1 km resolution within the same size and resampled for the same dates within one year (2017). Two indexes describing the normalized backscatter difference and soil moisture are introduced as equation (1) and (2):
|Index1 = (σ0i - σ0min) / (σ0max - σ0min)||(1)|
|Index2 = SMmin + (SMmax - SMmin) * Index1||(2)|
Different parameters were tested to train the Neural Network approach, the preliminary results show a correlation value compared with DISPATCH product about 0.71 over croplands, 0.73 over irrigated fields, and 0.65 over forests, considering Index1, Index2 and SMAP soil moisture. Works are still on-going to try to improve the results by better analyzing the SAR data performance over different fields and conditions. The final goal of the study is to produce 100 m resolution soil moisture product. After 1 km resolution study, we will apply the approach at 100m resolution, and the in-situ soil moisture will be used for validation.
This work is inscribed within the Water4Ever project, which is funded by the European Commission under the framework of the ERA-NET COFUND WATERWORKS 2015 Programme.
How to cite: Gao, Q., Escorihuela, M. J., Rodriguez-Fernandez, N., Merlin, O., and Zribi, M.: High-resolution soil moisture retrieval using a Neural Network approach from Sentinel-1 SAR data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9995, https://doi.org/10.5194/egusphere-egu2020-9995, 2020.
The sensitivity to water stress of different plant water indicators (PWI) at different plot scales (leaf and aerial) was evaluated during the second fruit growth stage of grapefruit (Citrus paradisi cv. Star Ruby) trees growing in a commercial orchard for a sustainable irrigation scheduling. Trees were drip-irrigated and submitted to two irrigation treatments: (i) a control (CTL), irrigated at 100% of crop evapotranspiration to avoid any soil water limitations, and (ii) a non-irrigated (NI) treatment, irrigated as the control until the 104 days after full bloom (DAFB) when the irrigation was suppressed, until to reach a severe water stress level in the plants (around -2.3 MPa of stem water potential at solar midday). The plant water indicators studied were: stem water potential (SWP); leaf conductance (Lc); net photosynthesis (Pn), and several vegetation indices (VI) in the visible spectral region derived from an unmanned aerial vehicle equipped with a multispectral sensor. The measurements were made at 9, 12 and 18h (solar time) on 50 and 134 DAFB, coinciding with a fruit diameter of 20 and 70 mm, respectively. The correlation analysis between the PWI at leaf scale (SWP, Lc and Pn) and at aerial scale showed relatively poor results, with Pearson correlation coefficients (r values) around 0.6. However, SWP presented the highest r value with the normalized difference vegetation index (NVDI), green index (GI), normalized difference greenness vegetation index (NDGI) and red green ratio index (RGRI) showing the higher coefficients 0.80, 0,80, 0.85 and 0.86, respectively. In addition, a quadratic regression curve fitting was made for the SWP and aforementioned indices, obtaining values of R2 around 0.7 in all cases; the best fit corresponded to SWP = - 4.869 + 15.765 NDGI - 14.283 NDGI2 (R2 = 0.749) to predict SWP values between -0.5 and -2.3 MPa. Results obtained show the possibility of using certain vegetation indices to be used in the detection of water stress in adult grapefruits, and thus propose a sustainable and efficient irrigation scheduling.
-WATER4EVER is funded by the European Commission under the framework of the ERA-NET COFUND WATERWORKS 2015 Programme
-RIS3MUR REUSAGUA is funded by the Consejería de Empresa, Industria y Portavocía of the Murcia Region under the Feder Operational Program 2014-2020
How to cite: Berrios, P., Temnani, A., Pérez, D., Gil, I., Zapata, S., Forcén, M., Ramos, T. B., Santos, F. N., López Riquelme, J. A., and Pérez-Pastor, A.: Multispectral reflectance vegetation indices are highly sensitive to water stress in grapefruit trees, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11408, https://doi.org/10.5194/egusphere-egu2020-11408, 2020.
Soil water storage (SWC) is a major spatio-temporal geophysical variable that control many atmospheric and hydrological processes including evaporation from soil surface, transpiration from plant cover, soil water uptake and plant growth. In agricultural practice is widely accepted that SWC is closely linked to plant water stress. In this respect SWC is used as main parameter in irrigation technology of agricultural crops with both uniform and non-uniform water application techniques. For both mentioned types of irrigation a determination of timing water application as well as dozes are critical for developing effective agricultural water management practices and improving of water use efficiency at sub-field scale. In case of uniform water application the SWC is averaged at the field level. In case of non-uniform water (variable rate) application the SWC is averaged for management zones at sub-field scale bringing spatially heterogeneous irrigated into group of quasi-homogeneous areas. Tuning of regulated deficit irrigation by management zones provide great opportunities to control more rigorously plant water stress at quite large agricultural field with site-specific patterns of spatial characteristics depending of surface topography as well as soil & plant cover properties.
A field experiment was conducted in summer 2012 at the Research Center of the Volghsky Scientific Research Institute for Hydrotechnics and Land Reclamation (VolgNIIGiM) located near town Engels (Saratov Region, Russian Federation) at the left bank of the middle part of the Volga river. Main aim of this experiment was to examine the spatial correlation between SWC and alfalfa yield production (AY) at plot of 400m2 which included one half of the field irrigated with pivot machine providing uniform water application. The results of the analysis of variation of both parameters was suspected to be essential to test the spatial correlation between them.
During the field experiment a SWC was monitored before and after 2nd alfalfa harvesting with electromagnetic sensor EM 38 (Geonics Ltd.). Spatial analyses of sets of SWC geodata showed a presence of quite stable patterns within irrigated and non-irrigated parts of experimental plot. Location of SWC patterns was controlled firstly by spatial variation of soil surface elevation forming some shallow ponds and secondly by narrow furrows of circular form formed by wheels of the irrigation machine connecting in some case not adjacent areas. To map alfalfa yield plant samples were harvested from about 10 to 10 m plots. Alfalfa yield data was resulted as organic carbon mass per m2 after drying in laboratory conditions. Spatial analysis of AY geodata set showed the presence of patterns like SWC patterns. The spatial correlation between SWC and AY indicated the quite strong relationship between both parameters.
Acknowledgments: The reported study was funded by RFBR, project number 19-29-05261 мк
How to cite: Zeyliger, A. and Ermolaeva, O.: An example of spatial correlation between soil water content and irrigated alfalfa yield at sub-field scale , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10944, https://doi.org/10.5194/egusphere-egu2020-10944, 2020.
Use of water pumping technologies (WPTs) to drive pressurized systems in smallholder irrigation schemes is one of the key interventions to secure water, hence to increase yields and to potentially alleviate poverty, as well as to foster local and global good security. Whichever the chosen WPT, smallholders face many decision-making variables when considering them: finances, information, technical performance, ease of use, market characteristics, and even environmental concerns are amongst them. We will present evidence that suggests that the way smallholders deal with those factors cannot be predicted based on mere land size-based classifications that are used in many existing policy studies and actual policies. As there are not many specific studies that focus on understanding the influence that the aforementioned variables, directly and indirectly, have on smallholders’ adoption of WPTs, we conducted field work in three different contexts—Nepal, Indonesia and Malawi—to identify the multidimensional gaps and relations between farmer and technology.
Due to the nature of the study, which comprised several (subjective) variables across a number of contexts and individuals, a triangulation of data collection techniques (e.g. direct observations, semi-structured interviews, surveys) was preferred. The main research method was Q-methodology, an increasingly popular inverted technique of factor analysis that combines the strengths of qualitative and quantitative research. Furthermore, one of its main advantages is that representativeness of the subjectivity does not depend on large samples of respondents but rather on their diversity.
