Managing water scarcity in European and Chinese cropping systems

July 15-19 at BOKU, University of Natural Resources and Life Sciences, Vienna, Austria

The University of Natural Resources & Life Sciences (BOKU) is organizing the 2019 SWAT International Conference on 15-19 July, 2019 in collaboration with the USDA-ARS and Texas AgriLife Research. The conference has become the most important scientific gathering for international experts and institutions in the field of river basin management.

Click here for information and registration

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.

Minutes of Shui meeting

Nanjing 18 October 2018 01:48


Guangju Zhao Institute of Soil and Water, CAS, Shanxi
Lixin Chen, Beijing Forestry University, Beijing
Jiao Chen, Nanjing Agriculture University, Nanjing
Roujing Li, Beijing Normal University, Beijing
Rui Miao, Fujian A&F University, Fujian
Weifeng Xu. Fuzhou Agriculture and Forestry University (China PI)
Funing Zhong, Nanjing Agricultural University
Jose Gomez, CSIC, Spain (EU PI)
Andreas Klik, Vienna Agricultural University (BOKU), Austria
Tomas Dostal, Prague Technical University, Czech Republic
John Quinton, Lancaster University, UK.

Chinese project progress

Approved, waiting for signature
Smaller project (£500K) so have had to consolidate to make it work
Start Jan 2019 for three years

  • WP1 -> group 1
  • WP2 +3 -> group 2
  • WP4 +7 ->group 3
  • WP5+6 -> group 4

Action: Chinese partners will identify leaders and inform Jose, and provide an English version of Chinese project plan and key slides. Deadline end of October

Consortium agreement

Has to be signed by all of the partners. Normally a high level administrator in the University.
Chinese partners can only sign after they start the project in January.

  • Action: Jose to provide CSIC lawyer with contacts of Chinese leaders and circulate the CA document. Nov 1st
  • Action: Chinese partners to provide Institutional administration contact to Jose. 30th Nov
  • Action: Jose provide contacts of Chinese PI in previous EU – China project to provide advice. 1st Nov
  • Action: Chinese partners to comment on the consortium agreement. End of Nov
  • Action: Once agreed by all EU and Chinese partners the University officials can sign the final version. End of Jan

Web site

Needs to be in Chinese and English End of Nov
Check existing websites in collaboration programme End of Oct
Chinese partner: Beijing Forestry University EU – Lancster
WeChat group needs to be established and WeChat feeds to website
Directory of expertise, corresponding person for particular areas

Close list of agricultural systems

A full list of Chinese demonstration fields will be developed at the at the Chinese kick-off meeting.

  • Action: Jose to send list of European cropping systems to Weifeng Xu and leaders of WP5 (socioeconomic) End of Oct
  • Action: Chinese partners to supply a list of cropping systems of interest to project coordinators (Jose, Weifeng) and leaders of WP5 End of Nov

Action: Chinese partners to provide a list of metadata about the data that is available, Guangju Zhao to co-ordinate and send to Andreas Klik Name 1st Nov- List Mid of Nov


Likely that Chinese budget will be cut by 10%.
Final budget clear by end of the year.

Student exchange

Action: John to talk to Kevin Jones/David Tyfield

Date and place for next meeting

Kick of meeting in China. March/Early April in Fuzhou city in Fujian Province. Last week in March first week in April

The SHui (soil hydrology that underpins innovation) project has started its journey from the Rectorate of the University of Cordoba.

During the past days, 24, 25 and 26 of September, the kick-off meeting of the SHui project was held. This project is part of the European Commission’s H2020 program, with a horizon of four years from its launch. The project will be coordinate from IAS-CSIC (Institute for Sustainable Agriculture) in the framework of their investigations.
Throughout the three days, the participants have presented and discussed the planned work packages, along with a field visit and working groups in which plan in detail the project actions for the next 12 months.
SHui is conceived as a network integrating long-term experiments of its 19 academic and SME partners to optimize productivity and environmental sustainability across different environmental conditions and cropping systems in the EU and China.

This project will develop and implement new strategies to increase water use efficiency and yield, based on sustainable intensification through integrated use of soil and water across different spatial scales. These technical approaches are reliant on optimum data utilization and transdisciplinary research among project partners and multiple stakeholders.
SHui will exploit scientific, technological and social innovations by disseminating and communicating these to multiple stakeholders, and implementing novel technological packages from farm to large regional scales.