The DOI matches its Crossref record; 210 of the 216 references it cites were found in a registry or an index.
The DSSAT cropping system model
Abstract
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The Cropping System Model (CSM)-CROPGRO-Cotton model (Jones et al., 2003; Pathak et al., 2007) is implemented in the Decision Support System for Agrotechnology Transfer (DSSAT; Hoogenboom et al., 2012).”
From Development and Application of Process-based Simulation Models for Cotton Production: A Review of Past, Present, and Future Directions (2014) · cites this paper as Jones et al., 2003
In the citing paper
“Notably process-based models have previously been used to evaluate the growth, development and yield of annual and perennial crops under a wide range of conditions (Jones et al., 2003;Keating et al., 2003;Stöckle et al., 2003), including climate change projection across the globe (White et al., 2011;Asseng et al., 2013).”
From Intensify production, transform biomass to energy and novel goods and protect soils in Europe—A vision how to mobilize marginal lands (2018) · cites this paper as Jones et al., 2003
Citations
Cites
Crop evapotranspiration : guidelines for computing crop water requirements
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998) · FAO eBooks · Cited by 20,540
On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters
Priestley, C. H. B., & Taylor, R. J. (1972) · Monthly Weather Review · doi:10.1175/1520-0493(1972)100 · Cited by 6,545
Model for predicting evaporation from a row crop with incomplete cover
Ritchie, J. T. (1972) · Water Resources Research · doi:10.1029/wr008i005p01204 · Cited by 2,240
Self-citationGuidelines for predicting crop water requirements
Doorenbos, J. (1977) · Medical Entomology and Zoology · Cited by 2,226
Dynamics of C, N, P and S in grassland soils: a model
Parton, W. J., Stewart, J. W., & Cole, C. V. (1988) · Biogeochemistry · doi:10.1007/bf02180320 · Cited by 1,483
Palmer, R. A. (1953) · Science · doi:10.1126/science.118.3075.3 · Cited by 1,354
Stochastic simulation of daily precipitation, temperature, and solar radiation
Richardson, C. W. (1981) · Water Resources Research · doi:10.1029/wr017i001p00182 · Cited by 1,299
A Modeling Approach to Determining the Relationship Between Erosion and Soil Productivity
Williams, J. R., Jones, C., & Dyke, P. T. (1984) · Transactions of the ASAE · doi:10.13031/2013.32748 · Cited by 1,145
CERES-Maize: a simulation model of maize growth and development
Jones, C., Kiniry, J. R., & Dyke, P. T. (1986) · Cited by 1,109
McCown, R. L., Hammer, G., Hargreaves, J. N. G., Holzworth, D. P., & Freebairn, D. M. (1996) · Agricultural Systems · doi:10.1016/0308-521x(94)00055-v · Cited by 967
Research works printed but not linked: 9
- Boote, K.J., Bennett, J.M., Jones, J.W., Jowers, H.E., 1989. On-farm testing of peanut and soybean models in Florida. ASAE P No. 89-4040. Am. Soc. Agr. Engr. (ASAE), St. Joseph, MI, pp. 54.
- Bowen, W.T., Jones, J.W., Thornton P.K., 1992. Crop simulation as a potential tool for evaluating sustainable land management. Utilization of Soil Survey Information for Sustainable Land Use Proceedings: 11–24 July.
- Giraldo, 1998, Adaptation of the DSSAT model for simulation of Brachiaria decumbens production, Pasturas Tropicales, 20, 2
- Hunkár, 1994, Validation of crop simulation model CERES-Maize, Quarterly Journal of Hungarian Meteorology Series, 98, 37
- Jones J.W. Ritchie, J.T., 1991. Crop growth models In: Hoffman, G.J, Howell, T.A., Solomon, K.H. (Eds.), Management of Farm Irrigation Systems, American Society for Agricultural Engineering, pp. 63–89.
- Jones, J.W., Boote, K.J., Hoogenboom, G., Jagtap, S.S., Wilkerson, G.G., 1989. SOYGRO V5.42, Soybean Crop Growth Simulation Model. User's Guide. Fl. Agric. Exp. Sta., Journal No. 8304. University of Florida, Gainesville. pp. 53.