By this process, it became evident that clustering farmers under the “smallholders” label—in line with the traditional farm size-based approach—did not reflect their heterogeneity in the WPTs’ adoption process. As a matter of fact, some smallholders are willing (and able, at times) to make substantial investments in WPTs for agricultural irrigation, thus moving away from the “external support-reliant-farmer” image. In conclusion, smallholder’s behaviour, thus decision making, is highly contextualized and cannot be underpinned by solely and simplistically looking at the holding size.
How to cite: Intriago Zambrano, J. C., van Dijk, R., van Beusekom, M., Diehl, J.-C., and Ertsen, M. W.: Smallholder: an inconvenient label in the adoption of water pumping technologies?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2304, https://doi.org/10.5194/egusphere-egu2020-2304, 2020.
The WATER4EVER Project (http://water4ever.eu/) was built on the premise that agriculture is by far the largest consumer of water, with about 70% of the diverted water being used in irrigation. Agriculture is also considered as a key source of diffuse pollution with inefficient practices resulting in high water and nutrient (particularly N and P) surpluses that are transferred to water bodies through diffuse processes (runoff and leaching), promoting eutrophication, with associated biodiversity loss. WATER4EVER aims thus to develop new monitoring strategies at the plot and catchment scales to provide detailed information of water and nutrient flow, and gain new insights on the connectivity between both scales. New monitoring strategies were developed and tested in agricultural fields in Portugal, Spain, Italy and Turkey and included: (i) crop physiological indicators assessment using static sensors for defining improved deficit irrigation strategies for woody crops; (ii) crop stress and productivity maps from measurements taken with a smart sensor mounted on a tractor and equipped with LIDAR 2D, normalized difference vegetation index (NDVI) and thermal cameras, and a GNSS receiver; (iii) leaf area index maps at 30 m resolution derived from ATCOR and Landsat 8 imagery data using the NDVI and the Soil Adjusted Vegetation Index (SAVI); (iv) soil moisture maps at 100 m resolution by combining the 10 m resolution synthetic-aperture radar (SAR) images from Sentinel 1 with the 10 m resolution NDVI computed from Sentinel 2 images, averaged into 100 m cells, and then by considering the backscatter difference with the driest day, or alternatively the backscatter difference between two consecutive dates; (v) soil moisture maps at 1 km resolution created with the DISaggregation based on a Physical And Theoretical scale CHange (DISPATCH) algorithm for the downscaling of the 40 km SMOS (Soil Moisture and Ocean Salinity) soil moisture data using land surface temperature (LST) and NDVI data; (vi) conventional monitoring techniques combined with modeling tools for assessing the impact of different soil managements (conventional tillage, tillage with grass trips, grass cover) on soil infiltration, soil water content, runoff and soil erosion of hillslope vineyards; (vii) an improved deterministic model for irrigation and fertigation management at the plot scale; and (viii) a decision support system for irrigation water management at the plot scale which integrated a deterministic model for irrigation scheduling and the NDVI computed from Sentinel 2 imagery data for crop growth monitoring. Preliminary results derived from the use of the innovative monitoring and mapping strategies, besides model applications are presented. The remote sensing products described above were also applied for catchment modeling validation of streamflow, which results fall outside the scope of this communication. WATER4EVER activities were thus wide and diverse, aimed at optimizing crop management practices which will help to promote the sustainability of different Mediterranean production systems.
WATER4EVER is funded by the European Commission under the framework of the ERA-NET COFUND WATERWORKS 2015 Programme
How to cite: Neves, R., Ramos, T., Simionesei, L., Oliveira, A., Grosso, N., Santos, F., Moura, P., Stefan, V., Escorihuela, M. J., Gao, Q., Pérez-Pastor, A., Riquelme, J., Forcén, M., Biddoccu, M., Rabino, D., Bagagiolo, G., and Karakaya, N.: Optimizing water use in agriculture to preserve soil and water resources. The WATER4EVER project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10599, https://doi.org/10.5194/egusphere-egu2020-10599, 2020.
The continuous intensification of agriculture along the High Plains in the US has been sustained by improvements in genetics, understanding of soil complexity, hydroclimate controls, and irrigation. The present work aims to identify the socioecological and sociotechnical processes involved in sustaining the intensification of yields in the past 50 years. We hypothesize that in the occurrence of extreme events, the boundaries of the agricultural systems –for example, water tradeoffs, governance, and natural availability—can be compromised, leading to a reduction in yields. Furthermore, the complexity of the Ag system –characterized by the interdependencies among complex hydroclimate, soil, and management – can change across spatial scales. The objectives are (1) to collect digital yield and climate data, as well as information about standards of water-for-agriculture; and (2) use the collected data to characterize the limits and limitations of the standards. In the proposed approach, the standards will represent our ability to manage resources, and ultimately create resilient water-for-food infrastructure in a changing climate.
How to cite: Munoz-Arriola, F.: Climate-resilient water-for-agriculture infrastructure: boundaries, complexities and standards in irrigated working lands, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21288, https://doi.org/10.5194/egusphere-egu2020-21288, 2020.
IrrigaSys is a decision support system (DSS) for irrigation water management based on online, open
access tools. The system includes remote access to local meteorological stations for weather
conditions, a MM5 model for weather forecast, the MOHID-Land model for the computation of the
soil water balance and irrigation scheduling, and a MySQL database for data repository. Despite its
complexity, the data necessary to run IrrigaSys is minimal, and include the location of the agricultural
field, crop type, sowing and harvest dates, soil texture, irrigation method, and daily/weekly irrigation
depths applied by the farmer. Based on this information, the system automatically downloads the
weather data from the meteorological station located closest to the agricultural plot, as well as the
weather forecast for the seven days following the current date. The soil water balance is then
computed for the previous and following week as well as the crop irrigation needs using the MOHIDLand
model. Results are made available via a web interface, a mobile app, SMS, and email. Besides the
model outputs, the IrrigaSys further provides the Normalized Difference Vegetation Index (NDVI) for
the agricultural field. The NDVI is computed from Sentinel 2 spectral bands with a resolution of 10m,
and is updated every time new Sentinel 2 imagery data (with cloud cover < 10%) is available. The
IrrigaSys has been developed in close cooperation with the Water Board Association of the Sorraia
Valley irrigation district (15360 ha), southern Portugal, over the last 5 years, supporting 103 plots of
30 farmers during the last irrigation season. As a result, the main limitation is naturally associated to
the difficulty in providing reliable estimates for all field plots based on calibrated model data. As the
next step, the service should start automatically identifying the culture status based on satellite
information as well as providing fertigation recommendations to farmers.
How to cite: Simionesei, L., Ramos, T. B., Palma, J., Oliveira, A. R., and Neves, R.: IrrigaSys – a decision support system for irrigation management in the Sorraia Valley region, Portugal, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9488, https://doi.org/10.5194/egusphere-egu2020-9488, 2020.
A possible way to save agricultural water is to improve global irrigation efficiency, defined as the ratio between the irrigation water volume used for crop transpiration and the total irrigation water volume. Higher irrigation efficiency leads to higher irrigation productivity ("more crop per drop") and profitability in relation to irrigation water costs: higher financial expectations for the same water and energy costs or identical financial expectations for lesser water and energy costs.
Improvements of irrigation efficiency may be sought either from better performing material (contextual relevance, technical quality or durability) or from optimized irrigation strategies with multiple levers of action (dates, doses, scheduling or trigger criteria, leaning on weather forecasts or not) and expected constraints (availability, quota, prefecture decrees).
