Managing water scarcity in European and Chinese cropping systems

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.