Agricultural production systems modelling and software: Current status and future prospects

During the past decade, the application of agricultural production systems modelling has rapidly expanded while there has been less emphasis on model improvement. Cropping systems modelling has become agricultural modelling, incorporating new capabilities enabling analyses in the domains of greenhouse gas emissions, soil carbon changes, ecosystem services, environmental performance, food security, pests and disease losses, livestock and pasture production, and climate change mitigation and adaptation. New science has been added to the models to support this broadening application domain, and new consortia of modellers have been formed that span the multiple disciplines.There has not, however, been a significant and sustained focus on software platforms to increase efficiency in agricultural production systems research in the interaction between the software industry and the agricultural modelling community. This paper describes the changing agricultural modelling landscape since 2002, largely from a software perspective, and makes a case for a focussed effort on the software implementations of the major models. The agricultural modelling community has broadened its scientific focus over the last decade.The software implementations of the leading agricultural models hasn't changed significantly in the last decade.A focussed effort on agricultural modelling software and process is needed.

[1]  Sander Janssen,et al.  Original papers: Linking models for assessing agricultural land use change , 2011 .

[2]  Sander Janssen,et al.  Evaluating OpenMI as a model integration platform across disciplines , 2013, Environ. Model. Softw..

[3]  Frank Ewert,et al.  Crop modelling for integrated assessment of risk to food production from climate change , 2015, Environ. Model. Softw..

[4]  Robert Muetzelfeldt,et al.  The Simile visual modelling environment , 2003 .

[5]  Tomas Persson,et al.  Reduction in greenhouse gas emissions due to the use of bio-ethanol from wheat grain and straw produced in the south-eastern USA , 2010, The Journal of Agricultural Science.

[6]  Senthold Asseng,et al.  An overview of APSIM, a model designed for farming systems simulation , 2003 .

[7]  Michael Winter,et al.  Valuing local knowledge as a source of expert data: Farmer engagement and the design of decision support systems , 2012, Environ. Model. Softw..

[8]  D. Raes,et al.  AquaCrop—The FAO Crop Model to Simulate Yield Response to Water: III. Parameterization and Testing for Maize , 2009 .

[9]  J. Gallant,et al.  A catchment framework for one‐dimensional models: introducing FLUSH and its application , 2008 .

[10]  Peter Krause,et al.  Environmental modeling framework invasiveness: Analysis and implications , 2011, Environ. Model. Softw..

[11]  Olaf David,et al.  A software engineering perspective on environmental modeling framework design: The Object Modeling System , 2013, Environ. Model. Softw..

[12]  M. Rivington,et al.  Report on the Meta-­Analysis of Crop Modelling for Climate Change and Food Security Survey , 2010 .

[13]  C. Müller,et al.  Constraints and potentials of future irrigation water availability on agricultural production under climate change , 2013, Proceedings of the National Academy of Sciences.

[14]  Raghavan Srinivasan,et al.  Soil-Landscape Estimation and Evaluation Program (SLEEP) to predict spatial distribution of soil attributes for environmental modeling. , 2015 .

[15]  Electra Kalaugher,et al.  An integrated biophysical and socio-economic framework for analysis of climate change adaptation strategies: The case of a New Zealand dairy farming system , 2013, Environ. Model. Softw..

[16]  Xiaomao Lin,et al.  Maize potential yields and yield gaps in the changing climate of northeast China , 2012 .

[17]  B. M. Petersen,et al.  Evaluating nitrogen taxation scenarios using the dynamic whole farm simulation model FASSET , 2003 .

[18]  C. Rotz,et al.  Whole-farm perspectives of nutrient flows in grassland agriculture , 2005 .

[19]  Philippe C. Baveye,et al.  Moving away from the geostatistical lamppost: Why, where, and how does the spatial heterogeneity of soils matter? , 2015 .

[20]  Jeffrey W. White,et al.  From genome to crop: integration through simulation modeling , 2004 .

[21]  Enli Wang,et al.  Large-scale, high-resolution agricultural systems modeling using a hybrid approach combining grid computing and parallel processing , 2013, Environ. Model. Softw..

[22]  Sahibzada Shafiullah,et al.  The development of an assessment tool to analyse the productivity and financial viability of dairy farms in Pakistan , 2012 .

[23]  Ian T. Foster,et al.  The parallel system for integrating impact models and sectors (pSIMS) , 2013, Environ. Model. Softw..

[24]  Chris Murphy,et al.  APSIM - Evolution towards a new generation of agricultural systems simulation , 2014, Environ. Model. Softw..