It is even possible to handle these two issues at once by
(i) evaluating the irrigation water losses attributable to material (e.g. accidental pipe rupture or unavoidable intrinsic losses when using rainguns, spatial heterogeneity of water delivery),
(ii) evaluating the losses due to inadequate irrigation strategies (drainage or evaporation of irrigation water, excessive irrigation water storage in soils, losses due to wind drift)
(iii) gathering all losses in a "cascade scheme" that organizes them in phenomenological and chronological manner, somehow assuming successive losses "from the canal to plant roots",
(iv) exploring numerous irrigation scenarios that would allow reducing losses, improving efficiency and finally finding the minimal irrigation water amount required to reach the target agricultural yield (or fulfil a typical set of contradictory constraints, e.g. irrigation quota vs. objectives in crop yield profitability and possibly no drainage)
This was the scope of the recent development of the "Efficiency" module of the Optirrig model built for the generation, analysis and optimisation of crop irrigation scenarios.
How to cite: Cheviron, B., Serra-Wittling, C., Delmas, M., Belaud, G., Molle, B., and Dominguez-Bohorquez, J.-D.: Irrigation efficiency and optimization: the Optirrig model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20547, https://doi.org/10.5194/egusphere-egu2020-20547, 2020.
The comparative environmental studies on the modernization of irrigation systems are generally based on different areas with different characteristics (soil, dominant crops, crop management, or even weather conditions), not allowing for comparing the environmental effects in the same pre- and post-modernized irrigation district. Thus, there is a need to analyze the effect of the modernization process through the use of actual, detailed data from the same irrigation district.
The Violada Irrigation District (VID; 5234 ha, widely studied since the 1980s), with 92% of the surface modernized in 2008-09 form gravity to pressurized irrigation, offers an ideal scenario to evaluate the environmental implications of irrigation modernization.
The main tools for this evaluation have been (i) the water balance in the VID, to characterize the main irrigation water flows and their concentrations in salts and N, (ii) the soil water balance, to determine the main crops consumption [corn, alfalfa and cereal actual evapotranspiration (ETa)]; and (iii) the farmers surveys to establish fertilization and cropping practices. With all this information under both systems, the differences between the water and nitrogen use efficiencies for the main crops have been established for surface and sprinkler irrigation.
Comparing periods with similar crop patterns, dominated by corn, the modernization reduced the water abstraction for irrigation, decreased irrigation return flows and increased the consumptive use by the crops. Altogether, the modernization left more high-quality water available for other uses in the basin.
The irrigation and fertilization management also changed considerably with the modernization, allowing for lower doses with higher frequencies, and increasing the crop yields. Corn (the main crop in VID) showed the highest decrease in nitrogen fertilization. Nevertheless, the total nitrogen inputs to the system slightly increased due to the introduction of double crops. Thus, the corn increased water use efficiency and the nitrogen use efficiency.
The salt and nitrogen loads exported decreased after modernization, due to the reduced irrigation return flows. Under surface irrigation, the salts leaching was mainly produced during the irrigation season while under sprinkler irrigation, it took place all the year-round, avoiding the higher salt loads to the water bodies during the period of lower flow, when their environmental impact would be higher.
On the basis of the results obtained, it can be concluded that the modernization of the irrigation system caused a decrease in the flow restored to the basin, reduced the irrigation water depletion and preserved water quality globally. In this way, modernization leaves more water available for further uses and reduces the irrigation return flows and the pollutant loads associated with them. Finally, it was inferred that the salt and nitrogen loads emitted from the VID depend mainly on the irrigation system, and secondly, in regard to nitrogen only, on the prevailing crops.
How to cite: Jimenez-Aguirre, M. T., Ouahdani, S., Barros, R., and Isidoro, D.: Environmental effects of irrigation modernization in The Violada District (Spain), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10876, https://doi.org/10.5194/egusphere-egu2020-10876, 2020.
Water resources planning at the basin scale is the keystone to adaptation of water resources systems to socio-economic and climate changes. Simulation and optimization models can provide a useful support to the planning process. Besides including all significant processes, they need to incorporate the contribution of the relevant stakeholders from the early stages of their development, particularly in areas where multiple concurring uses of water resources occur and where surface water-groundwater interactions are important. This is the case of the plain of the Lombardy Region, Italy, where an ancient system of irrigation canals has been successfully used for centuries to supply huge amounts of water to a large irrigated area, which is also one of the most industrialized in Europe (Lombardy is one of the “Four Motors for Europe”, a transnational network of highly industrialized regions including Rhône-Alpes, Baden-Württemberg and Catalonia). Indeed, the Lombardy water resources have suffered recurrent crisis in the last years and a huge pressure has been raising on irrigation water use, which is by far the main consumptive use. We illustrate here an integrated approach to the analysis of different strategies of adaptation of irrigation systems to changing conditions, which accounts for the links between water use, crop production, energy consumption and hydrological conditions (as a proxy of the ecosystems quality). We will consider the case study of the Adda river basin, an 8,000 km2 basin including lake Como, where the requirements of hydropower production and irrigation supply need to strike a balance with lake tourism, flood protection and environment conservation.
The approach is based on a combination of simulation models (of upstream sub-basin, lake and downstream sub-basin) and optimization model (of lake regulation policy) that allow assessing the effects of different climate and technological scenarios. The former scenarios were obtained downscaling the regional climate projections provided by the CORDEX project till 2100, while for the latter we focused on measures to increase the efficiency of irrigation systems, that emerged as priority from the discussions with the stakeholders. Specifically, we considered different degrees of reconversion of irrigation methods from surface irrigation to more efficient methods (sprinkler or drip). The effects of the reconversion, under different climate projections, were assessed by running simulations with the IdrAgra spatially distributed agro-hydrological model, which provided the estimated values of crop water use, groundwater recharge, return flows, as well as of crop production and energy consumption. The comparison of different reconversion intensities was carried out considering indicators for the satisfaction of crop water requirements, the energy consumption, the groundwater recharge, and the river hydrological regime. A number of remarks can be made from the analysis of the results, among which it clearly emerged that under the current trend of increasing temperature already at the mid of the century irrigation deficits and impacts on the river hydrological regime will be intolerable unless the irrigation system efficiency will increase significantly in vast portions of the study area. Finally, a preliminary estimate of the cost of interventions is provided.
How to cite: Gandolfi, C., Castagna, A., Castelletti, A., Giuliani, M., Lippera, M. C., and Rienzner, M.: Water-food-energy-ecosystems nexus in irrigation systems adaptation to climate change: a case study of the Adda basin (Italy), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18482, https://doi.org/10.5194/egusphere-egu2020-18482, 2020.
Agricultural practices and technologies play a crucial role in the adaptation to climate change and disaster risk reduction, especially in contexts of high social and environmental vulnerability as in the Meso American Dry Corridor. This area, home to more than 40 million people and half of the smallholders basic grain farmers, is highly sensitive to El Niño phenomenon, associated to 30-40% decrease of precipitation and long periods of water shortages. This in turn makes agricultural production difficult to success and maintain subsistence livelihoods of the rural poor. Thus, adaptation to climate variability is key for sustainable development in the dry corridor.
In this study we develop a methodology to systematically review Good Agricultural Practices (GAP) for climate change adaptation and disaster risk reduction to gain a comprehensive overview of adaptation options that can guide policy recommendations at the local level. The food-water-energy nexus approach has been considered in this methodology.
The methodology starts analyzing good agricultural practices (GAP) already identified in the Meso American Dry Corridor documented by different types of actors (International organizations, NGOs, local and national governments, academia, private sector). They were classified in different agricultural subsectors (farming, livestock, agroforestry, forestry and fishing and aquaculture) regarding climate variability and several natural hazards such as drought and flood. Then, a live spread sheet database was developed where the best practices were organized following the criteria defined based on literature review and expert knowledge. These criteria were established to assess each potential good practice taking into account agroecological adequacy, socioeconomic viability, increase in resilience and environmental co-benefits, and specific consideration to the water-energy nexus. Finally, a group of 145 GAP were identified for the region.
