O R I G I N A L A R T I C L E Open Access
Geospatial binding for transdisciplinary
research in crop science: the GRASPgfs
Didier G. Leibovici
, Suchith Anand
, Roberto Santos
, Sean Mayes
, Rumiana Ray
, Masoud Al-Azri
, Graham King
, Asha S. Karunaratne
, Sayed Azam-Ali
and Mike J. Jackson
The paper retraces the GRASPgfs endeavor (Geospatial Resource for Agricultural Species and Pests with integrated
workflow modelling to support Global Food Security) between multiple disciplines around a common objective of
facilitating research and model simulations for sustainable food security. Within this endeavor, the geospatial media
has been the enabler for multidisciplinary research in crop modelling. Geospatial genetic-trait variations and
associations with environmental forecasting were the main focus of the GRASPgfs. Designing the platform
achieving this objective generated a transdisciplinary vision of modelling and forecasting for sustainable agriculture.
Based on interoperability principles, seamless access as well as sharing for data, metadata and processing models,
the design is described in this paper. This geospatial binding facilitates and supports new types of hypotheses and
analysis as illustrated in the paper with a landscape genetic case study (bambara groundnut) and a crop disease
modelling (eyespot disease). The approach and the eGRASP platform are generic enough to accommodate further
complexity into the integrated modelling that this geospatial binding enables.
Keywords: Agriculture models, Crop modelling, Scientific workflow, Geospatial information, Genetic-trait,
Interoperability, GIS, OGC, Web services
A FAO report of the Commission on Genetic Resources
for Food and Agriculture (CGRFA 13/11) clearly identi-
fied “spatial analysis to identify varieties likely to have
climate-adapted traits as an aid to plant breeding”as
one of the eight priorities in multidisciplinary research.
In addition, Earth Observation data (EO) has proven the
capacity to provide measurements of key environmental
conditions to predict the production of the healthy crops
and potential disease threats.
Such agricultural modelling and simulations need ac-
cess to elaborated geolocated genetic-trait information
as well as complementary data sources coming from
geospatial data providers and geospatial data hubs,e.g.
soil moisture data, climate data. The BBSRC funded pro-
ject GRASPgfs “Geospatial Resource for Agricultural
Species, pests and Pathogens with workflow integrated
modelling to support Global Food Security”initiated the
design and prototype of an e-infrastructure linking to-
gether (i) a geo-germplasm database, (ii) a dynamic
metadata catalog and (iii) a workflow modelling tool to
enable optimal usage of the geo-genetic-trait informa-
tion. This is desired in various types of simulations for
crop trait variation, forecasts under climate forcing sce-
narios and crop disease epidemics.
This initiative and the prototype of the e-
infrastructure based on open standards is described.
Access to a wide range of information, from rigorous
scientific results to ‘hear-say’farmers’knowledge is be-
coming critical to be able to target efforts in food secur-
ity planning at community or at country levels. Also,
designing scientific and intervention strategies within
changing climates and markets is a fundamental
* Correspondence: Didier.Leibovici@nottingham.ac.uk;
Nottingham Geospatial Institute, University of Nottingham, Nottingham, UK
Full list of author information is available at the end of the article
Open Geospatial Data,
Software and Standards
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.
Leibovici et al. Open Geospatial Data, Software and Standards (2017) 2:20
challenge. The Plant Genetic Resources for Food and
Agriculture part of the FAO’s Global Plan of Action for
plant genetic resources has been established as a world-
wide dynamic mechanism using WIEWS
to foster infor-
mation exchange among members (more than 150
countries) and as an instrument for periodic assess-
The enhanced use of this type of resource and
other germplasm banks with additional contextual infor-
mation is nonetheless still highly desired. In a recent
from the Commission on Genetic Re-
sources for Food and Agriculture clearly identified
“spatial analysis to identify varieties likely to have
climate-adapted traits as an aid to plant breeding”as
one of the eight priorities in multidisciplinary research.
Technologies for the collection and dissemination of
geolocated information, using broad-band mobile com-
munications, sensor platforms, spatial search and perva-
sive computing, are fundamentally changing the access
to and use of location-based data in agriculture .
However, the necessary cross-disciplinary research
needed to transform raw data and information into use-
ful intelligence and knowledge to improve the planet’s
environmental, economic and societal well-being is still
constrained by disciplinary and organizational silos and
legacy concepts. Even if this was already acknowledged
in the 1940’s, the geo-location of genetic data in ecology
and agriculture for further spatial analysis: spatial gen-
etic, is still a recent concept . Within the “from farm
to fork”chain, various heterogeneous data including
genetic-trait information are to be considered as part of
the computational modelling for prevision and forecast;
most of them have a geo-location or spatial component
or would be required to have one to be used from a
plethora of model applications of various complexities.
