Agricultural System Data, Models and Knowledge Products for the Big Data Era

Sander J C Janssen,, Porter Cheryl H, Moore Andrew D, Athanasiadis Ioannis N, Foster Ian, Jones James W, Antle John M

Abstract


Agricultural modeling has long suffered from fragmentation in model implementation. Many models are developed, there is much redundancy, they are often poorly coupled, their  component re-use is rare, and it is frequently difficult to apply them to generate real solutions for the agricultural sector. To improve this situation, the study  argue that an open, self-sustained, and committed community is required to co-develop agricultural models and associated data and tools as a common resource. Such a community can benefit from recent developments in ICT. Examines how such developments can be leveraged to design and implement the next generation of data, models, and decision support tools for agricultural production systems. Evaluates relevant technologies for their maturity, expected development, and potential to benefit the agricultural modeling community. Technologies considered encompass methods for collaborative development involving stakeholders and users in a transdisciplinary manner. Suggests that an overall research challenge, the interoperability of data sources, modular granular open models, reference data sets for applications and specific user requirements analysis methodologies need to be addressed to allow agricultural modeling to enter in the big data era. This can  enable much higher analytical capacities and the integrated use of new data sources. Overall agricultural systems modeling needs to rapidly adopt and absorb state-of-the-art data and ICT technologies with a focus on the needs of beneficiaries and on facilitating those who develop applications of their models. It requires the widespread uptake of a set of best practices as standard operating procedures.


Keywords


Keywords: Agricultural models,ICT,Linked data, Big data, Open science, Sensing, Visualization

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References


Antle, J. M., Jones, J W and C. E. Rosenzweig. (2016). Towards a new generation of agricultural system models and knowledge products: introduction. Agric. Syst. (this issue).

Antle et al., (2017). Towards a new generation of agricultural system models and knowledge products: model design, improvement and implementation.. Agric. Syst., 155., pp. 255-268

Antoniou G and van Harmelen,F (2004). A Semantic Web Primer. The MIT Press, Cambridge, Massachusetts; London, England.

Arrouays , D et al.,( 2014). GlobalSoilMap: toward a fine-resolution global grid of soil properties. Adv. Agron., 125 , pp. 93-134

Asseng et al., (2013). Uncertainty in simulating wheat yields under climate change. Nat. Clim. Chang., 3 (2013), pp. 827-832, 10.1038/nclimate1916

Athanasiadis, I N. (2015). “Challenges in modelling of environmental semantics”, In: Environmental Software Systems: Infrastructures, Services and Applications (ISESS 2015), IFIP Advances in Information and Communication Technology, vol. 448, pg. 19–25, Springer.

Athanasiadis and Villa,I N ( 2013). A roadmap to domain specific programming languages for environmental modeling: key requirements and concepts. Proceedings of the 2013 ACM workshop on Domain-specific modeling , pp. 27-32. New York, NY, USA 2013. (doi:10.1145/2541928.2541934)

Athanasiadis et al., (2015). Thematic issue on agricultural systems modelling and software – part II. Environ. Model. Softw., 72 (2015), pp. 274-275, 10.1016/j.envsoft.2015.09.004

Bassu et al., (2014). How do various maize crop models vary in their responses to climate change factors? Glob. Chang. Biol., 20 (2014), pp. 2301-2320, 10.1111/gcb.12520

B.W. Boehm (1987). Improving software productivity. Computer, 20 (9) (1987), pp. 43-57

CAIDA, Center for Applied Internet Data Analysis, (2010). “Summary of anonymization best practice techniques.” www.caida.org/projects/predict/anonymization. (Accessed Sept 5, 2014).

Callahan, S P. et al.,( 2006).VisTrails: visualization meets data management. Proc. SIGMOD Int'l Conf. Management of Data (SIGMOD 06), ACM (2006), pp. 745-747

Capolupo, A. et al., (2015). Estimating plant traits of grasslands from UAV-acquired hyperspectral images: a comparison of statistical approaches. ISPRS International Journal of Geo-Information, 4 (2015), p. 2792

Chard, K et al., (2015). Globus Data Publication as a Service: Lowering Barriers to Reproducible Science, 11th IEEE International Conference on eScience Munich, Germany (2015)

Cockburn,A ( 2006). Agile Software Development: The Cooperative Game. Addison-Wesley.

Danes, A et al., (2014). Mobiles for agricultural development : exploring trends, challenges and policy options for the Dutch government. Alterra Report 2501, Alterra, Wageningen-UR, Wageningen , p. 25

Dixon, J. et al., (2004). Smallholders, Globalization and Policy Analysis. Agricultural Management, Marketing and Finance Service (AGSF), Agricultural Support Systems Division, Rome, FAO .