Most of the GAP correspond to crop production, and they are mostly related to drought management and coping with interannual climate variability. It is observed that GAP are frequently implemented as a combination of practices and techniques as well as to face several hazards at the same time. In this regard, the analysis of water resources and the energy component should be seen under the food-water-energy nexus approach to ensure that a complete assessment of a potential GAP.
How to cite: Urquijo Reguera, J., Postigo, J. L., Puigdueta, I., Juarez, L., Sánchez Jacob, E., Ruiz Ramos, M., Hernández Díaz-Ambrona, C. G., and Rodríguez-Sinobas, L.: Inventory of Good Agricultural Practices for climate resilience in Central America, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21612, https://doi.org/10.5194/egusphere-egu2020-21612, 2020.
Water limitation is one of the main environmental constraints that adversely aﬀects agricultural crop production around the world. Precise and rapid detection of plant water stress is critical for increasing agricultural productivity and water use efficiency. Numerous studies conducted over the years have attempted to ﬁnd eﬀective ways to correctly recognize situations of water stress in order to determine irrigation regimes.
Water stress detection is currently done by various methods that are not ideal; these methods are often very expensive, destructive and cumbersome. Water stress in plants is also expressed at diﬀerent visual levels. Image processing is alternative way to visually recognize water stress levels. Such analysis is non-destructive, inexpensive and allows to examine the spatial variability of stress level under ﬁeld conditions.
In our study, we propose a new method for detecting water stress in corn using image processing and deep learning. For the purpose of collecting the images, we performed a three-months experiment, in which we took images of ﬁve diﬀerent groups of corn. Each group had a diﬀerent irrigation treatment, which led to ﬁve diﬀerent levels of water stress. The images were collected using a web camera located approximately 2 m from the plants.
Stress classiﬁcation was done by inserting processed images into a Convolutional Neural Network (CNN). Training the network was done using transfer-learning techniques in order to exploit the performance of an already trained CNN, for a fast and efficient training over the dataset. Testing the quality of classiﬁcation was done using extra camera which took a diﬀerent set of images.
Results were tested upon two sub-experiments - classiﬁcation of three types of treatments and classiﬁcation of ﬁve types of treatments; the results were 98% accuracy in classiﬁcation into three types of treatments (well-watered, reduced-watered and draught stressed treatment), and 85% accuracy in classiﬁcation into ﬁve diﬀerent treatments. These initial results are deﬁnitely excellent and can certainly serve decision making for optimal irrigation.
How to cite: Soffer, M., Lazarovitch, N., and Hadar, O.: Real-Time Detection of Water Stress in Corn Using Image Processing and Deep Learning, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11370, https://doi.org/10.5194/egusphere-egu2020-11370, 2020.
The water use in agriculture has been the focus of special attention, particularly in regions where the pressure on water resources has increased and the prospect of climate change suggests that the temporal and spatial distribution of rainfall will likely become more uncertainty. In particular, there are concerns in relation to the use of water to irrigate crops that demand relatively higher quantities of water, such as rice, which is traditionally grown under continuous flooding. It therefore requires much more irrigation water than non-ponded crops. On the other hand, rice is strategic for food security in some countries, and human water consumption in the whole Mediterranean is steadily increasing.
The work reported was conducted in the framework of an international project (MEDWATERICE, www.medwaterice.org), which started in 2019 and aims to explore the opportunity to apply water-saving, alternative, rice irrigation methods. The project is focused on improving the sustainable use of water in Mediterranean rice-based agro-ecosystems, and involves several rice-producing countries in the Mediterranean basin. The MEDWATERICE consortium includes universities, research centres and private companies operating in the Mediterranean area (Italy, Spain, Portugal, Egipt, Turkey, Israel). The methodology adopted in this project involves experimental fields for testing different alternative rice production practices that adopt innovative irrigation technologies, as well as selected rice varieties and the most appropriate agronomic practices, tailored to local conditions. The alternatives to be tested will be identified by a participatory action research approach through the establishment of Stakeholder Panels (SHPs) in each country, which will include regional authorities, water managers, farmers’ associations and consultants, and private companies involved in the rice production chain. The participation of SHPs in the project is expected to improve the transfer of project’s results to the agricultural sector and decision makers.
In particular, the situation corresponding to the case study of the Lower Mondego (Portugal), which is part of this project, is described. The Lower Mondego, which corresponds to the most downstream section of the river Mondego catchment, comprehends an agricultural area of around 15 000 ha. The main agricultural production is rice, which occupies about 60% of this region; this crop has a very significant social and economic value in the region; despite the small area under rice production, the number of farmers involved is large. Other important crops are corn and beans (18,1% of the area). The study will use a multi-scale approach (farm and irrigation district scales), multi-disciplinary (water consumption, product quality, environmental quality and economic and social sustainability), and multi-actors (SHPs will guarantee that interests of all actors involved in the water management of paddy areas and in the rice production chain will be considered within the project).
How to cite: de Lima, I., G. Jorge, R., L.M.P. de Lima, J., Abreu, J. M., and L. de Almeida, J. P.: A contribution to the sustainable use of water in rice production in the Mediterranean region: the Lower Mondego case study (Portugal), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11924, https://doi.org/10.5194/egusphere-egu2020-11924, 2020.
The Available Soil Water Capacity (AWC) is standard data in most soil databases and expresses soil water contents in the rootzone between field capacity (FC;-33 kPa) and permanent wilting point (WP; -1500 kPa). Literature suggests that increasing the content of soil organic matter (SOM) of a given soil does not significantly increase AWC and this has important implications when estimating soil moisture supply to crops and evaluating the potential for climate mitigation. For most crops, the real FC values vary between -10 and -50 kPa in different soils and WP values between -800 and -1500 kPa. Thus standard values for AWC of FC and WP do not represent field conditions in many soils. When exploring AWC for six Italian soil series, ranging from clay to sandy, AWC values at increasing %SOM were lower in clay soils and higher in sand as compared with actual conditions, which could be explained by considering the shape of the corresponding moisture retention curves. Rather than focus on static AWC values to define moisture supply to plants, real or actual soil moisture supply capacities (MSC) can be obtained by dynamic modeling of the soil-water-atmosphere-plant system, including a “sink-term” indicating a continuous relation between water uptake and negative pressure head of soil water and evaporative demand. Also, only models allow exploration of the effects of future severe IPCC climate scenario RCP 8.5. Thus, studying MSC for the six Italian soil series showed that MSC values were: (i) on average 30% higher than the corresponding AWC; (ii) distinctly different for the six soils; (iii) affected by declines of 1-9% as a result of the effects of future climate scenarios.; (iv) not significantly affected by increases of %SOM when considering climate change, except for the sand. Generalizations as to the effect of future climate scenarios and %SOM on MSC can only be realistic when modeling is performed for soil series in different climate zones. The contribution has been published in Geoderma journal by Bonfante A., Basile A., and Bouma J. (https://doi.org/10.1016/j.geoderma.2019.114079).
How to cite: Bonfante, A., Basile, A., and Bouma, J.: Exploring the effect of varying soil organic matter contents on current and future moisture supply capacities of six Italian soils, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13846, https://doi.org/10.5194/egusphere-egu2020-13846, 2020.