These are either biophysical, agro-economically based
and more mechanistic or deterministic orientated [6, 13,
14, 19, 30, 36] or more stochastic orientated [2, 5, 12,
23, 42, 48, 52], more rule-based including agent-based
orientated [32, 45, 49] but all contain a combination of
those types. Therefore, a cross-disciplinary expertise
driven from geospatial sciences methodologies appeared
to be needed to develop an integrating framework for
relevant data sources, in order to allow knowledge gath-
ering across all subjects relevant to Food Security.
The objective of the geospatial integration e-
infrastructure framework that GRASPgfs initiative has
proposed to establish is to facilitate the use and reuse of
trait data in crop, animal and microbial species of agri-
cultural importance. The initiative relies on the position
that the geospatial realm, as an entry point and end-
point of this e-infrastructure, enables researchers but
also stakeholders and policy makers to ground their de-
velopment strategies but also to elaborate more easily al-
ternatives [3, 6, 26, 30, 31, 36, 40]. Not only this binding
is conceptual and interdisciplinary but also it has conrete
technical impacts on the e-infrastructure utilizing Open
Geosptial Consortium (OGC) standards. Linked with dy-
namic climate records within the framework capabilities
on scientific workflow modelling, this would allow ad-
dressing food security issues for sustainable agriculture
by enabling predictive modelling with identification and
characterization of new sources of germplasm.
The paper describes the initial overall architecture de-
sign and first results on establishing the eGRASP plat-
form and e-infrastructure. Section 2 concentrates on
expressing the method in defining the approach and ini-
tial challenges for long-term objectives; section 3 high-
lights the bases in designing the eGRASP solution and
architecture along with illustrative examples of initial re-
sults on using such approach.
Targeting global food security issues and sustainable
agriculture, related to crop selection and climate change
needs the development of models integrating a range of
disciplines such as genetic, agro-ecological modelling
and land-climate forecasts. Geospatial science can be the
mediating component of an e-infrastructure enabling
data and processing to be retrieved, integrated and made
available within a geospatial scientific workflow model-
ling interface with uncertainty management.
The main objective of the geospatial integration frame-
work wihin GRASPgfs was to facilitate the use and reuse
of known (and new) sources of crop traits together with
dynamic climate records within the framework capabil-
ities of workflow modelling addressing food security is-
sues concerning sustainable agriculture. Describing
agricultural species germplasm for genotype characteris-
tics with the data ordered by geospatial origin, the
higher-level descriptor being “agricultural trait”has been
put forward as enabling new way of expressing and ana-
lysing trait variations [34, 41].
Highlighting model complexity for integrated assess-
ments required for global food security, a recent review
of crop models under climate forcing pointed out the
need of generic solution enabling or facilitating the com-
bination of various models together . Figure 1 encap-
sulates the challenges of facilitating the elaboration of
such analysis via an integrated workflow modelling. This
workflow grasping the “big picture”and illustrating the
cross-disciplinary expertise required is conceptual and
each data entry or processing task may in fact illustrate
the use of complex data structures and sub-workflows
themselves. The framework to develop would need to fa-
cilitate the integration of the driving key conceptual as-
pects of this model. Following this conceptual approach
and being able to re-use data and models available to in-
stantiate such model would enable new perspectives on
Leibovici et al. Open Geospatial Data, Software and Standards (2017) 2:20 Page 2 of 11
crop genetic diversity by (i) identifying new sources of
trait variation, (ii) geolocating suitable germplasm, (iii)
planning breeding objectives with the greatest likely im-
pact from the added information of local market and
farmer’s knowledge [8, 34], and (iv) evaluating the effects
of climate change scenario.
Integrating heterogeneous datasets coming from vari-
ous sources within a generic platform means being able
to access and understand the semantics of these data
and processes in order to allow the platform to present
the data, analyse them, or instantiate a workflow model
using them [9, 25]. Easily integrating various data and
processing resources has considerable advantages in
terms of rapid development of models and their execu-
tion but gives less control on the quality of the results as
various uncertainties coexisting in the components of
the workflow model. Therefore, bounding with uncer-
tainty assessment the outcomes of the models should
also be the aim of the eGRASP platform to allow better
decision-making. Specific capacities are also needed to
integrate information such as genetic-trait encoding and
ontology binding with disparate germplasm data sources.
Pests, pathogens and weeds are encompassed at the crop
information level and in the process themselves as they
are often impacting from interacting with the environ-
Designing, developping and implementing
The GRASPgfs has therefore focused on designing and
implementing a flexible, interoperable platform based on
open source software
compliant with GEOSS
standards and services for data and processing
capabilities. From delivering a flexible, integrative and
sharing eGRASP web platform based on openess, the ob-
jectives of enabling researchers in crop modelling, agro-
ecological modelling either as developer of new models
or evaluating agriculture strategies (agro-ecomic model-
ling), to seamlessly re-use existing models and specific
data such genetic-trait information will be achieved. For
efficiency and controls on the quality in terms of uncer-
tainty and variability of the outcomes, the design of the
platform allowed functionalities to easily browse and
visualise metadata as well as has to geo-computationally
evaluate workflows output uncertainties [15, 25, 28].