[25]  V. Snow,et al.  Describing N leaching from urine patches deposited at different times of the year with a transfer function , 2012 .

[26]  Hongtao Li,et al.  A Generic Bio-Economic Farm Model for Environmental and Economic Assessment of Agricultural Systems , 2010, Environmental management.

[27]  D. Cammarano,et al.  Economic and environmental evaluation of site-specific tillage in a maize crop in NE Italy , 2011 .

[28]  James W. Jones,et al.  Optimizing irrigation management for a spatially variable soybean field , 2003 .

[29]  A. Ash,et al.  Research opportunities for sustainable productivity improvement in the northern beef industry: A scoping study , 2014 .

[30]  Andrew D. Moore,et al.  Integrating pest population models with biophysical crop models to better represent the farming system , 2015, Environ. Model. Softw..

[31]  Herman Van Keulen,et al.  CROSPAL, software that uses agronomic expert knowledge to assist modules selection for crop growth simulation , 2010, Environ. Model. Softw..

[32]  Brett A. Bryan,et al.  Landscape futures analysis: Assessing the impacts of environmental targets under alternative spatial policy options and future scenarios , 2011, Environ. Model. Softw..

[33]  Gianni Bellocchi,et al.  Regional-scale analysis of carbon and water cycles on managed grassland systems , 2015, Environ. Model. Softw..

[34]  James W. Jones,et al.  Uncertainty in Simulating Wheat Yields Under Climate Change , 2013 .

[35]  Sander Janssen,et al.  Using the SEAMLESS Integrated Framework for ex-ante assessment of trade policies. , 2010 .

[36]  Amgad Elmahdi,et al.  A biophysical and economic model of agriculture and water in the Murray-Darling Basin, Australia , 2013, Environ. Model. Softw..

[37]  W. J. Shuttleworth,et al.  Creation of the WATCH Forcing Data and Its Use to Assess Global and Regional Reference Crop Evaporation over Land during the Twentieth Century , 2011 .

[38]  G. Hammer,et al.  Simulating the Yield Impacts of Organ-Level Quantitative Trait Loci Associated With Drought Response in Maize: A “Gene-to-Phenotype” Modeling Approach , 2009, Genetics.

[39]  H. H. Laar,et al.  Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions , 2006 .

[40]  Elías Fereres,et al.  AquaCrop: FAO's crop water productivity and yield response model , 2014, Environ. Model. Softw..

[41]  Jeffrey W. White,et al.  Evaluating the accuracy of VEMAP daily weather data for application in crop simulations on a regional scale , 2010 .

[42]  Gerrit Hoogenboom,et al.  Development and Evaluation of the RZWQM‐CROPGRO Hybrid Model for Soybean Production , 2005 .

[43]  Reimund P. Rötter,et al.  Analysis and classification of data sets for calibration and validation of agro-ecosystem models , 2015, Environ. Model. Softw..

[44]  Claudio O. Stöckle,et al.  CropSyst model evolution: From field to regional to global scales and from research to decision support systems , 2014, Environ. Model. Softw..

[45]  Ioannis N. Athanasiadis,et al.  A roadmap to domain specific programming languages for environmental modeling: key requirements and concepts , 2013, DSM '13.

[46]  James E. Hook,et al.  Estimating irrigation water use for maize in the Southeastern USA: A modeling approach , 2012 .

[47]  Joseph H. A. Guillaume,et al.  Characterising performance of environmental models , 2013, Environ. Model. Softw..

[48]  Angileri Vincenzo,et al.  Assessing agriculture vulnerabilities for the design of effective measures for adaptation to climate change (AVEMAC project) , 2012 .

[49]  Lisa E. Brennan,et al.  A farm-scale, bio-economic model for assessing investments in recycled water for irrigation , 2008 .

[50]  H. Sinoquet,et al.  An overview of the crop model STICS , 2003 .

[51]  Gerrit Hoogenboom,et al.  Parameterizing soil and weather inputs for crop simulation models using the VEMAP database , 2010 .

[52]  Valerie O. Snow,et al.  The challenges - and some solutions - to process-based modelling of grazed agricultural systems , 2014, Environ. Model. Softw..

[53]  Shu-lin Chen,et al.  A review on parameterization and uncertainty in modeling greenhouse gas emissions from soil , 2012 .

[54]  Greg P. Laughlin,et al.  Modelling crop productivity and variability for policy and impacts of climate change in eastern Canada , 2008, Environ. Model. Softw..

