Boote, K. J., Jones, J. W., White, J. W., Asseng, S., & Lizaso, J. I. (2013). Putting mechanisms into crop production models: Putting mechanisms into crop production models. Plant Cell Environ, 36(9), 1658–1672.
Abstract: Crop growth models dynamically simulate processes of C, N and water balance on daily or hourly time-steps to predict crop growth and development and at season-end, final yield. Their ability to integrate effects of genetics, environment and crop management have led to applications ranging from understanding gene function to predicting potential impacts of climate change. The history of crop models is reviewed briefly, and their level of mechanistic detail for assimilation and respiration, ranging from hourly leaf-to-canopy assimilation to daily radiation-use efficiency is discussed. Crop models have improved steadily over the past 30–40 years, but much work remains. Improvements are needed for the prediction of transpiration response to elevated CO2 and high temperature effects on phenology and reproductive fertility, and simulation of root growth and nutrient uptake under stressful edaphic conditions. Mechanistic improvements are needed to better connect crop growth to genetics and to soil fertility, soil waterlogging and pest damage. Because crop models integrate multiple processes and consider impacts of environment and management, they have excellent potential for linking research from genomics and allied disciplines to crop responses at the field scale, thus providing a valuable tool for deciphering genotype by environment by management effects.
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Boote, K. J., Prasad, V., Allen Jr., L. H., Singh, P., & Jones, J. W. (2018). Modeling sensitivity of grain yield to elevated temperature in the DSSAT crop models for peanut, soybean, dry bean, chickpea, sorghum, and millet. European Journal of Agronomy, 100, 99–109.
Abstract: Crop models are increasingly being used as tools to simulate climate change effects or effects of virtual heat-tolerant cultivars; therefore it is important that upper temperature thresholds for seed-set, seed growth, phenology, and other processes affecting yield be developed and parameterized from elevated temperature experiments whether field or controlled-environment chambers. In this paper, we describe the status of crop models for dry bean (Phaseolus vulgaris L.), peanut (Arachis hypogaea L.), soybean (Glycine max L.), chickpea (Cicer arietinum L.), sorghum (Sorghum bicolor (L.) Moench), and millet (Pennisetum glaucum L. (R.) Br) in the Decision Support System for Agrotechnology Transfer (DSSAT) for response to elevated temperature by comparison to observed data, and we review where changes have been made or where needed changes remain. Temperature functions for phenology and photosynthesis of the CROPGRO-Dry Bean model were modified in 2006 for DSSAT V4.5, based on observed growth and yield of Montcalm cultivar grown in sunlit, controlled-environment chambers. Temperature functions for soybean and peanut models were evaluated against growth and yield data in the same chambers and found to adequately predict growth and yield, thus have not been modified since 1998 release of V3.5. The temperature functions for the chickpea model were substantially modified for many processes, and are updated for V4.6. The millet model was re-coded and modified for its temperature sensitivities, with a new function to allow the 8–10 day period prior to anthesis to affect grain set, as parameterized from field observations. For the sorghum model, the temperature effect on grain growth rate was modified to improve yield and grain size response to elevated temperature by comparison to data in controlled-environment chambers. For reliable assessments of climate change impact, it is critically important to gather additional temperature response data and to update parameterization and code of all crop models including DSSAT.
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Dzotsi, K. A., Basso, B., & Jones, J. W. (2013). Development, uncertainty and sensitivity analysis of the simple SALUS crop model in DSSAT. Ecological Modelling, 260, 62–76.
Abstract: Simplified approaches to modeling crop growth and development have recently received more attention due to increased interest in applying crop models at large scales for various agricultural assessments. In this study, we integrated the simple version of SALUS (System Approach to Land Use Sustainability) crop model in the widely-used Decision Support System for Agrotechnology Transfer (DSSAT) to enhance the capability of DSSAT to simulate additional crops without requiring detailed parameterization. An uncertainty and sensitivity analysis was conducted using the integrated DSSAT-simple SALUS model to assess the variability in model outputs and crop parameter ranking in response to uncertainties associated with crop parameters required by the model. The influence of year, production level, and location on the effect of crop parameter uncertainty was also investigated.
Parameter uncertainty resulted in a high variability in modeled outputs. Simulated potential aboveground biomass ranged from 1.2 t ha−1 to 38 t ha−1 for maize and 4 t ha−1 to 26.5 t ha−1 for peanut and cotton, all locations and years considered. The degree of variability was dependent upon the production level, the location, the year, and the crop. Ranking of crop parameters was not significantly affected by the year of study but was strongly related to the production level, location, and crop. The model was not sensitive to parameters related to prediction of the timing of germination and emergence. The most influential parameters were related to leaf area index growth, crop duration, and thermal time accumulation. Findings from this study contributed to understanding the effects of crop parameter uncertainty on the model's outputs under different environmental conditions.
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Fleisher, D. H., Condori, B., Quiroz, R., Alva, A., Asseng, S., Barreda, C., et al. (2017). A potato model intercomparison across varying climates and productivity levels. Glob Change Biol, 23(3), 1258–1281.