Spatial analysis of the spatial variations either of the pre-
dicted outcomes and their uncertainties were included
in the design to be part of the platform as well. That
way the modelling part and of the decision making part
are interlinked, allowing more flexibility and adaptability.
The approach and the concept of the eGRASP platform
has been the result of multidisciplinary exchanges lead-
ing to a real transdisciplinary vision [4, 21, 38] that is
highlighted in the next section.
Fig. 1 Workflow design of a generic model to be used in food security and sustainability: the Genetic Agro-ecological Sustainability Proposal
model (BPMN diagram)
Leibovici et al. Open Geospatial Data, Software and Standards (2017) 2:20 Page 3 of 11
Emergence of a transdisciplinary vision
Whilst building up a core collaboration on this topic
from a range of disciplines (within environmental and
human geography, crop science, geospatial information,
and computing science) at the University of Nottingham
by meeting regularly and having small funding for a few
summer internships in 2010, the common vision
expressed in Fig. 1 started to emerge. Later on, thanks
to a 18 months pump prime funding from the BBSRC
the research work could start. The workflow of Fig. 1 en-
capsulates the vision put into the design of the eGRASP
platform as much as it is a template of potential model-
ling scenarios envisioning the various components as
data and processes needed to consider fulfilling our ob-
jectives for GRASPgfs. If at first it may have seemed that
the geospatial sciences brought tools enabling this re-
search within a cross-disciplinary perspective, it trans-
formed rapidly into acting as a media of a more holistic
integrated approach , which then expressed itself in
challenging its specific developments within a context
beyond the disciplines involved. In addition to providing
more opportunities for expanding the capabilities and
applications looked for in the first place, this
advancement also created new avenues for interdiscip-
linary research and practices in the use of GIS in agricul-
Beyond the global concept and concepts encapsulated
in it, Fig. 1 is a truly transverse vision that not only put
each specialist of a sub-model within a contextual flow
but also enriches the geospatial e-infrastructure model-
ling framework. This resulted from various flow dia-
grams of conceptual information into a technical and
standardised representation using a cross-disciplinary
encoding standard, the BPMN standard (Business
Process Modelling Notation from the OMG standard or-
ganisation). As far as the cross-disciplinary concerns,
Fig. 1 as a BPMN representation is also a scientific geo-
computational model seen from a meta-level description
that can be linked to a workflow engine enabling its
computational execution once instantiated (Fig. 2).
In order to instantiate such models (entire Fig. 1 or
sub-models encapsulated) the design of the eGRASP
platform is based on the Use Case model in Fig. 2, which
translates the requirements exposed earlier. In this figure
only general use cases are presented with different col-
ours to express the different domains or disciplines
Fig. 2 Use case model (UML) for the GRASPgfs platform
Leibovici et al. Open Geospatial Data, Software and Standards (2017) 2:20 Page 4 of 11
concerned: the green use cases reflect the crop genetic
with genetic-trait information aspects, the yellow use
cases concern geospatial science with visualisation and
selection of environmental constraints, the bleu use
cases are to do with geocomputational modelling and
scientific workflow composition and evaluation, and the
pink use cases concerns crop epidemiology with the risk
factors associate with the crop modelling including pests
and disease risks from pathogens information.
(Unified Modelling Language) particu-
larly using class diagrams for object modelling and
use case diagrams such as in Fig. 2, has been enab-
ling cross-disciplinary exchanges from data modelling
, the BPMN language establishes a bridge between
the conceptual integrated modelling towards the ef-
fective execution of the models . Facilitating the
composition of such workflows using existing re-
sources is paramount .
Crop modelling complexity
Well-known crop modelling approaches such as APSIM
are considered here as expressing or
being a sub|-model of the “trait variation forecast inte-
gration”.The purpose of the GRASPgfs is to re-use
directly these established models within a flexible plat-
form; they can be wrapped into OGC web processing
services (WPS) and made available for the platform as
such [10, 35] or via a brokering system [7, 39]. When
the models can be broken down into sub-components, if
required by the crop-trait variation scenario, this can be
made available to the processing service. When possible
the interaction of these models can be complex to set up
and to combine, the BPMN editor is seen as a simplifica-
tion, particularly when a few models are to be combined.
Ultimately it brings interoperability in interfacing het-
erogeneous data and processing models that do not ne-
cessarily impose standardisation for each of them. This
does not preclude of course a good understanding of the
models used, but the goal of the eGRASP platform is to
hide this complexity and to focus on the ability to re-use
the resources within a more macro scenario for global
food security. Models and types of models identified in
introduction can be potentially re-used here and the
platform objectives are also to facilitate their encapsula-
tion as WPS services (Fig. 3).