[55]  Michael Webb,et al.  Development of an oil palm cropping systems model: Lessons learned and future directions , 2014, Environ. Model. Softw..

[56]  Kelly R. Thorp,et al.  A model-independent open-source geospatial tool for managing point-based environmental model simulations at multiple spatial locations , 2013, Environ. Model. Softw..

[57]  Daniel Wallach,et al.  The error in agricultural systems model prediction depends on the variable being predicted , 2014, Environ. Model. Softw..

[58]  Jeffrey W. White,et al.  Evaluation of NASA satellite- and assimilation model-derived long-term daily temperature data over the continental US , 2008 .

[59]  Marcello Donatelli,et al.  A set of software components for the simulation of plant airborne diseases , 2015, Environ. Model. Softw..

[60]  C. Messina,et al.  Yield-trait performance landscapes: from theory to application in breeding maize for drought tolerance. , 2011, Journal of experimental botany.

[61]  Michiel Blind,et al.  OpenMI: the essential concepts and their implications for legacy software , 2005 .

[62]  Kemachandra Ranatunga,et al.  Potential use of saline groundwater for irrigation in the Murray hydrogeological basin of Australia , 2010, Environ. Model. Softw..

[63]  Jeffrey W. White,et al.  Agronomic data: advances in documentation and protocols for exchange and use , 2001 .

[64]  Greg McLean,et al.  Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. , 2010, Journal of experimental botany.

[65]  M. K. van Ittersum Integration across disciplines: the lessons learnt from the integrated project SEAMLESS. , 2009 .

[66]  Dean P. Holzworth,et al.  Plant Modelling Framework: Software for building and running crop models on the APSIM platform , 2014, Environ. Model. Softw..

[67]  Marco Bindi,et al.  Modelling olive trees and grapevines in a changing climate , 2015, Environ. Model. Softw..

[68]  Marco Acutis,et al.  A generic framework for evaluating hybrid models by reuse and composition - A case study on soil temperature simulation , 2014, Environ. Model. Softw..

[69]  Eric Justes,et al.  Evolution of the STICS crop model to tackle new environmental issues: New formalisms and integration in the modelling and simulation platform RECORD , 2014, Environ. Model. Softw..

[70]  J. B. Gregersen,et al.  OpenMI: Open modelling interface , 2007 .

[71]  O. Marinoni,et al.  Quantifying yield gaps in rainfed cropping systems: A case study of wheat in Australia , 2012 .

[72]  Boris Kompare,et al.  Environmental Modelling & Software , 2014 .

[73]  Marco Acutis,et al.  Model simplification and development via reuse, sensitivity analysis and composition: A case study in crop modelling , 2014, Environ. Model. Softw..

[74]  C. Stöckle,et al.  CropSyst, a cropping systems simulation model , 2003 .

[75]  J. Wolf,et al.  Yield gap analysis with local to global relevance—A review , 2013 .

[76]  Maarten S. Krol,et al.  Identification and classification of uncertainties in the application of environmental models , 2010, Environ. Model. Softw..

[77]  D. Raes,et al.  AquaCrop-The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles , 2009 .

[78]  D. Holzworth,et al.  Scope for improved eco-efficiency varies among diverse cropping systems , 2013, Proceedings of the National Academy of Sciences.

[79]  Peter deVoil,et al.  A participatory whole farm modelling approach to understand impacts and increase preparedness to climate change in Australia , 2014 .

[80]  Mary C. Hill,et al.  Integrated environmental modeling: A vision and roadmap for the future , 2013, Environ. Model. Softw..

[81]  Jeffrey W. White,et al.  Gene‐Based Approaches to Crop Simulation , 2003 .

[82]  Gerrit Hoogenboom,et al.  Evaluation of the RZWQM-CERES-Maize hybrid model for maize production , 2006 .

[83]  D. Raes,et al.  AquaCrop — The FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description , 2009 .

[84]  S. Chander,et al.  InfoCrop: A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. I. Model description , 2006 .

[85]  M. Robertson,et al.  The FARMSCAPE approach to decision support: farmers', advisers', researchers' monitoring, simulation, communication and performance evaluation , 2002 .

[86]  James W. Jones,et al.  Integrated description of agricultural field experiments and production: The ICASA Version 2.0 data standards , 2013 .

[87]  G. Hammer,et al.  Modeling QTL for complex traits: detection and context for plant breeding. , 2009, Current opinion in plant biology.

[88]  Leonie J. Pearson,et al.  Interpretive review of conceptual frameworks and research models that inform Australia's agricultural vulnerability to climate change , 2011, Environ. Model. Softw..