M. Donatelli, A. Rizzoli (2008). A design for framework-independent model components of biophysical systems. International Congress on Environmental Modelling and Software iEMSs 2008, Proceedings of the iEMSs Fourth Biennial Meeting, Barcelona, Catalonia (2008), pp. 727-734. 7–10 July 2008

Donatelli et al., (2014). A generic framework for evaluating hybrid models by reuse and composition – a case study on soil temperature simulation. Environ. Model. Softw., 62), pp. 478-486

Drosatos, G. et al., (2014). Privacy-preserving computation of participatory noise maps in the cloud. Journal of Systems and Software, Elsevier (2014)

Dubey, A and Wagle, D (2007). Delivering software as a service. McKinsey Q. May

Elliott et al., J. ( 2014). The parallel system for integrating impact models and sectors (pSIMS)

Environ. Model. Softw., 62 , pp. 509-515

Fekete, J D ( 2013). Visual analytics infrastructures: from data management to exploration. Computer, Institute of Electrical and Electronics Engineers (IEEE), Visual Analytics: Seeking the Unknown, 46 (2013), pp. 22-29

Fekete, J D and Silva, C (2012). Managing data for visual analytics: opportunities and challenges. IEEE Data Engineering Bulletin, 35 (3), pp. 27-36

Foster, I (2011). Globus online: accelerating and democratizing science through cloud-based services. IEEE Internet Comput. (2011). (May/June):70–73

Foster, I (2015). Lessons from industry for science cyberinfrastructure: simplicity, scale, and sustainability via SaaS/PaaS. SCREAM'15: The Science of Cyberinfrastructure: Research, Experience, Applications and Models . Portland, Oregon,

Gartner, ( 2016). Top 10 Technology Trends Signal the Digital Mesh. Gartner Inc. Accessed online at: www.gartner.com/smarterwithgartner/top-ten-technology-trends-signal-the-digital-mesh/

Goecks, J , Nekrutenko A and Taylor, J. et al., (2010). Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol., 11 (8) (2010), p. R86

Goff, S A et al., (2011). The iPlant collaborative: cyberinfrastructure for plant biology. Front. Plant Sci., 2 .

Hillyer, C. et al., (2003). The ModCom modular simulation system. Eur. J. Agron., 18 (2003), pp. 333-343

Hochman, Z et al., (2009). Re-inventing model-based decision support with Australian dryland farmers. 4. Yield prophet® helps farmers monitor and manage crops in a variable climate. Crop and Pasture Science, 60 (2009), pp. 1057-1070

Holzworth, D P et al., (2010). Simplifying environmental model reuse. Environ. Model. Softw., 25 (2) (2010), pp. 269-275, 10.1016/j.envsoft.2008.10.018

Holzworth, D. et al., (2014a). Thematic issue on agricultural systems modelling and software – part I. Environ. Model. Softw., 62 (326) (2014), p. 2014, 10.1016/j.envsoft.2014.11.003

Holzworth, D P et al., (2014b). APSIM: evolution towards a new generation of agricultural systems simulation. Environ. Model. Softw., 62 (2014), pp. 327-360,

Holzworth, D P. et.al. (2015). Agricultural production systems modelling and software: current status and future prospects. Environ. Model. Softw., 72 (2015), pp. 276-286.

ArticleDownload PDFView Record in ScopusGoogle Scholar

Innis, G S (1978). Objectives and structure for a grassland simulation model. Grassland Simulation Model, Springer-Verlag, New York (1978), pp. 1-21

Janssen, S et al., (2009). A database for integrated assessment of European agricultural systems. Environ. Sci. Pol., 12 , pp. 573-587

Janssen, S et al., (2010). A generic bio-economic farm model for environmental and economic assessment of agricultural systems. Environ. Manag., 46 (2010), pp. 862-877

Janssen, S et al., (2011). Linking models for assessing agricultural land use change. Comput. Electron. Agric., 76 (2011), pp. 148-160

S. Janssen, S et.al. (2015). Towards a new generation of agricultural system models, data, and knowledge products: building an open web-based approach to agricultural data, system modeling and decision support. AgMIP.

Johnston, J M et al., (2011). An integrated modeling framework for performing environmental assessments: application to ecosystem services in the Albemarlee Pamlico basins (NC and VA, USA). Ecol. Model., 222 (14) (2011), pp. 2471-2484

Jones, J W. et al., (2003). The DSSAT cropping system model. Eur. J. Agron., 18 (3–4) (2003), pp. 235-265

Jones , J W. et al., (2017). Towards a new generation of agricultural system models and knowledge products: state of agricultural systems science. Agric. Syst., 155 (2017), pp. 269-288

Keating, B A. et al., (2003). An overview of APSIM, a model designed for farming systems simulation. Eur. J. Agron., 18 (2003), pp. 267-288

Kersebaum, K C. et al., (2015). Analysis and classification of data sets for calibration and validation of agro-ecosystem models. Environ. Model. Softw. (2015)

Laniak, G F. et al., (2013). Integrated environmental modeling: a vision and roadmap for the future. Environ. Model. Softw., 39 (2013), pp. 3-23

Lloyd, W. et al., (2011). “Environmental modeling framework invasiveness: analysis and implications.” Environ. Model. Softw. 25(10):1240–1250.