When looking at trait variation with genotypic infor-
mation, the crop modelling may start with building up a
selection for trait-variation linked to genotype linkage
Fig. 3 The GeoGermPlasmDB schema, an evolution from CropstoreDB
Leibovici et al. Open Geospatial Data, Software and Standards (2017) 2:20 Page 5 of 11
and environment interaction. This corresponds to the
“Trait Hypothesis Construction”process task in the gen-
eric workflow. To this end, it is described in Fig. 2
among the other capabilities of the eGRASP platform;
the functionalities associated to this genetic-trait selec-
tion, before performing the crop modeling for example,
are the green part of the use case model. To achieve this
aspect the platform is reusing the CropStoreDB
base, called GeoGermplasmDB in the architecture de-
sign (Fig. 4). The GeoGermplasmDB has an extended
schema in order to record the geometry associated to
few tables using the OGC standard (Fig. 3) and also to
be able to encode the pest and pathogens characteristics
along with model parameters associated to the crop var-
ieties as stipulated in the requirements. The Geogerm-
plasmDB allows users to record genotype information
and trait information with geo-location depending on
the origins of the seeds and the trial sites and imple-
ments the component “Bio-genetic Knowledge”compo-
nent of the platform. Geospatial variations associated
with genetic variations can lead to breed selection
programs [18, 33]. An example using the underutilized
crop of the Bambara groundnut (Vigna subterranea)is
detailed in the example section (Fig. 5).
The other aspects of complexity considered here
come on one hand from the interaction of farmer’s
knowledge with respect to the land races linked to
their strategies to make a living [24, 32] and on the
other hand to the climate forcing interacting with the
current land conditions. Due mostly to aggregation
and topological properties when modelling these
models, the spatial complexity can now be also intro-
duced [26, 47, 51]. Specific models for climate forcing
more often mechanistic can be used to predict future
ground conditions but are usually integrated with
interaction from general land use categories [43, 50].
The eGRASP capacity
The approach pursued in GRASPgfs and for the design
of the eGRASP platform has been as much top-down as
bottom-up from leading disciplines such crop genetics,
geospatial information modelling and crop modelling.
Fig. 4 The eGRASP platform architecture design as sub-module of the CropBASE system
Leibovici et al. Open Geospatial Data, Software and Standards (2017) 2:20 Page 6 of 11
Basically besides strong top-down emphasis on geolo-
cated genetic-trait database (the GeogermplasmDB), and
on a workflow modelling (based on OGC WPS and
BPMN standard), case studies analysis were used to
gather requirements. Mixing these two aspects as well as
envisaging direct use of the top-down elements into the
bottom-up approach, the UML use case diagram of the
required functionalities of the eGRASP platform was ob-
tained (Fig. 2). From the adoption of the use case dia-
gram, disciplinary research took place to refine the case
studies with focus on use case matching and potential
new developments whilst the computing architecture
was design to fit these requirements.
The architecture designed for the eGRASP platform to
enable global spatial data infrastructure functionalities, as
well as the ones described above, is given in Fig. 4. This
viewpoint gives an overview of the different components
without detailing on how specific analytical functionalities
are implemented. The objective for this pump-prime
funding was to establish the design and to demonstrate a
prototype. Therefore, specific functionalities are still to be
developed; further funding is required to pursue these ef-
forts. In Fig. 4, front-end services with their clients are
represented as square boxes and back-end services often
associated with specific information (e.g.,databases, re-
positories) are represented as cylindrical boxes. The
eGRASP system appears in this design as sub-architecture
of the CropBASE
initiative led by CFF (Crops For the
Future), a wiki-knowledge sharing platform integrating
multiple CFF programs also in development.
For the sake of demonstrating the architecture the set
of services implemented and facilities currently avail-
but the platform as well as the CropBASE portal
are not yet operational. The OGC services, for example
using WPS and WFS, can also be used directly in other
clients such as in QGIS (from the OSGeo
the Geovisualisation is supported from QGIS and
from the WMS client provided from the Geoserver
serving the GeogermplasmDB
the Discovery via Metadata Catalogue service (OGC
CSW) is supported by GeoNetwork
, queries on
GEOSS registered catalogue can brings re-usable re-
sources (data or processing services) as well a s local
the GeoWorkflow is supported by a bespoke
specification for OGC services using the jBPM
suite with a web editor and a workflow engine .
the GeoGermplasmDB services as well as local
environmental data are served using GeoServer
the results of the simulations or other workflows
can be stored in the local environmental data
a set of ontologies can be used to enrich the data
and processes enabling refined queries via the
metadata catalogue client.
Quality information available for data and processes in
the metadata catalogue are used for uncertainty
Fig. 5 A landscape genetic analysis workflow on bambara groundnut (Vigna subterranea) landraces
Leibovici et al. Open Geospatial Data, Software and Standards (2017) 2:20 Page 7 of 11
assessmet from the error propagation, by then allowing
better decision-making. This is currently available as
added functionality from the web editor from re-using
the MetaPUnT WPS
service [27, 28] and allowing to
meta-propagate the uncertainties.
