Correction: A time-course comparative clinical and immune response evaluation study between the human pathogenic Orientia tsutsugamushi strains: Karp and Gilliam in a rhesus macaque (Macaca mulatta) model.

[This corrects the article DOI: 10.1371/journal.pntd.0010611.].

A time-course comparative clinical and immune response evaluation study between the human pathogenic Orientia tsutsugamushi strains: Karp and Gilliam in a rhesus macaque (Macaca mulatta) model.

BACKGROUND: Scrub typhus is a vector-borne febrile illness caused by Orientia tsutsugamushi transmitted by the bite of Trombiculid mites. O. tsutsugamushi has a high genetic diversity and is increasingly recognized to have a wider global distribution than previously assumed. METHODOLOGY/PRINCIPLE FINDINGS: We evaluated the clinical outcomes and host immune responses of the two most relevant human pathogenic strains of O. tsutsugamushi; Karp (n = 4) and Gilliam (n = 4) in a time-course study over 80 days post infection (dpi) in a standardized scrub typhus non-human primate rhesus macaque model. We observed distinct features in clinical progression and immune response between the two strains; Gilliam-infected macaques developed more pronounced systemic infection characterized by an earlier onset of bacteremia, lymph node enlargement, eschar lesions and higher inflammatory markers during the acute phase of infection, when compared to the Karp strain. C-reactive protein (CRP) plasma levels, interferon gamma (IFN-γ, interleukin-1 receptor antagonist (IL-1ra), IL-15 serum concentrations, CRP/IL10- and IFN-γ/IL-10 ratios correlated positively with bacterial load in blood, implying activation of the innate immune response and preferential development of a T helper-type 1 immune response. The O. tsutsugamushi-specific immune memory responses in cells isolated from skin and lymph nodes at 80 dpi were more markedly elevated in the Gilliam-infected macaques than in the Karp-infected group. The comparative cytokine response dynamics of both strains revealed significant up-regulation of IFN-γ, tumor necrosis factor (TNF), IL-15, IL-6, IL-18, regulatory IL-1ra, IL-10, IL-8 and granulocyte-colony-stimulating factor (G-CSF). These data suggest that the clinical outcomes and host immune responses to scrub typhus could be associated with counter balancing effects of pro- and anti-inflammatory cytokine-mediated responses. Currently, no data on characterized time-course comparisons of O. tsutsugamushi strains regarding measures of disease severity and immune response is available. Our study provides evidence for the strain-specificity of host responses in scrub typhus, which supports our understanding of processes at the initial inoculation site (eschar), systemic disease progression, protective and/or pathogenic host immune mechanisms and cellular immune memory function. CONCLUSIONS/SIGNIFICANCE: This study characterised an improved intradermal rhesus macaque challenge model for scrub typhus, whereby the Gilliam strain infection associated with higher disease severity in the rhesus macaque model than the previous Karp strain infection. Difficulties associated with inoculum quantitation for obligate-intracellular bacteria were overcome by using functional inoculum titrations in outbred mice. The Gilliam-based rhesus macaque model provides improved endpoint measurements and contributes towards the identification of correlates of protection for future vaccine development.

Dynamic QTL-based ecophysiological models to predict phenotype from genotype and environment data.

BACKGROUND: Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework. RESULTS: We describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype, Environment, and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model. CONCLUSIONS: The dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.

Nationwide Seroprevalence of Scrub Typhus, Typhus, and Spotted Fever in Young Thai Men.

Scrub typhus group (STG), typhus group (TG), and spotted fever group (SFG) rickettsiae are pathogens distributed worldwide and are important causes of febrile illnesses in southeast Asia. The levels of rickettsioses burden and distribution in Thai communities are still unclear. Nonspecific symptoms, limit diagnostic capacity and underdiagnoses contribute to the absence of clarity. The objective of this study was to determine the nationwide IgG seroprevalence of STG, TG, and SFG by ELISA in repository sera from the Royal Thai Army recruits collected during 2007-2008 and 2012 to estimate rickettsiae exposure in young Thai men to better understand rickettsiae exposure distribution in the Thai population. IgG seroprevalence of STG, Orientia tsutsugamushi; TG, Rickettsia typhi; and SFG, R. rickettsii was 12.4%, 6.8%, and 3.3% in 2007-2008 and 31.8%, 4.2%, and 4.5% in 2012, respectively. The STG had the highest seroprevalence of Rickettsia assessed, with the highest regional seroprevalence found in southern Thailand. The STG seroprevalence changed significantly from 2007 to 2008 (P value < 0.05), which corresponds with morbidity rate of scrub typhus from the last decade in Thailand. We were unable to determine the causality for seroprevalence changes between the two periods due to the limitation in sample numbers for intervening years and limited information available for archived specimens. Additional research would be required to determine agency. However, study results do confirm Rickettsia endemicity in Thailand lends weight to reports of increasing STG seroprevalence. It also corroborates the need to raise rickettsial disease awareness and educate the general public in prevention measures.

