Managing water scarcity in European and Chinese cropping systems
Department of Irrigation, Drainage and Landscape Engineering, Faculty of Civil Engineering, Czech Technical University in Prague, Thakurova 7, 16629 Prague, Czech Republic
*Author to whom correspondence should be addressed.
Water 202012(6), 1787;
Received: 26 April 2020 / Revised: 11 June 2020 / Accepted: 20 June 2020 / Published: 23 June 2020
Accelerated soil erosion by water has many offsite impacts on the municipal infrastructure. This paper discusses how to easily detect potential risk points around municipalities by simple spatial analysis using GIS. In the Czech Republic, the WaTEM/SEDEM model is verified and used in large scale studies to assess sediment transports. Instead of computing actual sediment transports in river systems, WaTEM/SEDEM has been innovatively used in high spatial detail to define indices of sediment flux from small contributing areas. Such an approach has allowed for the modeling of sediment fluxes in contributing areas with above 127,484 risk points, covering the entire Czech Republic territory. Risk points are defined as outlets of contributing areas larger than 1 ha, wherein the surface runoff goes into residential areas or vulnerable bodies of water. Sediment flux indices were calibrated by conducting terrain surveys in 4 large watersheds and splitting the risk points into 5 groups defined by the intensity of sediment transport threat. The best sediment flux index resulted from the correlation between the modeled total sediment input in a 100 m buffer zone of the risk point and the field survey data (R2 from 0.57 to 0.91 for the calibration watersheds). Correlation analysis and principal component analysis (PCA) of the modeled indices and their relation to 11 lumped characteristics of the contributing areas were computed (average K-factor; average R-factor; average slope; area of arable land; area of forest; area of grassland; total watershed area; average planar curvature; average profile curvature; specific width; stream power index). The comparison showed that for risk definition the most important is a combination of morphometric characteristics (specific width and stream power index), followed by watershed area, proportion of grassland, soil erodibility, and rain erosivity (described by PC2).

FranciscoPedreroa, S.R.Grattanb, AlonBen-Galc, Gaetano AlessandroVivaldid

aDepartment of Irrigation, CEBAS-CSIC, Campus Universitario de Espinardo, 30100, Murcia, Spain
bDepartment of Land, Air and Water Resources, University of California, Davis, 95616, USA
cInstitute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Gilat Research Center, M.P. Negev, 85280, Israel
dDipartimento di Scienze Agro-Ambientali e Territoriali, Università Degli Studi Di BariAldo Moro, Via Amendola 165/A, 70126 Bari, Italy

Received 29 April 2020, Revised 9 June 2020, Accepted 11 June 2020, Available online 23 June 2020.


Olive trees are iconic to the Mediterranean landscape and in recent times, have expanded to other regions across the globe that share similar climatic conditions. Olive oil production benefits from irrigation, but with a changing climate and uncertainty in precipitation patterns, wastewaters will likely play a larger role supplementing irrigation water requirements. However, due to their relatively poor quality, wastewaters present challenges for sustained long-term use in olive production. Wastewaters include all effluents from municipalities, agricultural drainage, animal production facilities, agricultural processing and industrial processes. This review focuses on potential opportunities and limitations of sustaining olive oil production in the Mediterranean region using wastewater of various sources. The primary challenges for using such wastewaters include concerns related to salinity, sodicity, metals and trace elements, nutrients, organics, and pathogens. Organics and plant nutrients in the effluents are typically beneficial but depend on dosages.

Many studies have shown that saline wastewaters have been successfully used to irrigate olives in Greece, Israel, Italy, Jordan and Tunisia. Still, olive varieties and rootstocks have different tolerances to salinity and could respond differently and oil quality may improve or be compromised. Salts and trace elements need to be monitored in plants and soil to make sure accumulation does not continue from year to year and that soil physical conditions are not affected. Some food industries generate effluents with suitable characteristics for irrigation but one must balance the benefits (e.g. addition of nutrients), detriments (e.g. addition of salts or other limiting chemicals) and costs when determining the feasibility and practicality of reuse. Long-term accumulation of trace elements and metals will likely limit the feasibility of using industrial-originating effluents without treatment processes that would remove the toxic constituents prior to reuse. Therefore, untreated wastewaters from the many industries have limited long-term potential for reuse at this time. Application of olive mill wastewater may be agronomically and economically beneficial, particularly as a local disposal solution, but there are concerns associated with high-concentrations of polyphenols that may be phytotoxic and toxic to soil microbial populations.