Two illustrative examples are presented here to highlight
the potential of the eGRASP. The first one, a landscape
gentic modelling, uses directly the GeoGermplasmDB
and WFS associated to describe spatially genetic dis-
tances of germplasms. The second one illustrates the
crop disease modelling of the eGRASP facility by design-
ing an examplar wheat eyespot disease model . Both
examples, the landscape genetic association analysis and
the crop disease modelling are using a BPMN scientific
workflow representation, by then demonstrating the
range of modelling situations that eGRASP is intending
For the landscape genetic modelling, a glasshouse trial
with 128 plants from 4 repetitions each of 32 landraces
was analysed (Figs. 5 and 6). Here only the genotypic in-
formation was used to retrace geo-location associations
of similar genetic profiles based on 20 microsatellites
molecular markers (SSR) [37, 46]. Five genetic profiles
were identified from k-means on main principal compo-
nents of the SSR response data. In Fig. 6, the green and
red profiles capturing most of the genetic variability are
relatively clustering spatially with an East-west gradient
in the Sahel for the reds and a North-south gradient in
the East and South-East Africa for the greens. Adapta-
tions to similar climatic environment can be though as
explaining these zones with the Sahel zone for the reds
and a more humid tropical zone in the East-Africa for
the greens. Trade routes can be also involved. Further
analysis including the phonologic data with comparison
to local data will be needed to confirm these sorts of
Each task of the workflow in Fig. 5 was performed
from R scripts based on existing packages. These R
scripts are in the process of being encapsulated as WPS
in order to be used and shared from the eGRASP
The second example illustrated in Fig. 7 is a scientific
workflow for crop modelling with potential occurrence
of the eyespot disease. The purpose was to integrate spe-
cific epidemiological disease modelling within a normal
growth simulation model. The Eyespot disease is mod-
elled using few sub-models interfering with the normal
development of the crop:
The inoculation potential model (IPM) determines
the amount of inoculum available for infection of
the host depending on land condition risks and
The disease development model (DDM) based on
the inoculation level and key environmental factors
related toinfection and disease developement.
Finally at a key developmental growth stage the
severity of the disease is determined (DSM) and is
based on estimates from the previous two models.
The impact of the severity of disease is then
evaluated iteratively (HRM) at the subsequent
growth stages until the crop has been harvested.
-4 -2 0 2 4 6
Fig. 6 Bambara groundnut (Vigna subterranea) landrace origins classified by genetic distance (bottom: first two principal components and kmeans
classes, top: geo-locations of the sample)
Leibovici et al. Open Geospatial Data, Software and Standards (2017) 2:20 Page 8 of 11
Each one of the models: IPM, DDM, DSM and HRM
are stochastic models and estimated at the given growth
stages that were identified as crucial during the develop-
ment of the crop on controlled data: GS13, GS32, GS39
and GS65 . The models are to be combined with
physiological based model for crop growth as in the
BPMN representation in Fig. 7. The disease evolution
models have been implemented in R
was chosen as crop growth model. Within APSIM
and using the script manager, R scripts can be ran,
making APSIM the orchestrating engine. Nonehteless,
encapsulating APSIM within a WPS could be a future
solution using the workflow engine wihtin eGRASP.
Details of first results and variables involved in the
IPM, DDM, DSM and HRM model can be seen in 
as well as the full validation of the models. Nonethe-
less, despite the capacity of APSIM to run R scripts,
the targetted variables by the disease modelling
couldn’t be updated during simulations which led to a
much simpler adaptation of Fig. 7.
For the eGRASP the interest lies in the fact that such
composition and conceptualisation of the models can be
facilitated and controlled, e.g. looking for model ad-
equacy. The interoperability ensures that the models
designed according to the BPMN standard can be then
shared using a standard graphical representation for
better communication but also as XML encoding enab-
ling any workflow engine to run the scientific model
represented as a workflow.
Like UML (Unified Modelling Language) used as a
computing science tool to design of application systems,
leading both to databases and object programing
implementations, the meta-language of the BPMN can
be very rapidly understood from the scientists involved
[22, 29]. This transdisciplinary process enabled to
conceptualise the disease evolution and impact in a
comprehensive way that has been also efficient to put in
practice once each sub-model (tasks in the BPMN
diagram) has been established and fitted.
Interdisciplinary projects often reduce to cross-
disciplinary spill over; however over a shared building
up initiative to advance on the GRASPgfs concept, a
real transdisciplinary collaboration has been initiated
and experienced. Not only the co-design of the
eGRASP platform with its embryo of capacities has
enabled to envisage new potential research ideas in
each of our disciplines, it also concretised global food
security strategies and analyses. The recent develop-
ment of the GRASPgfs framework along with the
design of the eGRASP was limited as due to the
budget and not all the disciplines firstly envisaged
could be adequately integrated. Whilst in Fig. 1, the
agro-ecologial interaction would derives mostly from
re-using models in landscape genetic and landscape
ecology, as well as the agro-economic would benefit
from models mentioned in the background section,
their data modelling integration represented on the
left hand side of the model has not been yet investi-
gated. For a prototype design this was not crucial as
long as we could still represent its future influence
when composing the models.