- Paz, 2001, Estimating potential economic return for variable rate management in soybeans, Trans. ASAE, 44, 1335
- Singh, 1999, Soybean (Glycine max) yield prediction from current and historical weather data using CROPGRO model, Indian Journal of Agricultural Sciences, 69, 639
- Zalud, 2001, Estimation of winter wheat nitrogen stress using the CERES crop model, Rostlinna Vyroba, 47, 253
Cited by
Sorted by citations
Rising temperatures reduce global wheat production
Asseng, S., Ewert, F., Martre, P., Rötter, R. P., Lobell, D. B., Cammarano, D., Kimball, B. A., Ottman, M. J., Wall, G. W., White, J. W., et al. (2015) · Nature Climate Change · doi:10.1038/nclimate2470 · Cited by 2,491
Self-citationRosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C., Arneth, A., Boote, K. J., Folberth, C., Glotter, M., Khabarov, N., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1222463110 · Cited by 2,319
Self-citationRemote sensing for agricultural applications: A meta-review
Weiss, M., Jacob, F., & Duveiller, G. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111402 · Cited by 1,948
Steduto, P., Hsiao, T. C., Raes, D., & Fereres, E. (2009) · Agronomy Journal · doi:10.2134/agronj2008.0139s · Cited by 1,878
APSIM – evolution towards a new generation of agricultural systems simulation
Holzworth, D. P., Huth, N. I., deVoil, P. G., Zurcher, E. J., Herrmann, N. I., McLean, G., Chenu, K., van Oosterom, E. J., Snow, V., Murphy, C., et al. (2014) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2014.07.009 · Cited by 1,756
Yield gap analysis with local to global relevance—A review
van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P., & Hochman, Z. (2013) · Field Crops Research · doi:10.1016/j.fcr.2012.09.009 · Cited by 1,652
Resilience in Agriculture through Crop Diversification: Adaptive Management for Environmental Change
Lin, B. B. (2011) · BioScience · doi:10.1525/bio.2011.61.3.4 · Cited by 1,570
Crop Yield Gaps: Their Importance, Magnitudes, and Causes
Lobell, D. B., Cassman, K. G., & Field, C. B. (2009) · Annual Review of Environment and Resources · doi:10.1146/annurev.environ.041008.093740 · Cited by 1,606
Herrero, M., Havlík, P., Valin, H., Notenbaert, A., Rufino, M. C., Thornton, P. K., Blümmel, M., Weiss, F., Grace, D., & Obersteiner, M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1308149110 · Cited by 1,254
Elliott, J. W., Deryng, D., Müller, C., Frieler, K., Konzmann, M., Gerten, D., Glotter, M. J., Flörke, M., Wada, Y., Best, N., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1222474110 · Cited by 1,198
Thornton, P. K., van de Steeg, J., Notenbaert, A. M. O., & Herrero, M. (2009) · Agricultural Systems · doi:10.1016/j.agsy.2009.05.002 · Cited by 1,112
The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies
Rosenzweig, C. E., Jones, J. W., Hatfield, J. L., Ruane, A. C., Boote, K. J., Thorburn, P. J., Antle, J. M., Nelson, G. C., Porter, C. H., Janssen, S. J. C., et al. (2012) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2012.09.011 · Cited by 1,096
Self-citationOn the use of statistical models to predict crop yield responses to climate change
Lobell, D. B., & Burke, M. (2010) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2010.07.008 · Cited by 1,040
Crop evapotranspiration estimation with FAO56: Past and future
Pereira, L. S., Allen, R. G., Smith, M., & Raes, D. (2014) · Agricultural Water Management · doi:10.1016/j.agwat.2014.07.031 · Cited by 929
Decision support systems for agriculture 4.0: Survey and challenges
Zhai, Z., Martínez-Ortega, J.-F., Beltrán, V., & Martínez, N. L. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105256 · Cited by 936
Zeng, L., Wardlow, B. D., Xiang, D., Hu, S., & Li, D. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111511 · Cited by 872
Climate change : Impact on agriculture and costs of adaptation
Nelson, G. C., Rosegrant, M., Koo, J., Robertson, R., Sulser, T., Zhu, T., Ringler, C., Msangi, S., Palazzo, A., Batka, M., et al. (2009) · International Food Policy Research Institute eBooks · doi:10.2499/089629535 · Cited by 855