Sustainable irrigation water management is expected to accurately meet crop water requirements in order to avoid stress and, consequently, yield reduction, and at the same time avoid losses of water and nutrients due to deep percolation and leaching. Sensors to monitor soil water status and plant water status (in terms of canopy temperature) can help planning irrigation with respect to time and amounts accordingly. The presented study aimed at quantifying and comparing crop water stress of soybeans irrigated by means of different irrigation systems under subhumid conditions.
The study site was located in Obersiebenbrunn, Lower Austria, about 30 km east of Vienna. The region is characterized by a mean temperature of 10.5°C with increasing trend due to climate change and mean annual precipitation of 550 mm. The investigations covered the vegetation period of soybean in 2018, from planting in April to harvest in September. Measurement data included precipitation, air temperature, relative humidity and wind velocity. The experimental field of 120x120 m2 has been divided into four sub-areas: a plot of 14x120 m2 with drip irrigation (DI), 14x120 m2 without irrigation (NI), 36x120 m2 with sprinkler irrigation (SI), and 56x120 m2 irrigated with a hose reel boom with nozzles (BI). A total of 128, 187 and 114 mm of water were applied in three irrigation events in the plots DI, SI and BI, respectively. Soil water content was monitored in 10 cm depth (HydraProbe, Stevens Water) and matric potential was monitored in 20, 40 and 60 cm depth (Watermark, Irrometer). Canopy temperature was measured every 15 minutes using infrared thermometers (IRT; SI-411, Apogee Instruments). The IRTs were installed with an inclination of 45° at 1.8 m height above ground. Canopy temperature-based water stress indices for irrigation scheduling have been successfully applied in arid environments, but their use is limited in humid areas due to low vapor pressure deficit (VPD). To quantify stress in our study, the Crop Water Stress Index (CWSI) was calculated for each plot and compared to the index resulting from the Degrees Above Canopy Threshold (DACT) method. Unlike the CWSI, the DACT method does not consider VPD to provide a stress index nor requires clear sky conditions. The purpose of the comparison was to revise an alternative method to the CWSI that can be applied in a humid environment.
CWSI behaved similar for the four sub-areas. As expected, CWSI ≥ 1 during dry periods (representing severe stress) and it decreased considerably after precipitation or irrigation (representing no stress). The plot with overall lower stress was BI, producing the highest yield of the four plots. Results show that DACT may be a more suitable index since all it requires is canopy temperature values and has strong relationship with soil water measurements. Nevertheless, attention must be paid when defining canopy temperature thresholds. Further investigations include the development and test of a decision support system for irrigation scheduling combining both, plant-based and soil water status indicators for water use efficiency analysis.
How to cite: Morales Santos, A. and Nolz, R.: Assessing canopy temperature-based water stress indices for soybeans under subhumid conditions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16325, https://doi.org/10.5194/egusphere-egu2020-16325, 2020.
The Water4ever project aims to increase irrigation water and fertilization efficiencies through precision irrigation. The project has 3 major components: A technological component devoted to the development of measuring technologies based on optical sensors; a modelling component addressing both the local and the catchment scales; and a fieldwork component based on 3 case studies dedicated to vineyards and fruit trees where the new sensor and modelling tools will be combined with field data obtained by conventional monitoring and remote sensing. The project aims also to improve modelling at plot and catchment scale in order to quantify the effect of agriculture practices on water availability and quality. MOHID-Land is a physically-based, spatially distributed, continuous, variable time step model for the water and property cycles. It integrates four compartments or mediums (atmosphere, porous media, soil surface, and river network). In this study, the MOHID-Land model has been calibrated and implemented at plot scale in two of the project’s study cases, located in Portugal and Italy, that are representative of local vineyards, with different management, climate and topographical conditions: (i) the Vinha do Mel - Companhia das Lezírias (Portugal) is an irrigated vineyard of 14000 m2 with limited slope, while (ii) the Cannona Erosion Plots (NW Italy) are 1200 m2 portions of a rainfed hillslope experimental vineyard, with different inter-rows’ management. Water inputs (precipitation and irrigation), meteorological parameters and soil water content at different depths have been monitored in both plots during two years (2017-2018), using field sensors. Direct runoff measurements are available for the Cannona Erosion Plots. The vegetative development of the vineyards has been estimated from remote imagery. The field and remote datasets were used to calibrate and validate the MOHID-Land model, by comparing with simulated values of soil water content and LAI, with satisfactory to good efficiency of the model. The performance of the model was considered acceptable to support the IrrigaSys decision support system, using the Portuguese study case as reference for weekly irrigation recommendation in the region. The Italian study case was also used to estimate the water balance in two growing seasons with contrasting weather conditions, in order to evaluate the different behaviour with respect to the adopted soil management and the needing to introduce irrigation in a region where vines are traditionally rainfed.
WATER4EVER is co-funded by the European Commission under the framework of the ERA-NET COFUND WATERWORKS 2015 Programme.
How to cite: Capello, G., Biddoccu, M., Simionesei, L., Ramos, T., Oliveira, A., Grosso, N., Podder, P., Rabino, D., Bagagiolo, G., and Neves, R.: Use of Mohid-Land to model water balance for implementation of deficit irrigation in vineyards , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16459, https://doi.org/10.5194/egusphere-egu2020-16459, 2020.
The last decades have been characterized by an important development of viticulture in Italy, especially in Lombardy, where this sector is focusing on improving grapevine production, by enhancing quantity and, even more, quality. The increasing frequency of extreme meteorological events that has been observed in recent years has started raising concerns about the risks for grapevine quality and production, caused by summer heat waves and late spring frosts. The role of over-vine sprinklers in frost protection is well known; less so is their effect on heat stress protection. In fact, recent studies have shown that evaporation of sprayed water in the canopy layer during heat waves can reduce local air temperature through latent heat absorption by water evaporation. Moreover, in order to minimize the temperature-related stress, water spraying can be combined with the control of soil water content through drip irrigation, to lower soil temperature and enhance turgor maintenance.
The ADAM project (http://www.adam-disaa.eu/IT/DEFAULT.ASP#) fits into this research framework. The objective of the project is to develop a multifunctional irrigation strategy combining controlled soil water content and protection from temperature-related stress conditions. An experimental activity has started in the 2019 season in a Chardonnay vineyard located in the Colli Morenici area (Lombardy, northern Italy). Four irrigation management strategies have been compared, namely: no irrigation (NI); farmer’s drip irrigation (IT); automated drip irrigation, based on tensiometer measurements (IG); automated drip plus over-vine micro-sprinkler irrigation based on tensiometer measurements, temperature measurements and short-term forecast (IS). In the latter case, irrigation is activated before heat wave occurrence, based on 5days-ahead temperature forecasts (with 3 h refresh period).
At the end of the first year of experiment, we have obtained interesting preliminary results: while the first three strategies did not lead to significant differences in grape quality (in terms of sugars content, acidity and pH of musts), differences were found in all three parameters for the IS strategy. Specifically, pH and acidity are higher and sugars content is lower. Further analysis, including micro-vinification, are ongoing in order to assess the effects on wine quality. The experimental activity will continue in 2020 and 2021 with the aims of: collecting enough data to define a preliminary protocol for multi-functional irrigation management; assess the irrigation water requirements and the energy consumptions; test the effectiveness of VIS/NIR techniques for the quick measurement of crop conditions; verify the sustainability of the different strategies, both at the farm and district scale.
How to cite: Masseroni, D., Brancadoro, L., Guidetti, R., Beghi, R., Bianchi, D., Casson, A., Cazzaniga, S., Giovenzana, V., Modina, D., Ortuani, B., Tugnolo, A., and Gandolfi, C.: Multifunctional irrigation for viticulture adaptation to climate change: a case study in northern Italy , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16487, https://doi.org/10.5194/egusphere-egu2020-16487, 2020.