Towards a multiscale crop modelling framework for climate change adaptation assessment.

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.

Coexistence Of Plasmid-Mediated mcr-1 And bla NDM-4 Genes In A Klebsiella pneumoniae Clinical Strain In Vietnam.

In this study, we characterized the first clinical Klebsiella pneumoniae strain co- harboring mcr-1 and bla NDM-4 genes in Vietnam, which was recovered from a patient admitted to hospital in 2015. This strain demonstrated nonsusceptible to all tested antibiotics, including last-line antibiotics such as carbapenems (MICs ≥128 µg/mL) and colistin (MIC =32 µg/mL), except tigecycline (MIC =1 µg/mL). Whole-genome analysis using both MinION and MiSeq data revealed that the strain carried 29 resistance genes. Particularly, mcr-1 and bla NDM-4 genes were carried by different self-conjugative plasmids and able to be transferred to a recipient by conjugation. The colistin resistance of this strain was conferred by mcr-1 and additional chromosomal resistance determinants. Eight amino acid substitutions found in PmrA, PmrB, PmrC, PmrI, and PmrJ, all proteins that are involved in lipopolysaccharide modifications, may be associated with chromosomal colistin resistance. The accumulation of multiple antibiotic resistance mechanisms in this clinical isolate raises alarm on potential spread of extensively drug-resistant K. pneumoniae in healthcare settings.

Complex Class 1 Integron in a Clinical Escherichia coli Strain From Vietnam Carrying Both mcr-1 and bla NDM-1.

The co-production of MCR and carbapenemase in Enterobacteriaceae has been previously reported. Here, we describe a clinical strain of Escherichia coli from Vietnam carrying both mcr-1 and bla NDM-1. Whole-genome sequencing showed that the genome of this strain consists of a 4,975,832-bp chromosome and four plasmids. The mcr-1 and bla NDM-1 genes are located on IncI2 and IncA/C2-type plasmids, respectively. Genetic analysis revealed the presence of a multidrug-resistant region with the structure of a novel complex class 1 integron including a class 1 integron region bearing two 5' conserved segments and one 3' conserved segment and two complete structures of ISCR1. The complex integron contains aminoglycoside resistance genes aadA2, aadB, strA, strB, and aphA6, quinolone resistance gene qnrA1, extended-spectrum ß-lactamase gene bla OXA- 4, and a Tn125-like transposon bearing bla NDM-1. In addition, the dfrA12-gcuF-aadA2-cmlA1-aadA1-qacH gene cassette array belonging to the sul3-type integron was also identified, but the region found downstream of the gene cassette array is the IS440-tet(M)-IS26 element instead of the sul3 gene. The results further support that Enterobacteriaceae isolates co-harboring mcr and bla NDM are widely being distributed. The structural characteristics of the complex integron reveal that ISCR1 elements play an important role in the mobilization of bla NDM-1 and the development of multidrug-resistant regions.

Future irrigation expansion outweigh groundwater recharge gains from climate change in semi-arid India.

Simultaneous effects of future climate and irrigation intensification on surface and groundwater systems are not well understood. Efforts are needed to understand the future groundwater availability and associated surface flows under business-as-usual management to formulate policy changes to improve water sustainability. We combine measurements with integrated modeling (MIKE SHE/MIKE11) to evaluate the effects of future climate (2040-2069), with and without irrigation expansion, on water levels and flows in an agricultural watershed in low-storage crystalline aquifer region of south India. Demand and supply management changes, including improved efficiency of irrigation water as well as energy uses, were evaluated. Increased future rainfall (7-43%, from 5 Global Climate Models) with no further expansion of irrigation wells increased the groundwater recharge (10-55%); however, most of the recharge moved out of watershed as increased baseflow (17-154%) with a small increase in net recharge (+0.2mm/year). When increased rainfall was considered with projected increase in irrigation withdrawals, both hydrologic extremes of well drying and flooding were predicted. A 100-year flow event was predicted to be a 5-year event in the future. If irrigation expansion follows the historical trends, earlier and more frequent well drying, a source of farmers' distress in India, was predicted to worsen in the future despite the recharge gains from increased rainfall. Storage and use of excess flows, improved irrigation efficiency with flood to drip conversion in 25% of irrigated area, and reduced energy subsidy (free electricity for 3.5h compared to 7h/day; $1 billion savings) provided sufficient water savings to support future expansion in irrigated areas while mitigating well drying as well as flooding. Reductions in energy subsidy to fund the implementation of economically desirable (high benefit-cost ratio) demand (drip irrigation) and supply (water capture and storage) management was recommended to achieve a sustainable food-water-energy nexus in semi-arid regions.