Fig. 7 Eyespot disease workflow modelling using APSIM for wheat crop growth simulation
Leibovici et al. Open Geospatial Data, Software and Standards (2017) 2:20 Page 9 of 11
If some of the services in Fig. 4 are in place the actual
data and processes content is rather small as this was a
proof of concept exercise. Nonetheless PhD students
and recents projects are providing valuable examples
also enhancing the capacity of this platform. The
interoperability principle adopted by the eGRASP,
including its open source and open standard focus, is
the chance for maximum dissemination of this capacity
as a set of cross-platform clients and services. Geospatial
risk assessments in agriculture in relation to species and
pests, can be greatly facilitated from sharing data and
processes which can then reused by the eGRASP.
APSIM: Agricultural Production Systems Simulator; BPMN: Business Process
Modelling Notation; CFF: Crop For the Future; CSW: Catalog Service Web;
DDM: Disease Development Model; DSM: Disease Severity Model; eGRASP:
e-infrastructure for GRASP; FAO: Food and Agriculture Organisation;
GEOSS: Group on Earth Observation System of Systems; GIS: Geographical
Information Science; GRASP: Geospatial Resource for Agricultural Species,
pests and Pathogens; GRASPgfs: GRASP with integrated workflow modelling
to support global food security; GS13: Growth Stage 13; HRM: Harvest
Reduction Model; IPM: Inoculation Potential Model; JBPM: JBoss Business
Process Modelling suite (Java); OGC: Open Geospatial Consortium;
OMG: Object Modelling Group; OSGeo: Open Source Geo; SSR: Simple
sequence repeats; UML: Unifed modelling language; WPS: Web processing
service; XML: eXtended Markup Language
All the co-authors are very grateful to the support and scientific interaction
we had along this initiative with a number of colleagues: Jeremy Morley
now chief geospatial scientist at Ordnance Survey, UK, Sue Walker emeritus
professor in agro-meteorology at the university of the Free State, South
Africa, Charlie Hodgman, honorary professor at the university of Nottingham,
Tim Brailsford, associate professor in computing science at the university of
Nottingham., Thuy Vu, associate professor in geography, at the university
of Nottingham, Malaysian campus and Razlin Azman,now working with
Pr Graham King.
Most of this work has been possible thanks to the funding from the British
Biological Sciences Research Council (BBSRC) grant n° BB/K004034/1 for the
project “Geospatial Resource for Agricultural Species, pests and Pathogens
with integrated workflow modelling to support Global Food Security
(GRASPgfs): a prototype”.
DL and SA have been leading scientifically the project. DL finalised the
design of the eGRASP and wrote the paper, both based on contributions
from all. Particularly, SM, GK AB, RR, RS, AK provided work, expertise and
information on the database genetic materials. Illustrative examples were
from RS and MA-A whilst MJ and SA-A provided valuable discussions. All
authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Nottingham Geospatial Institute, University of Nottingham, Nottingham, UK.
Plant and Crop Sciences, University of Nottingham, Nottingham, UK.
Sciences, Southern Cross University, Lismore, Australia.
Faculty of Agricultural
Sciences, Sabaragamuwa University, Belihuloya, Sri Lanka.
Crop For the
Future (CFF), Semenyih, Malaysia.
Global Open Data for Agriculture and
Nutrition (GODAN) http://www.godan.info.
Received: 27 January 2017 Accepted: 5 July 2017
1. Al-Azri M, Leibovici DG, Karunaratne A, Meek S, Ray RV. Simulating eyespot
disease development and yield loss using APSIM for UK wheat. In:
Agriculture and climate change, adapting crops to increased uncertainty,
Amsterdam, The Netherlands, 15–17 February 2015; 2015.
2. Angulo C, Rötter R, Trnka M, Pirttioja N, et al. Characteristic ‘fingerprints’of
crop model responses to weather input data at different spatial resolutions.
Eur J Agron. 2013;49:104–14.
3. Azam-Ali SN, Aguilar-Manjarrez J, Bannayan M. A global mapping system for
bambara groundnut. In: FAO, agricultural information and knowledge
management papers, vol. 1; 2001. p. 55.
4. Azam-Ali SN. Fitting underutilised crops within research-poor environments:
lessons and approaches. S Afr J Plant Soil. 2010;27(4):293–8.
5. Basso B, Ritchie JT, Pierce FJ, Braga RP, et al. Spatial validation of crop
models for precision agriculture. Agric Syst. 2001;68(2):97–112. doi:10.1016/
6. Batchelor WD, Basso B, Paz JO. Examples of strategies to analyze spatial
and temporal yield variability using crop models. Eur J Agron. 2002;
7. Bigagli L, Santoro M, Mazzetti P, Nativi S. Architecture of a process broker
for interoperable geospatial modeling on the web. ISPRS Int J Geo Inf.