This paper presents a system that helps farmers to irrigate crops, minimizing water consumption, while productivity is kept, when deficit irrigation techniques are applied, according to the phenological stage of such crop. Such stage is automatically inferred by using a Machine Learning-based technique, which uses single images, which can be acquired by simply using a low cost commercial camera (even the one embedded in a smartphone), as inputs. Specifically, this work compares several Machine Learning approaches, in particular, classical and deep neural networks trained with a dataset obtained from taking multiple real images from a citrus crop. Such images represent different growing stages of the citrus associated to different phenological stages. Since, according to the deficit irrigation approach, the amount of water that can be reduced without affecting the yield depends on the phenological stage of the crop, once such stage is inferred, a Decision Support System uses such information for automatically programming irrigation. The paper also remarks the main advantages of using a single camera as unique sensor in terms of low economic cost as opposed to other systems that uses more expensive and invasive sensors in the crop. In addition, as a smartphone camera could be used as sensor, the smartphone itself could be used as computing device to run the phenological stage detector in real time, and to interact with the Decision Support System by using Cloud and Edge computing technologies. Finally, a set of experiments show the main results obtained after testing different Machine Learning approaches. After comparing such approaches, the best choice is selected to be integrated as a part of the mentioned Decision Support System.
How to cite: Forcén, M., Pavón Pulido, N., Pérez Noguera, D., Berríos Reyes, P., Pérez Pastor, A., and López Riquelme, J. A.: Machine Learning-based inference system to detect the phenological stage of a citrus crop for helping deficit irrigation techniques to be automatically applied., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18284, https://doi.org/10.5194/egusphere-egu2020-18284, 2020.
In the Mediterranean basin, rice is cultivated over an area of 1,300,000 hectares. The most important rice-producing countries are Italy and Spain in Europe (72% of the EU production; 345,000 ha), and Egypt and Turkey among the extra-EU countries (almost totality of the production; 789,000 ha). Traditionally, rice is grown under continuous flooding; thus, it requires much more irrigation than non-ponded crops. The MEDWATERICE project (PRIMA-Section 2-2018; https://www.medwaterice.org/) aims at exploring sustainability of innovative rice irrigation management solutions, in order to reduce rice water consumption and environmental impacts, and to extend rice cultivation outside of traditional paddy areas to meet the escalating demand. Within the MEDWATERICE project, irrigation management options to address the main site-specific problems are being tested for each rice areas involved in the project (IT, ES, PT, EG, TR). Case studies are being conducted in pilot farms, with the involvement of Stake-Holder Panels (SHPs) in each country. Data collected at the farm level will be extrapolated to the irrigation district level, to support water management decisions and policies. Moreover, indicators for quantitative assessment of environmental, economic and social sustainability of the irrigation options will be defined.
This work illustrates the first year of results for the Italian Case Study (Lomellina area, Pavia) at the pilot farm scale. This area is characterized by a growing water scarcity in drought years in many districts. Within the farm managed by the National Rice Research Center (CRR), in the agricultural season 2019 the experimentation was conducted in six plots of about 20 m x 80 m each, with two replicates for each of the following water regimes: i) water-seeded rice with continuous flooding (WFL), ii) dry-seeded rice with continuous flooding from the 3-4 leaf stage (DFL), and iii) water seeded-rice with alternate wetting and drying from fertilization at the tillering stage (AWD). One out of the two replicates of each treatment was instrumented with: water inflow and outflow meters, set of piezometers, set of tensiometers and water tubes for the irrigation management in the AWD plots. A soil survey was conducted before the agricultural season (EMI sensor and physico-chemical analysis of soil samples). Periodic measurements of crop biometric parameters (LAI, crop height, crop rooting depth) were performed. Moreover, nutrients (TN, NO3, PO4, K) and two widely used pesticides (Sirtaki – a.i. Clomazone; Tripion E – a.i. MCPA) were measured in irrigation water (inflow and outflow), groundwater, and porous cups installed at two soil depths (20 and 70 cm, above and below the plough pan). Finally, rice grain yields and quality (As and Cd in the grain) were determined. First results in terms of cumulative water balance components (rainfall, irrigation inflow and outflow, difference in soil and ponding water storage, evapotranspiration, net percolation), water application efficiency (evapotranspiration over net water input), and water productivity (grain production over net water input), will be presented and discussed. Results of a 1D Richard-equation-based numerical simulation model applied to generalize results obtained under the different irrigation regimes will be moreover illustrated.
How to cite: Facchi, A., Mayer, A., Chiaradia, E., Ricciardelli, A., Rienzner, M., Ortuani, B., Gharsallah, O., Gandolfi, C., and Romani, M.: Sustainable water use for rice agro-ecosystems in northern Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20534, https://doi.org/10.5194/egusphere-egu2020-20534, 2020.
Agricultural water use in irrigated areas plays a key role in the Mediterranean regions characterized by semi-arid climate and water shortage. In the face of optimizing irrigation water use, farmers must revise their irrigation practices to increase the drought resilience of agricultural systems and to avoid severe damages in agro-ecosystems. In this direction, during the last decades, the research has been focused on mathematical models to simulate the process of driving mass transport and energy exchanges in the Soil-Plant-Atmosphere system.
The objective of the paper was to test the suitability of the combination of FAO56 agro-hydrological model with remote sensing data retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform, to assess the spatiotemporal distributions of crop water requirement and to schedule irrigation in an irrigation district of the south-west of Sicily, Italy.
The proposed approach allowed obtaining the spatiotemporal distributions of soil and crop parameters used in the FAO56 model implemented in a GIS environment to simulate the water balance, as well as to assess the actual irrigation strategy. The GIS database was organized to include soil and crop parameters, as well as the irrigation volumes actually delivered to each farmer; the latter data can be used not only as input for water balance to evaluate the efficiency of the actual irrigation strategies but also to identify different irrigation scheduling scenario obtained by the FAO56 procedure.
The first application was carried out for the period 2014-2017, to identify a combination of irrigation scheduling parameters to be implemented in the model aimed at reproducing the ordinary strategy adopted by the farmers, based on the spatiotemporal variability of soil and climate forcings. When the model outputs were aggregated for single crop types, a fairly good agreement was found between simulated and actual seasonal irrigation volumes delivered either at the level of district and secondary units. Alternative scenarios of irrigation water distribution were then identified and analyzed, to provide irrigation technicians and policymakers a decision support tool to improve the efficiency of irrigation systems and to optimize the distribution based on the availability of water resources.
How to cite: Ippolito, M., Minacapilli, M., and Provenzano, G.: Combining the FAO56 agrohydrological model and remote sensing data to assess water demand in a Sicilian irrigation district , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-319, https://doi.org/10.5194/egusphere-egu2020-319, 2020.
In the last few decades, the use of centre-pivot irrigation systems has significantly increased, since it makes farming easier, is more efficient and less time-consuming compared to the other irrigation systems. Several studies have been focused on the hydraulics of the centre-pivot systems. Standard high-pressure impact sprinklers or low-pressure spray sprinklers or Low Energy Precision Application (LEPA) systems are generally mounted on the pipeline.
To ensure the uniformity of water application, the centre-pivot design requires increasing the flow rates along the lateral, because the sprinklers farther from the pivot move faster, and therefore their instantaneous application rates must be greater. Thus, the irrigated area under a centre-pivot system expands substantially with increasing system length. To irrigate the increased area by maintaining constant the application intensity, the manufacturers propose: i) to increase the flow rates of equally spaced sprinklers, ii) to gradually decrease the spacing of equal-flow sprinklers along the centre-pivot lateral, and iii) to use semi-uniform spacing, which is a combination of the first two methods.
