Cheryl H. Porter

The DSSAT cropping system model

The decision support system for agrotechnology transfer (DSSAT) has been in use for the last 15 years by researchers worldwide. This package incorporates models of 16 different crops with software that facilitates the evaluation and application of the crop models for different purposes. Over the last few years, it has become increasingly difficult to maintain the DSSAT crop models, partly due to fact that there were different sets of computer code for different crops with little attention to software design at the level of crop models themselves. Thus, the DSSAT crop models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation and maintenance. The basis for the new DSSAT cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. It has one Soil module, a Crop Template module which can simulate different crops by defining species input files, an interface to add individual crop models if they have the same design and interface, a Weather module, and a module for dealing with competition for light and water among the soil, plants, and atmosphere. It is also designed for incorporation into various application packages, ranging from those that help researchers adapt and test the CSM to those that operate the DSSAT-CSM to simulate production over time and space for different purposes. In this paper, we describe this new DSSAT-CSM design as well as approaches used to model the primary scientific components (soil, crop, weather, and management). In addition, the paper describes data requirements and methods used for model evaluation. We provide an overview of the hundreds of published studies in which the DSSAT crop models have been used for various applications. The benefits of the new, re-designed DSSAT-CSM will provide considerable opportunities to its developers and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems at field, farm, and higher levels.

Approaches to modular model development

Abstract One of the main goals of the International Consortium for Agricultural Systems Applications (ICASA) is to advance the development and application of compatible and complementary models, data and other systems analysis tools. To help reach that goal, it will adopt and recommend modular approaches that facilitate more systematic model development, documentation, maintenance, and sharing. In this paper, we present criteria and guidelines for modules that will enable them to be plugged into existing models to replace an existing component or to add a new one with minimal changes. This will make it possible to accept contributions from a wide group of modellers with specialities in different disciplines. Two approaches to modular model development have emerged from different research groups in ICASA. One approach was developed by extending the programming methods used in the Fortran Simulation Environment developed in The Netherlands. This method is being used in revisions of some of the Decision Support Systems for Agrotechnology Transfer crop models. A simple example of this approach is given in which a plant growth module is linked with a soil water balance module to create a crop model that simulates growth and yield for a uniform area. The second approach has been evolving within the Agricultural Production Systems Research Unit group in Australia. This approach, implemented in software called Agricultural Production Systems Simulator, consists of plug-in/pull-out modules and an infrastructure for inter-module communication. The two approaches have important similarities, but also differ in implementation details. In both cases, avoiding reliance on any particular programming language has been an important design criterion. By comparing features of both approaches, we have started to develop a set of recommendations for module design that will lead to a ‘toolkit’ of modules that can be shared throughout the ICASA network.

Brief history of agricultural systems modeling

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the “next generation” models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

Toward a New Generation of Agricultural System Data, Models, and Knowledge Products: State of Agricultural Systems Science

We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.

Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology

Agricultural modeling has long suffered from fragmentation in model implementation. Many models are developed, there is much redundancy, models are often poorly coupled, model component re-use is rare, and it is frequently difficult to apply models to generate real solutions for the agricultural sector. To improve this situation, we 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 information and communications technology (ICT). We examine how such developments can be leveraged to design and implement the next generation of data, models, and decision support tools for agricultural production systems. Our objective is to assess relevant technologies for their maturity, expected development, and potential to benefit the agricultural modeling community. The technologies considered encompass methods for collaborative development and for involving stakeholders and users in development in a transdisciplinary manner. Our qualitative evaluation suggests that as 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 will 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. This adoption requires the widespread uptake of a set of best practices as standard operating procedures.

Harmonization and translation of crop modeling data to ensure interoperability

The Agricultural Model Intercomparison and Improvement Project (AgMIP) seeks to improve the capability of ecophysiological and economic models to describe the potential impacts of climate change on agricultural systems. AgMIP protocols emphasize the use of multiple models; consequently, data harmonization is essential. This interoperability was achieved by establishing a data exchange mechanism with variables defined in accordance with international standards; implementing a flexibly structured data schema to store experimental data; and designing a method to fill gaps in model-required input data. Researchers and modelers are able to use these tools to run an ensemble of?models on a single, harmonized dataset. This allows them to compare models directly, leading ultimately to model improvements. An important outcome is the development of a platform that facilitates researcher collaboration from many organizations, across many countries. This would have been very difficult to achieve without the AgMIP data interoperability standards described in this paper. Heterogeneous data can be harmonized and translated to multiple model formats.The ICASA data standards provide an extensible data structure and ontology.JSON structures provide a flexible, efficient means of handling heterogeneous data.DOME functions enable a consistent means of providing missing or inadequate data.Data provenance is maintained from data sources through simulated model outputs.