8. Birch ANE, Begg GS, Squire GR. How agro-ecological research helps to
address food security issues under new IPM and pesticide reduction policies
for global crop production systems. J Exp Bot. 2011;62(10):3251–61.
9. Butenuth M, Gösseln GV, Tiedge M, Heipke C, Lipeck U, Sester M.
Integration of heterogeneous geospatial data in a federated database.
ISPRS J Photogrammetry Remote Sensing. 2007;62(5):328–46.
10. Castronova AM, Goodall JL, Elag MM. Models as web services using the
open geospatial consortium (OGC) web processing service (WPS) standard.
Environ Model Softw. 2013;41:72–83.
11. Cruz SAB, Monteiro AMV, Santos R. Automated geospatial web services
composition based on geodata quality requirements. Comput Geosci.
12. Dorigo WA, Zurita-Milla R, de Wit AJW, Brazile J, et al. A review on reflective
remote sensing and data assimilation techniques for enhanced
agroecosystem modelling. Int J Appl Earth Obs Geoinf. 2007;9(2):165–93.
13. Estes LD, Bradley BA, Beukes H, Hole DG, et al. Comparing mechanistic and
empirical model projections of crop suitability and productivity: implications
for ecological forecasting. Glob Ecol Biogeogr. 2013;22(8):1007–18.
Leibovici et al. Open Geospatial Data, Software and Standards (2017) 2:20 Page 10 of 11
14. Ewert F, Rötter RP, Bindi M, Webber H, Trnka M, Kersebaum KC, Olesen JE,
van Ittersum MK, Janssen S, Rivington M, Semenov MA, Wallach D, Porter
JR, Stewart D, Verhagen J, Gaiser T, Palosuo T, Tao F, Nendel C, Roggero PP,
Bartošová L, Asseng S. Crop modelling for integrated assessment of risk
to food production from climate change. Environ Model Softw. 2015;72:
15. Frank A. Analysis of dependence of decision quality on data quality. J Geogr
16. Gaber N, et al. (2008) Integrated modeling for integrated environmental
decision making, white paper EPA100/R-08/010, EPA (united states
environmental protection agency).
17. Guillot G, Leblois R, Coulon A, Frantz AC. Statistical methods in spatial
genetics. Mol Ecol. 2009;18(23):4734–56.
18. Hintum van TJL, Knupffer H (2010) Current taxonomic composition of
European genebank material documented in EURISCO. In: Plant genetic
resources: characterization and utilization 8 (2)S: 182–188.
19. Holzworth DP, Huth NI, deVoil PG, Zurcher EJ, et al. APSIM –evolution
towards a new generation of agricultural systems simulation. Environ Model
20. Jackson MJ, Hobona G, Bernard L, Brauner J, Higgins C. The role and
development of a persistent interoperability test bed for geosciences
research. In: Sinha AK, Arctur D, Jackson I, Gundersen L, editors. Geological
Society of America, societal challenges and GeoInformatics special paper,
vol. 482; 2011. p. 69–75.
21. Kuhn W. Core concepts of spatial information for transdisciplinary research.
Int J Geogr Inf Sci. 2012;26(12):2267–76.
22. Larsen TJ, Niederman F, Limayem M, Chan J. The role of modelling in
achieving information systems success: UML to the rescue? Inf Syst J. 2009;
23. Launay M, Guerif M. Assimilating remote sensing data into a crop model to
improve predictive performance for spatial applications. Agric Ecosyst
24. Leblanc MJ, Favreau G, Massuel S, Tweed SO, et al. Land clearance and
hydrological change in the Sahel: SW Niger. Glob Planet Chang. 2008;
25. Leibovici DG, Hobona G, Stock K, Jackson M. Qualifying geospatial workflow
models for adaptive controlled validity and accuracy. In: IEEE 17th
international conference on GeoInformatics 2009. Fairfax; 2009. p. 1–5.
26. Leibovici DG, Jackson MJ. Multi-scale integration for Spatio-temporal
Ecoregioning delineation. Int J Image Data Fusion. 2011;2(2):105–19.
27. Leibovici DG, Pourabdollah A, Jackson MJ. Meta-propagation of
uncertainties for scientific workflow Management in Interoperable Spatial
Data Infrastructures. In: EGU 2011, European Geosciences Union. Vienna:
General Assembly; 2011.
28. Leibovici DG, Pourabdollah A, Jackson MJ. Which spatial quality can be
meta-propagated? J Spat Sci. 2013;58(1):3–14.
29. Leymann F. BPEL vs. BPMN 2.0: should you care? Lecture Notes Business
Info Proc. 2011;67:8–13.
30. Liu J, Williams JR, Zehnder AJB, Yang H. GEPIC –modelling wheat yield and
crop water productivity with high resolution on a global scale. Agric Syst.