Characterization of the rhesus macaque (Macaca mulatta) scrub typhus model: Susceptibility to intradermal challenge with the human pathogen Orientia tsutsugamushi Karp.

BACKGROUND: Scrub typhus is an important endemic disease in tropical Asia caused by Orientia tsutsugamushi for which no effective broadly protective vaccine is available. The successful evaluation of vaccine candidates requires well-characterized animal models and a better understanding of the immune response against O. tsutsugamushi. While many animal species have been used to study host immunity and vaccine responses in scrub typhus, only limited data exists in non-human primate (NHP) models. METHODOLOGY/PRINCIPLE FINDINGS: In this study we evaluated a NHP scrub typhus disease model based on intradermal inoculation of O. tsutsugamushi Karp strain in rhesus macaques (n = 7). After an intradermal inoculation with 106 murine LD50 of O. tsutsugamushi at the anterior thigh (n = 4) or mock inoculum (n = 3), a series of time course investigations involving hematological, biochemical, molecular and immunological assays were performed, until day 28, when tissues were collected for pathology and immunohistochemistry. In all NHPs with O. tsutsugamushi inoculation, but not with mock inoculation, the development of a classic eschar with central necrosis, regional lymphadenopathy, and elevation of body temperature was observed on days 7-21 post inoculation (pi); bacteremia was detected by qPCR on days 6-18 pi; and alteration of liver enzyme function and increase of white blood cells on day 14 pi. Immune assays demonstrated raised serum levels of soluble cell adhesion molecules, anti-O. tsutsugamushi-specific antibody responses (IgM and IgG) and pathogen-specific cell-mediated immune responses in inoculated macaques. The qPCR assays detected O. tsutsugamushi in eschar, spleen, draining and non-draining lymph nodes, and immuno-double staining demonstrated intracellular O. tsutsugamushi in antigen presenting cells of eschars and lymph nodes. CONCLUSIONS/SIGNIFICANCE: These data show the potential of using rhesus macaques as a scrub typhus model, for evaluation of correlates of protection in both natural and vaccine induced immunity, and support the evaluation of future vaccine candidates against scrub typhus.

A Predictive Model for Time-to-Flowering in the Common Bean Based on QTL and Environmental Variables.

The common bean is a tropical facultative short-day legume that is now grown in tropical and temperate zones. This observation underscores how domestication and modern breeding can change the adaptive phenology of a species. A key adaptive trait is the optimal timing of the transition from the vegetative to the reproductive stage. This trait is responsive to genetically controlled signal transduction pathways and local climatic cues. A comprehensive characterization of this trait can be started by assessing the quantitative contribution of the genetic and environmental factors, and their interactions. This study aimed to locate significant QTL (G) and environmental (E) factors controlling time-to-flower in the common bean, and to identify and measure G × E interactions. Phenotypic data were collected from a biparental [Andean × Mesoamerican] recombinant inbred population (F11:14, 188 genotypes) grown at five environmentally distinct sites. QTL analysis using a dense linkage map revealed 12 QTL, five of which showed significant interactions with the environment. Dissection of G × E interactions using a linear mixed-effect model revealed that temperature, solar radiation, and photoperiod play major roles in controlling common bean flowering time directly, and indirectly by modifying the effect of certain QTL. The model predicts flowering time across five sites with an adjusted r-square of 0.89 and root-mean square error of 2.52 d. The model provides the means to disentangle the environmental dependencies of complex traits, and presents an opportunity to identify in silico QTL allele combinations that could yield desired phenotypes under different climatic conditions.

Strong interferon-gamma mediated cellular immunity to scrub typhus demonstrated using a novel whole cell antigen ELISpot assay in rhesus macaques and humans.