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Gbegbelegbe, S., Cammarano, D., Asseng, S., Robertson, R., Chung, U., Adam, M., et al. (2017). Baseline simulation for global wheat production with CIMMYT mega-environment specific cultivars. Field Crops Research, 202, 122–135.
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Her, Y. G., Boote, K. J., Migliaccio, K. W., Fraisse, C., Letson, D., Mbuya, O., et al. (2017). Climate change impacts and adaptation in Florida's agriculture. In E. P. Chassignet, J. W. Jones, V. Misra, & J. Obeysekera (Eds.), Florida's climate: Changes, variations, & impacts (pp. 235–267). Gainesville, FL: Florida Climate Institute.
Abstract: In this chapter, we describe Florida�s agriculture, the vulnerability of its crops and livestock to climate change and possible adaptation strategies. Much of Florida�s agricultural success is linked to its moderate climate, which allows vegetable and fruit crop production during the winter/spring season as well as the production of perennial crops such as citrus and sugarcane. In addition, there is a substantial livestock industry that uses the extensive perennial grasslands. While rising CO2 is generally beneficial to crop production but detrimental to nutritional quality, increase in temperature will cause mostly negative effects on yield. Florida�s agriculture faces additional challenges from climate change characterized by sea level rise and intensified extreme climate events, affecting land and irrigation water availability, livestock productivity and pest and disease pressure. New technologies and adaptation strategies are needed for sustainable agricultural production in Florida, including increased water and nutrient use efficiency in crops, crop and livestock breeding for heat stress, pest and disease resistance and reduced exposure of livestock to high temperature. Irrigation is a favored adaptation, but places an even greater burden or potential conflict between agriculture and community use of water resources.
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Raymundo, R., Asseng, S., Prassad, R., Kleinwechter, U., Concha, J., Condori, B., et al. (2017). Performance of the SUBSTOR-potato model across contrasting growing conditions. Field Crops Research, 202, 57–76.
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Ruane, A. C., Hudson, N. I., Asseng, S., Camarrano, D., Ewert, F., Martre, P., et al. (2016). Multi-wheat-model ensemble responses to interannual climate variability. Environmental Modelling & Software, 81, 86–101.
Abstract: We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R-2 <= 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.
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Sharda, V., Handyside, C., Chaves, B., McNider, R. T., & Hoogenboom, G. (2017). The Impact of Spatial Soil Variability on Simulation of Regional Maize Yield. Transactions of the ASABE, 60(6), 2137–2148.
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Wallach, D., Makowski, D., Jones, J. W., & Brun, F. (2014). Working with Dynamic Crop Models: Methods, Tools, and Examples for Agriculture and Environment, 2nd Edition. Elsevier/Academic Press.
Abstract: Detailed explanations and descriptions of methods for working with dynamic system models in crop and agricultural sciences, including real-world examples and computer code
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Wang, R., Bowling, L. C., Cherkauer, K. A., Cibin, R., Her, Y., & Chaubey, I. (2017). Biophysical and hydrological effects of future climate change including trends in CO2, in the St. Joseph River watershed, Eastern Corn Belt. Agricultural Water Management, 180, 280–296.
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White, J. W., Hunt, L. A., Boote, K. J., Jones, J. W., Koo, J., Kim, S., et al. (2013). Integrated description of agricultural field experiments and production: The ICASA Version 2.0 data standards. Computers and Electronics in Agriculture, 96, 1–12.
Abstract: Agricultural research increasingly seeks to quantify complex interactions of processes for a wide range of environmental conditions and crop management scenarios, leading to investigation where multiple sets of experimental data are examined using tools such as simulation and regression. The use of standard data formats for documenting experiments and modeling crop growth and development can facilitate exchanges of information and software, allowing researchers to focus on research per se rather than on converting and re-formatting data or trying to estimate or otherwise compensate for missing information. The standards developed by the International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) project and subsequently revised by the International Consortium for Agricultural Systems Applications (ICASA) were of considerable value for describing experiments. However, the resulting ICASA Version 1 standards did not consider important management practices such as tillage and use of mulches, lacked descriptors for certain soil and plant traits (especially related to nutrient levels), and contained minor logical inconsistencies. The ICASA standards have evolved to allow description of additional management practices and traits of soils and plants and to provide greater emphasis on standardizing vocabularies, clarifying relations among variables, and expanding formats beyond the original plain text file format. This paper provides an overview of the ICASA Version 2.0 standards. The foundation of the standards is a master list variables that is organized in a hierarchical arrangement with major separations among descriptions of management practices or treatments, environmental conditions (soil and weather data), and measurements of crop responses. The plain text implementation is described in detail. Implementations in other digital formats (databases, spreadsheets, and data interchange formats) are also reviewed. Areas for further improvement and development are noted, particularly as related to describing pest damage, data quality and appropriate use of datasets. The master variable list and sample files are provided as electronic supplements.
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