31. Liu J, Folberth C, Yang H, Röckström J, et al. A global and spatially explicit
assessment of climate change impacts on crop production and
consumptive water use. PLoS One. 2013;8(2):e57750.
32. Loireau M, Leibovici DG, Desconnets J-C, D'Herbès J-M. LEIS, a tool for
diagnosis and prevision of Antropogene pressure on natural vegetation.
In: RGLDD (remote sensing and Geoinformation processing in the assessment
and monitoring of land degradation and desertification). Trier; 2005.
33. Manel S, Holderegger R. Ten years of landscape genetics. Trends Ecol Evol.
34. Mayes S, Massawe FJ, Alderson PG, Roberts JA, Azam-Ali SN, Hermann M.
The potential for underutilised crops to improve security of food
production. J Exp Bot. 2012;63(3):1075–9.
35. Meek S, Jackson M, Leibovici DG. A generic BPMN client to enable seamless
workflow composition of OGC web services. Comput Geosci. 2016;87:76–83.
36. Mirschel W, Wieland R, Wenkel KO, Nendel C, et al. YIELDSTAT –a spatial
yield model for agricultural crops. Eur J Agron. 2014;52(Part A):33–46.
37. Molosiwa OO (2012) Genetic diversity and population structure analysis
of Bambara groundnut [Vigna subterranea (L.) Verdc.] landraces using
morph-agronomic characters and SSR markers. PhD in crop sciences
University of Nottingham.
38. Morin E. Interdisciplinarité et transdisciplinarité. Transversales Sci Cult.
39. Nativi S, Mazzetti P, Geller GN. Environmental model access and
interoperability: the GEO model web initiative. Environ Model Softw.
40. Neumann K, Verburg PH, Stehfest E, Müller C. The yield gap of global grain
production: a spatial analysis. Agric Syst. 2010;103(5):316–26.
41. Olukolu BA, Mayes S, Stadler F, Ng NQ, et al. Genetic diversity in Bambara
groundnut (Vigna subterranea (L.) Verdc.) as revealed by phenotypic
descriptors and DArT marker analysis. Genet Resour Crop Evol. 2012;59(3):
42. Priya S, Shibasaki R. National spatial crop yield simulation using GIS-based
crop production model. Ecol Model. 2001;136(2–3):113–29.
43. Reynolds JF, Grainger A, Stafford-Smith DM, Bastin G, Garcia-Barrios L,
Fernandez RJ, et al. Scientific concepts for an integrated analysis of
desertification. Land Degrad Dev. 2011;22:166–83.
44. Recker J. BPMN research: what we know and what we Don’t know.
In: Mendling J, Weidlich M, editors. Business process model and notation.
Lecture notes in business information processing. Berlin Heidelberg:
Springer; 2012. p. 1–7.
45. Rounsevell MDA, Annetts JE, Audsley E, Mayr T, et al. Modelling the spatial
distribution of agricultural land use at the regional scale. Agric Ecosyst Environ.
46. Santos R, Algar A, Field R, Mayes S. (2017) Integrating GIScience and Crop
Science datasets: a study involving genetic, geographic and environmental
data. PeerJ Preprints. 5:e2248v4. https://doi.org/10.7287/peerj.preprints.2248v4.
47. Sghaier M. Analyse de l’intégration entre les données socio économiques et
écologiques: synthèse régionale. In: Requier-Desjardins M, Ben Khatra N,
Nedjraoui D, Issoufou WS, Sghaier M, Briki M, editors. Surveillance
environnementale et développement : acquis et perspectives -
Méditerranée, Sahara et Sahel. Montpellier: CIHEAM (Centre International de
Hautes Etudes Agronomiques Méditerranéennes), 2012. (Options
Méditerranéennes, Série B : Etudes et Recherches, n. 68); 2012. p. 71–94.
48. Tan G, Shibasaki R. Global estimation of crop productivity and the impacts
of global warming by GIS and EPIC integration. Ecol Model. 2003;168(3):
49. Van Ittersum MK, Ewert F, Heckelei T, Wery J, et al. Integrated assessment of
agricultural systems –a component-based framework for the European
Union (SEAMLESS). Agric Syst. 2008;96(1–3):150–65.
50. Wainwright J, Mulligan M, editors. Environmental modelling: finding
simplicity in complexity: Wiley; 2013.
51. Wilson P, Harpur N, Darling R. Explaining variation in farm and farm
business performance in respect to farmer segmentation analysis:
implications for land use policies. Land Use Policy. 2013;30(1):147–56.
52. Xiao Y, Mignolet C, Mari J-F, Benoît M. Modelling the spatial distribution
of crop sequences at a large regional scale using land-cover survey data:
a case from France. Comput Electron Agric. 2014;102:51–63.
Leibovici et al. Open Geospatial Data, Software and Standards (2017) 2:20 Page 11 of 11
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at