[89]  James W. Jones,et al.  The DSSAT cropping system model , 2003 .

[90]  François Bousquet,et al.  Modelling with stakeholders , 2010, Environ. Model. Softw..

[91]  Peter J. Thorburn,et al.  Sugarcane model intercomparison: Structural differences and uncertainties under current and potential future climates , 2015, Environ. Model. Softw..

[92]  Jie Zhang,et al.  Dynamic assessment of the impact of drought on agricultural yield and scale-dependent return periods over large geographic regions , 2014, Environ. Model. Softw..

[93]  Valerie O. Snow,et al.  Modelling the manager: Representing rule-based management in farming systems simulation models , 2014, Environ. Model. Softw..

[94]  Peter A. Vanrolleghem,et al.  Uncertainty in the environmental modelling process - A framework and guidance , 2007, Environ. Model. Softw..

[95]  Dean P. Holzworth,et al.  Simple software processes and tests improve the reliability and usefulness of a model , 2011, Environ. Model. Softw..

[96]  Brian Keating,et al.  Approaches to modular model development , 2001 .

[97]  James F. Cruise,et al.  An integrated crop and hydrologic modeling system to estimate hydrologic impacts of crop irrigation demands , 2015, Environ. Model. Softw..

[98]  Andrea Emilio Rizzoli,et al.  Ontology for Seamless Integration of Agricultural Data and Models , 2009, MTSR.

[99]  Andrea Emilio Rizzoli,et al.  Modelling with knowledge: A review of emerging semantic approaches to environmental modelling , 2009, Environ. Model. Softw..

[100]  Jeffrey W. White,et al.  Simulation-based analysis of effects of Vrn and Ppd loci on flowering in wheat , 2008 .

[101]  James W. Jones,et al.  The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and Pilot Studies , 2013 .

[102]  M. Rivington,et al.  Raising the bar? - The challenges of evaluating the outcomes of environmental modelling and software , 2011, Environ. Model. Softw..

[103]  James W. Jones,et al.  AgClimate: A climate forecast information system for agricultural risk management in the southeastern USA , 2006 .

[104]  Jimmy R. Williams,et al.  Simulating soil C dynamics with EPIC: Model description and testing against long-term data , 2006 .

[105]  J. Goudriaan,et al.  ON APPROACHES AND APPLICATIONS OF THE WAGENINGEN CROP MODELS , 2003 .

[106]  Stefano Balbi,et al.  Modeling trade-offs among ecosystem services in agricultural production systems , 2015, Environ. Model. Softw..

[107]  James W. Jones,et al.  Harmonization and translation of crop modeling data to ensure interoperability , 2014, Environ. Model. Softw..

[108]  B. Walsh,et al.  Models for navigating biological complexity in breeding improved crop plants. , 2006, Trends in plant science.

[109]  Michael Robertson,et al.  Whole-farm effects of livestock intensification in smallholder systems in Gansu, China , 2012 .

[110]  M. Donatelli,et al.  Integrated assessment of agricultural systems: a component - based framework for the European Union (Seamless) , 2008 .

[111]  Jeffrey W. White,et al.  Methodologies for simulating impacts of climate change on crop production , 2011 .

[112]  Tomas Persson,et al.  Simulating the production potential and net energy yield of maize-ethanol in the southeastern USA , 2010 .

[113]  G. Bellocchi,et al.  A Component-Based Framework for Simulating Agricultural Production and Externalities , 2010 .

[114]  M. Donatelli,et al.  Modelling cropping systems¿highlights of the symposium and preface to the special issues , 2002 .

[115]  Junguo Liu,et al.  A GIS-based tool for modelling large-scale crop-water relations , 2009, Environ. Model. Softw..

[116]  William D. Batchelor,et al.  Methodology for the use of DSSAT models for precision agriculture decision support , 2008 .

[117]  Fernando E. Miguez,et al.  A methodology and an optimization tool to calibrate phenology of short-day species included in the APSIM PLANT model: Application to soybean , 2014, Environ. Model. Softw..

[118]  Patrick Matgen,et al.  Enhanced biomass prediction by assimilating satellite data into a crop growth model , 2014, Environ. Model. Softw..

[119]  Andrew D. Moore,et al.  Trade-offs between productivity and ground cover in mixed farming systems in the Murrumbidgee catchment of New South Wales , 2009 .

[120]  D. Holzworth,et al.  Re-inventing model-based decision support with Australian dryland farmers. 4. Yield Prophet® helps farmers monitor and manage crops in a variable climate. , 2009 .