Scrub typhus is a febrile infection caused by the obligate intracellular bacterium Orientia tsutsugamushi, which causes significant morbidity and mortality across the Asia-Pacific region. The control of this vector-borne disease is challenging due to humans being dead-end hosts, vertical maintenance of the pathogen in the vector itself, and a potentially large rodent reservoir of unclear significance, coupled with a lack of accurate diagnostic tests. Development of an effective vaccine is highly desirable. This however requires better characterization of the natural immune response of this neglected but important disease. Here we implement a novel IFN-γ ELISpot assay as a tool for studying O. tsutsugamushi induced cellular immune responses in an experimental scrub typhus rhesus macaque model and human populations. Whole cell antigen for O. tsutsugamushi (OT-WCA) was prepared by heat inactivation of Karp-strain bacteria. Rhesus macaques were infected intradermally with O. tsutsugamushi. Freshly isolated peripheral blood mononuclear cells (PBMC) from infected (n = 10) and uninfected animals (n = 5) were stimulated with OT-WCA, and IFN-γ secreting cells quantitated by ELISpot assay at five time points over 28 days. PBMC were then assayed from people in a scrub typhus-endemic region of Thailand (n = 105) and responses compared to those from a partially exposed population in a non-endemic region (n = 14), and to a naïve population in UK (n = 12). Mean results at Day 0 prior to O. tsutsugamushi infection were 12 (95% CI 0-25) and 15 (2-27) spot-forming cells (SFC)/106 PBMC for infected and control macaques respectively. Strong O. tsutsugamushi-specific IFN-γ responses were seen post infection, with ELISpot responses 20-fold higher than baseline at Day 7 (mean 235, 95% CI 200-270 SFC/106 PBMC), 105-fold higher at Day 14 (mean 1261, 95% CI 1,097-1,425 SFC/106 PBMC), 125-fold higher at Day 21 (mean 1,498, 95% CI 1,496-1,500 SFC/106 PBMC) and 118-fold higher at Day 28 (mean 1,416, 95% CI 1,306-1,527 SFC/106 PBMC). No significant change was found in the control group at any time point compared to baseline. Humans from a scrub typhus endemic region of Thailand had mean responses of 189 (95% CI 88-290) SFC/106 PBMC compared to mean responses of 40 (95% CI 9-71) SFC/106 PBMC in people from a non-endemic region and 3 (95% CI 0-7) SFC/106 PBMC in naïve controls. In summary, this highly sensitive assay will enable field immunogenicity studies and further characterization of the host response to O. tsutsugamushi, and provides a link between human and animal models to accelerate vaccine development.

Next generation agricultural system models and knowledge products: Synthesis and strategy.

The purpose of this Special Issue of Agricultural Systems is to lay the foundation for the next generation of agricultural systems data, models and knowledge products. In the Introduction to this Special Issue, we described a vision for accelerating the rate of agricultural innovation and meeting the growing global need for food and fiber. In this concluding article of the NextGen Special Issue we synthesize insights and formulate a strategy to advance data, models, and knowledge products that are consistent with this vision. This strategy is designed to facilitate a transition from the current, primarily supply-driven approach toward a more demand-driven approach that would address key Use Cases where better data, models and knowledge products are seen by end-users as essential to meet their needs.

Next generation agricultural system data, models and knowledge products: Introduction.

Agricultural system models have become important tools to provide predictive and assessment capability to a growing array of decision-makers in the private and public sectors. Despite ongoing research and model improvements, many of the agricultural models today are direct descendants of research investments initially made 30-40 years ago, and many of the major advances in data, information and communication technology (ICT) of the past decade have not been fully exploited. The purpose of this Special Issue of Agricultural Systems is to lay the foundation for the next generation of agricultural systems data, models and knowledge products. The Special Issue is based on a "NextGen" study led by the Agricultural Model Intercomparison and Improvement Project (AgMIP) with support from the Bill and Melinda Gates Foundation.

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.

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.

Towards a new generation of agricultural system data, models and knowledge products: Design and improvement.

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools.

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.

Development of a QTL-environment-based predictive model for node addition rate in common bean.

KEY MESSAGE: This work reports the effects of the genetic makeup, the environment and the genotype by environment interactions for node addition rate in an RIL population of common bean. This information was used to build a predictive model for node addition rate. To select a plant genotype that will thrive in targeted environments it is critical to understand the genotype by environment interaction (GEI). In this study, multi-environment QTL analysis was used to characterize node addition rate (NAR, node day- 1) on the main stem of the common bean (Phaseolus vulgaris L). This analysis was carried out with field data of 171 recombinant inbred lines that were grown at five sites (Florida, Puerto Rico, 2 sites in Colombia, and North Dakota). Four QTLs (Nar1, Nar2, Nar3 and Nar4) were identified, one of which had significant QTL by environment interactions (QEI), that is, Nar2 with temperature. Temperature was identified as the main environmental factor affecting NAR while day length and solar radiation played a minor role. Integration of sites as covariates into a QTL mixed site-effect model, and further replacing the site component with explanatory environmental covariates (i.e., temperature, day length and solar radiation) yielded a model that explained 73% of the phenotypic variation for NAR with root mean square error of 16.25% of the mean. The QTL consistency and stability was examined through a tenfold cross validation with different sets of genotypes and these four QTLs were always detected with 50-90% probability. The final model was evaluated using leave-one-site-out method to assess the influence of site on node addition rate. These analyses provided a quantitative measure of the effects on NAR of common beans exerted by the genetic makeup, the environment and their interactions.

An AgMIP framework for improved agricultural representation in IAMs.

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.