However, the most common centre-pivot systems have equally spaced sprinklers with increasing flow rates (nozzle sizes) along the lateral, which is probably the easiest method from a practical point of view. Although many definitions and design procedures can be found in the technical literature, a universally accepted design procedure has not yet been found. In fact, the issue of centre-pivot irrigation system design is widely debated and there is still a need for simple, yet adaptive designing guidelines for farmers using these systems, specifically to maximize water use efficiency.
This study presents an alternative design procedure of centre-pivot irrigation system allowing to set favourable water application rates. First, the sprinklers’ spacing distribution corresponding to a fixed irrigated area along the radial direction is derived. According to this outcome, the results showed that sprinkler characteristics and/or pipe diameter need to be varied along the lateral, based on the desired and uniform water application rate. Then, for a practical case, an application based on the proposed hydraulic design procedure was performed and discussed.
How to cite: Baiamonte, G., Elfahl, M., and Provenzano, G.: Centre-pivot irrigation system design for uniform water application rate, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2711, https://doi.org/10.5194/egusphere-egu2020-2711, 2020.
In this study, we developed a simulation-optimization model for optimum water allocation to meet environmental flow requirements and agricultural demand. The simulation model consists of three modules: a hydrologic module, an agronomic module, and an economic module. The hydrologic module is based on a dynamic coupling of WEAP and MODFLOW, and includes water balances for the crop root zone, the surface water system, and the underlying aquifer. The agronomic module simulates the effect of deficit irrigation on crop yield response in each growth stage, while the economic module calculates the net benefit of crop production. The optimization model contains two objective functions, one related to agricultural production and the other related to environmental flows. These conflicting objective functions are maximized using the Multi-Objective Particle Swarm Optimization algorithm. Decision variables include crop acreages, minimum environmental flow requirements in the river, and the degree of deficit irrigation. We applied the simulation-optimization model to the irrigated Miyandoab plain in the semi-arid northwest of Iran, for the historical period 1984 to 2013. There is competition between irrigation demands in the plain and environmental flow requirements to downstream Lake Urmia, which has been shrinking in recent years due to decreased inflows. Our results quantify what the (Pareto) trade-off looks like between meeting environmental and agricultural water demand in the region. We find that historical water allocations were suboptimal and that both agricultural and environmental benefits can be increased by better management of cropping decisions, deficit irrigation, and environmental flow requirements. We further show that increased groundwater use for irrigation can partly alleviate the trade-off, but that it leads to significant declines in groundwater levels due to the relatively small specific yield of the aquifer.
How to cite: Dehghanipour, A., Schoups, G., and Zahabiyoun, B.: Simulation–optimization model for optimum water allocation between environmental and agricultural demand using a coupled WEAP-MODFLOW model: Application in Miyandoab plain, Urmia basin, Iran, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3143, https://doi.org/10.5194/egusphere-egu2020-3143, 2020.
Irrigation is the most important water use sector that can impact land-atmosphere feedback and climate. The use of irrigation is increasing but its effects on climate are still ignored in most of the climate models due to the lack of accurate information on its sources or its extent over the whole globe. The only map that presented a global inventory on the extent of areas irrigated with groundwater and surface water was published in 2013 (Siebert et al., 2013). Here, we take advantage of the abundance of global satellite observations to investigate the effects of irrigation on long-term trends in essential climate variables: (i) temperature obtained from the Climate Research Unit (CRU data), (ii) precipitation obtained from CRU, Global Precipitation Climatology Project (GPCP), and Global Precipitation Climatology Centre (GPCC), (iii) soil moisture obtained from Soil moisture and ocean salinity (SMOS) satellite, (iv) evapotranspiration obtained from CRU and the Global Land Evaporation Amsterdam Model (GLEAM), and (v) land cover based on the multi-epoch ESA LC dataset. Based on the potential links between the existing information of irrigation and these five climate and land-surface variables, possible tracking of the irrigation extent over other regions, where no information exist, will be investigated. This study is ongoing and preliminary results will be presented.
Siebert, S., Henrich, V., Frenken, K., Burke, J., 2013. Update of the Global Map of Irrigation Areas to version 5. Proj. Rep.
How to cite: Ducharne, A., Al-Yaari, A., Cheruy, F., and Wigneron, J.-P.: Can we deduce irrigation trends at global scale from the ones of essential climate variables?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3582, https://doi.org/10.5194/egusphere-egu2020-3582, 2020.
The “enarenado” (sand-covering soil) is a technique used in greenhouses located in the southeast of Spain that consists of placing a layer of soil between 20 and 40 cm above the original material, a thin layer of organic matter and above it a layer of sand of about 5 to 10 cm.
It is necessary to know the shape of the wet bulb produced by the emitters for a correct design and management of the drip irrigation systems. In stratified soils, as in the case of “enarenado” soils, the distribution of water can change substantially with respect to the case of homogeneous soils. The objective of this work is to present the methodology of data acquisition and the actions carried out so far to obtain a model that precisely defines the evolution of humidity in wet bulbs generated in “enarenado” soils characteristic of intensive horticultural crops.
The tests have been carried out at the facilities of the IFAPA Center La Mojonera, Almería, SE Spain. The textures of the added soils are sandy loam and clay loam, representative of the horticultural crops of Almeria. The crop was pepper , Mazo variety, planted on September 15, 2018 in the two selected greenhouses. The irrigation is automatic, with drippers of nominal flow Qn = 3 l / h, self-compensating, anti-drainage of Netafim. Irrigation control is carried out using classic tensiometers with built-in pressure transducer.
The humidity has been measured at 10 points distributed around a dripper, 7 probes at 5 cm deep in the added soil layer and 3 probes at 18 cm depth, near the original soil layer. The sensor used is TE5 Decagon. The plantation frame coincides with that of the drippers, Sg = 50 cm and Sr = 120 cm, for this reason the probes were placed up to half of the plantation frame.
The data collected show a small variation in humidity over time. That is, the added soil, with a clayey texture, quickly redistributes moisture and the probes register very small variations.
Once the values specified in the methodology have been measured, the theoretical humidity retention curves of greenhouse soils have been calibrated. With the data collected, the system has been simulated by completing the fields that the Hydrus model needs. This operation has been specified in the definition of a simple, multi-layered 3D model. In general, the model predicts moisture behavior well in the conditions set.
How to cite: Roldán Cañas, J., Zapata Sierra, A. J., Reyes Requena, R., and Moreno Pérez, M. F.: Study of the wet bulb in drip irrigation in stratified soils using HYDRUS, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4575, https://doi.org/10.5194/egusphere-egu2020-4575, 2020.
Optimizing irrigation management requires increasing the accuracy of moisture monitoring in soils or substrates, especially when it depends on electronic sensor readings. Substrates are widely used in horticulture, for growing urban ornamental plants, as well as on green roofs. Due to the lack of information about the accuracy of soil water content sensors on substrates, this research was carried out to evaluate the accuracy of the 10HS sensor (Decagon Devices Inc., Pullman, WA) to estimate soil water content (SWC) in organic substrates and mineral soil. The study was carried out at the Hydrology Laboratory of the University of Palermo. The sensors were inserted into substrates or soil in conical vessels (4 dm3 volume), drilled at the base to measure the drained volume and covered with a transparent film to limit surface evaporation. For both the substrates (A and B) and the mineral soil (C), a known amount was placed in the vessel and compacted to a value of bulk density equal to 0.177 g cm-3, 0.471 g cm-3, 1.480 g cm-3, respectively. The sensors were connected to a CR1000 datalogger (Campbell Scientific Inc., Logan, UT), which allowed the data acquisition and storage. The tests were conducted by wetting the samples with the progressive addition of known volumes of water (about 40 cm3) that were evenly distributed over the sample surface. After the end of the redistribution process of water applied to the container, the sensor readings were acquired. SWC monitoring was performed until reaching the value corresponding to the field capacity. The calibration equation recommended by the sensor manufacturer systematically underestimated the values of SWC of about 5% or more when the substrate A and B were used. On the other hand, when evaluating the sensor performance in the mineral soil (C), it was observed that the errors associated with the manufacturer's equation resulted in ±5%. Therefore, for both substrates specific calibration is necessary to improve the sensor’s accuracy, even accounting for the bulk density; on the other hand, for the mineral soil, the manufacturer's equation can be considered suitable.