R. Lokers, R et.al. (2016). Analysis of big data technologies for use in agro-environmental science. Environ. Model. Softw., 84 (2016), pp. 494-504

Ludäscher, B. et al., (2003). GEON: toward a cyberinfrastructure for the geosciences: a prototype for geologic map integration via domain ontologies. U.S. Geological Survey Open-File Report 03-071.

Maidment, D R et al., (2009). Accessing and sharing data using CUAHSI water data services. Hydroinformatics in Hydrology, Hydrogeology and Water Resources, 331, Proc. of Symposium JS.4 at the Joint IAHS & IAH Convention, Hyderabad, India, September 2009, IAHS Publ. , pp. 213-223

Manolescu, I et al., (2009). Reactive Workflows for Visual Analytics. In Data Management & Visual Analytics workshop, Berlin (2009)

McCown, R L (2002). Changing systems for supporting farmers' decisions: problems, paradigms, and prospects. Agric. Syst., 74 (2002), pp. 179-220

Mitchell, EEL and Gauthier, J S (1976). Advanced continuous simulation language (ACSL). Simulation 1976, 26 (3).

Montella, E et al., (2015). FACE-IT: a science gateway for food security research. Concurrency and Computation: Practice and Experience. (DOI:10.1002/cpe.3540)

Moore, A D et al., (2007). The common modelling protocol: a hierarchical framework for simulation of agricultural and environmental systems. Environ. Model. Softw., 95 (2007), pp. 37-48

Muetzelfeldt, R and Massheder, UJ (2003). The simile visual modelling environment. Eur. J. Agron., 18 (2003), pp. 345-358

NESSI (2012). Big Data: A New World of Opportunities, NESSI White Paper. Networked European Software and Services Initiative, NESSI.

NIST, National Institute of Standards and Technology (2013). NIST, National Institute of Standards and Technology. 2013. “NIST Cloud Computing Standards Roadmap.” US Department of Commerce. Special Publication 500–291, (Version 2).

Noble, I R (1975). Computer simulations of sheep grazing in the arid zone. Ph.D. thesis, University of Adelaide (1975), p. 209

Porter, C H. et al., (2014). Harmonization and translation of crop modeling data to ensure interoperability. Environ. Model. Softw.

Powell, M., T. Davies, and K.C. Taylor, K C . (2012). “ICT for or against development? An introduction to the ongoing case of Web 3.0.” IKM Working Paper No. 16, March 2012.

Raymond, E S (1999). The Cathedral and the Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary. O′Reilly Media (October 1999), p. 279

Reynolds, J F and Acock, B (1997). Modularity and genericness in plant and ecosystem models. Ecol. Model., 94 (1997), pp. 7-16

Ritchie, J R et al., (1991). A User's Guide to CERES Maize - V2.10. International Fertilizer Development Center, Muscle Shoals, Alabama.

Rizzoli, A E et al., (2008). Semantic links in integrated modelling frameworks. Math. Comput. Simul., 78 (2) (2008), pp. 412-423

Rojas-Ruiz, J and Diofasi, A (2014). Upwardly Mobile: How Cell Phones Are Improving Food and Nutrition Security. Hunger and Undernutrition Blog. http://www.hunger-undernutrition.org/blog/2014/04/upwardly-mobile-how-cell-phones-are-improving-food-and-nutrition-security.html

Rosenzweig, C et al., (2013). The agricultural model Intercomparison and improvement project (AgMIP): protocols and pilot studies. Agric. For. Meteorol., 170 (2013), pp. PDFView Record in ScopusGoogle Scholar

Thomas, J and Cook, k. Eds. (2005). Illuminating the Path: Research and Development Agenda for Visual Analytics, National Visualization and Analytics Center, IEEE.

van Ittersum, M K. et al., (2003). On approaches and applications of the Wageningen crop models. Eur. J. Agron., 18 (2003), pp. 201-234

White, J W. et al., (2013). Integrated description of agricultural field experiments and production: the ICASA version 2.0 data standards. Comput. Electron. Agric., 96 (2013), pp. 1-12

Wight, J R and Skiles, J W (1987). SPUR: Simulation of Production and Utilization of Rangelands. Documentation and User Guide. U.S. Department of Agriculture, Agricultural Research Service (1987). ARS 63. 372 pp

Williams, D N et al., (2009). The earth system grid: enabling access to multi-model climate simulation data. Bull. Am. Meteorol. Soc., 90 (2) (2009), pp. 195-205

Zeegers, E (2012). Thousands of Dutch citizens take part in the first national iSPEX-measure-day (2012). http://ispex.nl/en/duizenden-nederlanders-doen-mee-aan-eerste-nationale-ispex-meetdag/.


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