With regards to human safety, risk of contamination of table olives and olive oil is very low because irrigation methods deliver water below the canopy, fruits are not picked from the ground, processing itself eliminates pathogens and the irrigation season typically ends days or weeks before the harvest (depending on the climate condition). Finally, considering physiological, nutritional and intrinsic characteristics of this species, it is clear that olive trees are appropriate candidates for the reuse of recycled water as an irrigation source.

Agricultural Water Management 2020, 240, 106254;

Partner Publication (CSIC & UCO):

Tomás R. Tenreiro a,*, Margarita García-Vila b, José A. Gómez a, José A. Jimenez-Berni a, Elías Fereres a,b

a InstituteforSustainableAgriculture(CSIC),14004Cordoba,Spain

b DepartmentofAgronomy,UniversityofCordoba,14014Cordoba,Spain


• Scaling up point-based simulation modelling is a challenge due to the heterogeneity of water-related processes, and it is essential for many applications in precision agriculture.

• Seven crop simulation models and five hydrologic models were selected and their water modelling approaches were systematically reviewed for comparison. Regarding spatial modelling of water at crop field level, our analysis indicates that there is scope for conceptual improvements, but that combining both types of models may not be the best way forward.

• The most promising advances are related to the incorporation of surface inflow and subsurface lateral flows, by using differential equations or through novel water spatial partitioning relations to use in discrete-type approaches.

R.López-Urreaa, J.M.Sánchezb, A.Montoroa, F.Mañasa, D.S.Intriglioloc

a Instituto Técnico Agronómico Provincial (ITAP), Parque Empresarial Campollano, 2ª Avda. Nº 61, 02007, Albacete, Spain
b Dept. of Applied Physics, Regional Development Institute (IDR), Univ. of Castilla-La Mancha, Av. España, s/n, 02071 Albacete, Spain
c Departamento de Riego, Centro de Edafología y Biología Aplicada del Segura (CEBAS-CSIC) Espinardo, Murcia, Spain

Received 18 December 2019, Revised 25 May 2020, Accepted 31 May 2020, Available online 16 June 2020.

SOIL Discuss., 2019;

Partner Publication (IAS – CSIC):

José A. Gómez1, Gema Guzmán2, Arsenio Toloza3, Christian Resch3, Roberto García-Ruíz4, and Lionel Mabit3

1Institute for Sustainable Agriculture-CSIC, Córdoba, Spain

2Applied Physics Dept., University of Córdoba, Spain

3Soil and Water Management and Crop Nutrition Laboratory, FAO/IAEA Agriculture & Biotechnology Laboratory, IAEA Laboratories Seibersdorf, Austria

4Animal and Plant Biology and Ecology Dept., Ecology section, Center for advance studies in olive groves and olive oils, University of Jaén, Spain



This study compares the distribution of bulk soil organic carbon (SOC also reported as Corg), its fractions (unprotected, physical, chemical and biochemically protected), available P (Pavail), organic nitrogen (Norg) and stable isotopes (δ15N and δ13C) signatures at four soil depths (0–10, 10–20, 20–30, 30–40 cm) between a nearby forested reference area and an historical olive orchard (established in 1856) located in Southern Spain. In addition, these soil properties, as well as water stable aggregates (Wsagg) were contrasted at eroding and deposition areas within the olive orchard, previously determined using 137Cs. Results highlight a significant depletion of SOC stock in the olive orchard as compared to the forested area, approximately 120 vs. 55 t C ha−1 at the top 40 cm of soil respectively, being severe in the case of unprotected carbon fraction. Erosion and deposition within the old olive orchard created large differences in soil properties along a catena, resulting in higher Corg, Pavail and Norg contents and δ15N at the deposition area and therefore defining two areas with a different soil quality status (degraded vs. non-degraded). Differences in δ15N at such different catena locations suggest that this isotopic signature has the potential for being used as an indicator of soil degradation magnitude, although additional studies would be required to confirm this finding. These overall results indicate that proper understanding of Corg content and soil quality in olive orchards require the consideration of the spatial variability induced by erosion/deposition processes for a convenient appraisal at farm scale.