How to cite: Provenzano, G., Gugliuzza, G., and Duarte Guedes Cabral de Almeida, C.: Response of Decagon 10HS soil water content sensor to different porous media, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5751, https://doi.org/10.5194/egusphere-egu2020-5751, 2020.
Soil water content is an important parameter for irrigation management. Among the indirect methods to determine soil water content (SWC), there are electronic sensors, that need site-specific calibration to increase the accuracy of the measurements. In this research, a capacitance probe (Diviner 2000®, Sentek Pty Ltda., Australia) was calibrated for two agricultural soils. The experiment was carried out in a protected environment at the Federal Rural University of Pernambuco (UFRPE), Brazil. The textural classes of soils were sandy clay loam (66% sand) and sandy (95% sand). Undisturbed and disturbed soil samples were collected in the soil top layer (0-30 cm). The disturbed soil samples were initially air-dried, passed through a 4.75 mm mesh sieve, and then introduced to fill eight vessels (four replications for each soil). These vessels, equipped with drainage holes, have lower and upper diameters of 15 cm and 25 cm, respectively, and height of 22.5 cm (4.66 L). In each pot, a 5 cm layer of gravel with an average diameter of 2 cm covered with bidim® geotextile was disposed before introducing the soil. During filling, the soil was compacted to reach the same bulk density measured on the undisturbed samples (sandy clay loam: 1.54 g cm-3 and sandy: 1.50 g cm-3). In the center of each pot, a PVC access tube was installed. According to the manufacturer's recommendation, during calibration, the probe normalization was performed. The pots were wetted by capillary rise and, once saturated, they were placed on a bench for drainage. After this process stopped each pot was daily weighed at a fixed time (8 a.m.), and the sensor reading was acquired until when the daily mass variations became negligible. Data were used for regression analysis to fit the site-specific calibration equation and to evaluate the mean error. Linear calibration equations, characterized by R2=0.931 and 0.986, were obtained for the sandy clay loam and the sandy soil, respectively. The mean errors (ME) associated with the manufacturer’s equation resulted in -0.05 and -0.01 for sandy clay loam and for sandy soil and decreased after calibration. The results confirmed the suitability of the manufacturer's equation in sandy soils. On the other hand, the manufacture’s equation slightly underestimated SWC, in sandy clay loam soil, especially in the range above 0.26 m3 m-3. The Diviner 2000 probe can be therefore successfully used to support irrigation management in irrigated areas with soils similar to those investigated because it is easy to operate and allows fairly accurate estimations of soil water content.
How to cite: Duarte Guedes Cabral de Almeida, C., Barreto Franco, L., Barbosa dos Santos, J. E., Gomes de Almeida, B., and Provenzano, G.: Calibration and evaluation of a capacitance probe in agricultural soils in northeast Brazil, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5774, https://doi.org/10.5194/egusphere-egu2020-5774, 2020.
New technologies in agriculture present the opportunity to create intuitive and user-friendly decision support systems, and to improve the productivity of the systems requiring water and energy. In the last few years, the adoption of these technologies have been increasing through third mission activities, and the collaboration between researchers, consultants, agri-food managers and farmers.
The general objective of the proposed dissemination activity carried out by the AgrHySMo laboratory of the University of Pisa, was to transfer a soil moisture-based wireless sensor network (SM-WSN) to a commercial pear orchard named Illuminati Frutta (Arezzo, Italy), for the feedback control of irrigation.
The plan of the third mission activity was designed by the following phases: i) the team evaluated the hydraulic performance and management of the irrigation system in the pear orchard; ii) the use of proximal sensing provided the NDVI for the biophysical characterization of the crop in a pilot area extended thirteen ha; iii) the open-source QGIS suite program was used to elaborate the collected images, to assess a zoning analysis, and to discretize homogeneous areas inside the orchard. These zoning maps were used to define the topology of the SM-WSN.
The orchard was characterized by four homogeneous zones, inside which at least one sensor of soil water content (FDR Drill and Drop probe, Sentek Inc.) was installed. A total of 6 probes were installed in the pilot area. The hardware and the smartphone of the dedicated sensor network applications, AgriNET, were provided by Tuctronics (Walla Walla, Washington, USA). The measurements of volumetric soil water contents are sent to a platform using the MODBUS RTU protocol interfaced with a communication board and then delivered, using the cellular 3G data network, to a MySQL database operated by AgriNET/Tuctronics accessible from the web. According to the ordinary scheduling of irrigation, the expert system allowed the farmer to maintain the soil water content within a pre-defined optimal range, which upper limit corresponds to the soil field capacity and the lower is the threshold below which water stress occurs. During the first experimental growing season, by considering the results obtained in the pilot plot, compared with the ordinary irrigation scheduling the farmer saved up to 35% of the water and energy supply. In the future, the proposed feedback control of irrigation protocol will be extended to the entire farm. Thus, the adoption of this new technology aimed at identifying the most appropriate irrigation management, have the potential to generate positive economic returns and to reduce the environmental impacts.
How to cite: Puig Sirera, A., Rallo, G., Giusti, S., Provenzano, G., Sbrana, A., Tuker, J., and Massai, R.: Expert soil moisture Wireless Sensor Network for the feed-back control of irrigation in heterogeneous crop systems, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-982, https://doi.org/10.5194/egusphere-egu2020-982, 2020.
Tomato (Solanum lycopersicum L.), grown under greenhouse in Ecuador, has a major weight in the farmers’ income in regions with water scarcity. In the one hand, these areas show small water use efficiency caused by the non-technical criteria in the design of drip irrigation systems. On the other hand, farmers are unknown of the tomato water requirements, and do not know how to determine them. Moreover, they do not know how much water apply and the irrigation frequency depends on the availability of farmers’ time. In addition, in most cases greenhouses are lacked of equipment to measure climatic conditions.
This study evaluates different irrigation strategies, and its efficiency in the use of water, in the cultivation of tomatoes under greenhouse. It considers also they effect on the production and organoleptic quality of the fruit (size, dry matter and number of fruits). The methodology, first estimates the tomato water needs which was developed through the measures taking with practical and affordable equipment for farmers in the area. Then, the optimal water depth for irrigation was estimated on a daily basis application. Likewise, two factors were evaluated: number of irrigations per day (one or two) and water depth (80%, 100%, 120% of crop evapotranspiration, and the one applied by local farmers). Thus, the combination of the two factors resulted in eight irrigation strategies which were implemented in irrigation plots following a randomized block design with four repetitions. The evaluation was accomplished in the four crop harvest over one year. The results helped to develop sustainable irrigation criteria for tomato crop under greenhouse in the area. These have improved water use efficiency, and maintained the production and quality of the fruits, which will be beneficial not only for the farmers’ income but also in agriculture's resilience .
How to cite: Rodriguez-Sinobas, L., Colimba-Limaico, J. E., and Zubelzu, S.: Development of criteria to improve water use efficiency in tomato crop (Solanum lycopersicum L.) under greenhouse in Ecuador, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11344, https://doi.org/10.5194/egusphere-egu2020-11344, 2020.