FACE-IT: A science gateway for food security research: FACE-IT: A SCIENCE GATEWAY FOR FOOD SECURITY RESEARCH

Progress in sustainability science is hindered by challenges in creating and managing complex data acquisition, processing, simulation, post‐processing, and intercomparison pipelines. To address these challenges, we developed the Framework to Advance Climate, Economic, and Impact Investigations with Information Technology (FACE‐IT) for crop and climate impact assessments. This integrated data processing and simulation framework enables data ingest from geospatial archives; data regridding, aggregation, and other processing prior to simulation; large‐scale climate impact simulations with agricultural and other models, leveraging high‐performance and cloud computing; and post‐processing to produce aggregated yields and ensemble variables needed for statistics, for model intercomparison, and to connect biophysical models to global and regional economic models. FACE‐IT leverages the capabilities of the Globus Galaxies platform to enable the capture of workflows and outputs in well‐defined, reusable, and comparable forms. We describe FACE‐IT and applications within the Agricultural Model Intercomparison and Improvement Project and the Center for Robust Decision‐making on Climate and Energy Policy. Copyright

Issues of spatial and temporal scale in modeling the effects of field operations on soil properties

Tillage is an important procedure for modifying the soil environment to enhance crop growth and conserve soil and water resources. Process-based models of crop production are widely used in decision support, but few explicitly simulate tillage. The Cropping Systems Model (CSM) was modified to simulate tillage and related field operations for single seasons or multiple years. This paper provides an overview of how the new routines were implemented and discusses issues of spatial and temporal scaling that influenced the underlying strategy. The processes considered included effects of crop residues on the soil surface and on chemical and physical properties that vary with soil depth. Each event is described by date and implement used. The implement is characterized by its effects on soil properties, including mixing of soil layers and crop residues and changes in soil bulk density. The modeled responses are illustrated with a hypothetical case comparing effects of four implements (mold board plow, tandem disk, tine harrow, and planking) and a field experiment where winter wheat (Triticum aestivum L.) was grown with different tillage and residue management practices. From a modeling viewpoint, a key issue was how to manage different spatial and time scales. The soil is simulated as varying only with depth but in reality, the thickness of the soil is affected by tillage. This poses challenges for ensuring that the masses for water, nutrients, residues and the soil per se are conserved as soil layers are mixed and the density of each layer is altered. The model runs on a daily time step, but events such as tillage, application of residue, and irrigation can all happen within a single day and the sequence/timing can influence simulations. The new routines for field operations improve representation of tillage and residue management in the CSM model, but they are best viewed as providing a framework for future work that explicitly considers effects of residue type, soil type and distribution, and soil moisture on tillage effects and that deal with effects of rainfall kinetic energy in more detail.

Calibration-induced uncertainty of the EPIC model to estimate climate change impact on global maize yield

Understanding the interactions between agricultural production and climate is necessary for sound decision-making in climate policy. Gridded and high-resolution crop simulation has emerged as a useful tool for building this understanding. Large uncertainty exists in this utilization, obstructing its capacity as a tool to devise adaptation strategies. Increasing focus has been given to sources of uncertainties for climate scenarios, input-data, and model, but uncertainties due to model parameter or calibration are still unknown. Here, we use publicly available geographical data sets as input to the Environmental Policy Integrated Climate model (EPIC) for simulating global-gridded maize yield. Impacts of climate change are assessed up to the year 2099 under a climate scenario generated by HadEM2-ES under RCP 8.5. We apply five strategies by shifting one specific parameter in each simulation to calibrate the model and understand the effects of calibration. Regionalizing crop phenology or harvest index appears effective to calibrate the model for the globe, but using various values of phenology generates pronounced difference in estimated climate impact. However, projected impacts of climate change on global maize production are consistently negative regardless of the parameter being adjusted. Different values of model parameter result in a modest uncertainty at global level, with difference of the global yield change less than 30% by the 2080s. The uncertainty subjects to decrease if applying model calibration or input data quality control. Calibration has a larger effect at local scales, implying the possible types and locations for adaptation.

Climate adaptation imperatives: untapped global maize yield opportunities

Climate change represents an unavoidable and growing challenge to food security, imposing new adaptation imperatives on all farmers. Maize is arguably the worlds most productive grain crop, as measured by grain yield. However, maize yields vary dramatically due to many factors, including soils, climate, pests, disease, agronomic practices, and seed quality. The difference between observed yields and those achievable by optimized crop production methods is called the yield gap. In this work we quantified the current yield gap for 44 countries through the use of a large private-sector data set recently made available to the crop modelling community. The yield gap was quantified for three groups of countries, categorized by level of intensification. Observed yield gaps for high, medium, and low levels of intensification are 23%, 46%, and 68%, respectively. If all maize production countries were able to shrink their yield gap to 16.5% (as in the USA) an additional 335 million metric tons (MMT) of maize grain would be produced. This represents a 45% increase over the 741 MMT produced by these countries in 2010. These data demonstrate that a major untapped maize yield opportunity exists, especially in those countries where intensification has not kept pace with the rest of the world.