International Soil and Water Conservation Research, 2020,


José A. Gómeza, Alon Ben-Galb, Juan J. Alarcónc, Gabrielle De Lannoyd, Shannon de Roosd, Tomáš Dostále, Elias Fereresf, Diego S. Intriglioloc, Josef Krásae, Andreas Klikg, Gunther Liebhardg, Reinhard Nolzg, Aviva Peetersh, Elke Plaasi, John N. Quintonj, Miao Ruik, Peter Straussl, Xu Weifengk, Zhiqiang Zhangm, Funing Zhongn, David Zumre, Ian C. Doddj


a Institute for Sustainable Agriculture, IAS, CSIC, Avda Menendez Pidal S/N, Cordoba, Spain

b Agricultural Research Organization, Gilat Research Center, Israel

c Centro de Edafología y Biología Aplicada Del Segura (CSIC), Dept. Riego, Murcia, Spain

d Department of Earth and Environmental Sciences, KU Leuven, Heverlee, Belgium

e Czech Technical University in Prague, Faculty of Civil Engineering. CVUT, Prague, Czech Republic

f Agronomy Department, University of Cordoba, Cordoba, Spain

g University of Agricultural Sciences Vienna (BOKU), Vienna, Austria

h TerraVision Lab, Midreshet Ben-Gurion, Israel

I Georg-August-Universität Göttingen, Germany

j Centre for Sustainable Agriculture, Lancaster Environment Centre, Lancaster University, UK

k Center for Plant Water-Use and Nutrition Regulation and College of Life Sciences, Joint International Research Laboratory of Water and Nutrient in Crops, Fujian Agriculture and Forestry University, Fuzhou, China

l Institute for Land and Water Management Research, Federal Agency for Water Management, Petzenkirchen, Austria

m College of Soil and Water Conservation, Beijing Forestry University, Beijing, China

n College of Economics and Management, Nanjing Agricultural University, NAU, Nanjing, China



This article outlines the major scientific objectives of the SHui project that seeks to optimize soil and water use in agricultural systems in the EU and China, by considering major current scientific challenges in this area. SHui (for Soil Hydrology research platform underpinning innovation to manage water scarcity in European and Chinese cropping systems) is large cooperative project that aims to provide significant advances through transdisciplinary research at multiple scales (plot, field, catchment and region). This paper explains our research platform of long-term experiments established at plot scale, approaches taken to integrate crop and hydrological models at field scale; coupled crop models and satellite-based observations at regional scales; decision support systems for specific farming situations; and the integration of these technologies to provide policy recommendations through socio-economic analysis of the impact of soil and water saving technologies. It also outlines the training of stakeholders to develop a basic common curriculum despite the subject being distributed across different disciplines and professions. As such, this article provides a review of major challenges for improving soil and water use in EU and China as well as information about the potential to access information made available by SHui, and to allow others to engage with the project.


Water 201911(11), 2245;

Partner Publication (CSIC):

José M. Mirás-Avalos 1,,José S. Rubio-Asensio 1,Juan M. Ramírez-Cuesta 1,José F. Maestre-Valero 2 and Diego S. Intrigliolo 1,3
1 Centro de Edafología y Biología Aplicada del Segura (CEBAS), Consejo Superior de Investigaciones Científicas (CSIC), Espinardo, 30100 Murcia, Spain
2 Escuela Técnica Superior de Ingeniería Agronómica, Universidad Politécnica de Cartagena, Paseo Alfonso XIII 48, 30203 Cartagena, Spain
3 Instituto Valenciano de Investigaciones Agrarias (IVIA), Centro Desarrollo Agricultura Sostenible (CEDAS), Unidad asociada al CSIC “Riego en la agricultura mediterránea”, Apartado Oficial, 46113 Moncada, Valencia, Spain
Climate change will intensify water scarcity, and therefore irrigation must be adapted to save water. Operational tools that provide watering recommendations to end-users are needed. This work presents a new tool, Irrigation-Advisor (IA), which is based on weather forecasts and is able to separately determine soil evaporation and crop transpiration, and thus is adaptable to a broad range of agricultural situations. By calculating several statistical indicators, IA was tested against the FAO-56 crop evapotranspiration (ETcFAO) methodology using local crop coefficients. Additionally, IA recommendations were compared with current standard practices by experienced farmers (F). Six field experiments with four widely cultivated species (endive, lettuce, muskmelon and potato) were performed in Southeast Spain. Irrigation water applied, crop yield, aboveground biomass and water productivity were determined. Crop water needs underestimations (5%–20%) were detected when comparing IA against ETcFAO, although the index of agreement proved reasonable adjustments. The IA recommendations led to water savings up to 13% when compared to F, except for lettuce, with a 31% surplus in irrigation when using IA. Crop yield was not compromised and water productivity was increased by IA. Therefore, IA mimicked the farmers′ irrigation strategies fairly well without deploying sensors on-site. Nevertheless, improvements are needed for increasing the accuracy of IA estimations.

Water 201911(9), 1918;

Partner Publication (University of Cordoba):
Margarita Garcia-Vila 1,Rodrigo Morillo-Velarde 2 and Elias Fereres 1,3
1 Agronomy Department, University of Cordoba, 14007 Córdoba, Spain
2 Research Association for Sugar Beet Crop Improvement, 47012 Valladolid, Spain
3 Institute for Sustainable Agriculture, CSIC, 14004 Córdoba, Spain


Process-based crop models such as AquaCrop are useful for a variety of applications but must be accurately calibrated and validated. Sugar beet is an important crop that is grown in regions under water scarcity. The discrepancies and uncertainty in past published calibrations, together with important modifications in the program, deemed it necessary to conduct a study aimed at the calibration of AquaCrop (version 6.1) using the results of a single deficit irrigation experiment. The model was validated with additional data from eight farms differing in location, years, varieties, sowing dates, and irrigation. The overall performance of AquaCrop for simulating canopy cover, biomass, and final yield was accurate (RMSE = 11.39%, 2.10 t ha−1, and 0.85 t ha−1, respectively). Once the model was properly calibrated and validated, a scenario analysis was carried out to assess the crop response in terms of yield and water productivity to different irrigation water allocations in the two main production areas of sugar beet in Spain (spring and autumn sowing). The results highlighted the potential of the model by showing the important impact of irrigation water allocation and sowing time on sugar beet production and its irrigation water productivity.

Partner Publication (ARO):

Ohana-Levi, N., Bahat, I., Peeters, A., Stein, A., Cohen, Y., Nezer Y., Ben-Gal, A. (2019)

Management of agricultural fields according to spatial and temporal variability is an important aspect of precision agriculture. Precision management relies on division of a field into areas with homogeneous characteristics, management zones (MZs), which are likely affected by multiple, interrelated factors.


We present a method, based on machine learning and spatial statistics, to analyze the spatial relationship between a set of variables and determine management zones in a vineyard. The method involves:

  1. Fitting a model that quantifies the relationship between multiple variables and yield;
  2. Fitting a model that quantifies the effect of the spatial variability of multiple variables on yield spatial characteristics;
  3. Developing a weighted multivariate spatial clustering model as a method to determine MZs.

Twelve variables were sampled for 3893 vines in the wine grape vineyard. These variables included soil properties, terrain characteristics, and environmental impact, as well as crop-condition, using indices calculated from remote sensing images. The predictor variables were spatially characterized using hot-spot analysis (Getis Ord Gi* Z-score values) to assess their spatial variability. A gradient boosted regression trees (BRT) algorithm was used to analyze the spatial multivariable effect on yield spatial characteristics. MZs were determined using multivariate K-means clustering, with relative weights given to the predictors, based on their relative influence on yield spatial variability provided by the BRT model.

This method was compared to ordinary K-means clustering and K-means with spatial representation of the variables without weights using a dissimilarity index and spatial autocorrelation measures. Model performance was found to be very high and demonstrated that among the evaluated predictors, crop condition indices were the most important regressors for yield and its spatial characteristics. The weighted multivariate spatial clustering was found to perform better in terms of separability of the points and their spatial distribution than the other two clustering techniques. Quantifying yield and its within-field spatial variability, ranking the effects of the predictors and their spatial variabilities, and segmentation of MZs through multivariable spatial analysis, are expected to benefit irrigation management and agricultural decision-making processes.