Modeling Soil Processes: Review, Key Challenges, and New Perspectives
Vereecken, H., Schnepf, A., Hopmans, J. W., Javaux, M., Or, D., Roose, T., Vanderborght, J., Young, M. H., Amelung, W., Aitkenhead, M. J., et al. (2016) · Vadose Zone Journal · doi:10.2136/vzj2015.09.0131 · Cited by 895
Leng, G., & Hall, J. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2018.10.434 · Cited by 729
Brief history of agricultural systems modeling
Jones, J. W., Antle, J. M., Basso, B., Boote, K. J., Conant, R. T., Foster, I. T., Godfray, H. C. J., Herrero, M., Howitt, R. E., Janssen, S. J. C., et al. (2016) · Agricultural Systems · doi:10.1016/j.agsy.2016.05.014 · Cited by 775
Self-citationHsiao, T. C., Heng, L., Steduto, P., Rojas-Lara, B. A., Raes, D., & Fereres, E. (2009) · Agronomy Journal · doi:10.2134/agronj2008.0218s · Cited by 696
On approaches and applications of the Wageningen crop models
van Ittersum, M. K., Leffelaar, P. A., van Keulen, H., Kropff, M. J., Bastiaans, L., & Goudriaan, J. (2002) · European Journal of Agronomy · doi:10.1016/s1161-0301(02)00106-5 · Cited by 697
Climate change and challenges of water and food security
Misra, A. K. (2014) · International Journal of Sustainable Built Environment · doi:10.1016/j.ijsbe.2014.04.006 · Cited by 645
Evolutionary aspects of the trade-off between seed size and number in crops
Sadras, V. O. (2006) · Field Crops Research · doi:10.1016/j.fcr.2006.07.004 · Cited by 589
On the Spatio-Temporal Dynamics of Soil Moisture at the Field Scale
Vereecken, H., Huisman, J. A., Pachepsky, Y., Montzka, C., van der Kruk, J., Bogena, H., Weihermüller, L., Herbst, M., Martinez, G., & Vanderborght, J. (2014) · Journal of Hydrology · doi:10.1016/j.jhydrol.2013.11.061 · Cited by 584
Daily gridded meteorological variables in Brazil (1980–2013)
Xavier, A. C., King, C. W., & Scanlon, B. R. (2015) · International Journal of Climatology · doi:10.1002/joc.4518 · Cited by 584
A review of data assimilation of remote sensing and crop models
Jin, X., Kumar, L., Li, Z., Feng, H., Xu, X., Yang, G., & Wang, J. (2017) · European Journal of Agronomy · doi:10.1016/j.eja.2017.11.002 · Cited by 590
Risk of increased food insecurity under stringent global climate change mitigation policy
Hasegawa, T., Fujimori, S., Havlík, P., Valin, H., Bodirsky, B. L., Doelman, J. C., Fellmann, T., Kyle, P., Koopman, J. F. L., Lotze-Campen, H., et al. (2018) · Nature Climate Change · doi:10.1038/s41558-018-0230-x · Cited by 564
Multimodel ensembles of wheat growth: many models are better than one
Martre, P., Wallach, D., Asseng, S., Ewert, F., Jones, J. W., Rötter, R., Boote, K., Ruane, A., Thorburn, P., Cammarano, D., et al. (2015) · Global Change Biology · doi:10.1111/gcb.12768 · Cited by 511
Self-citationSimulating the impact of extreme heat and frost events on wheat crop production: A review
Barlow, K. M., Christy, B. P., O’Leary, G. J., Riffkin, P. A., & Nuttall, J. G. (2015) · Field Crops Research · doi:10.1016/j.fcr.2014.11.010 · Cited by 546
Global and regional health effects of future food production under climate change: a modelling study
Springmann, M., Mason-D’Croz, D., Robinson, S., Garnett, T., Godfray, H. C. J., Gollin, D., Rayner, M., Ballón, P., & Scarborough, P. (2016) · The Lancet · doi:10.1016/s0140-6736(15)01156-3 · Cited by 566
Karthikeyan, L., Chawla, I., & Mishra, A. K. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124905 · Cited by 527
Consistent negative response of US crops to high temperatures in observations and crop models
Schauberger, B., Archontoulis, S. V., Arneth, A., Balkovič, J., Ciais, P., Deryng, D., Elliott, J. W., Folberth, C., Khabarov, N., Müller, C., et al. (2017) · Nature Communications · doi:10.1038/ncomms13931 · Cited by 547
Palosuo, T., Kersebaum, K. C., Angulo, C., Hlavinka, P., Moriondo, M., Olesen, J. E., Patil, R. H., Ruget, F., Rumbaur, C., Takáč, J., et al. (2011) · European Journal of Agronomy · doi:10.1016/j.eja.2011.05.001 · Cited by 512
25 years of the WOFOST cropping systems model
de Wit, A. J. W., Boogaard, H., Fumagalli, D., Janssen, S. J. C., Knapen, R., van Kraalingen, D. W. G., Supit, I., van der Wijngaart, R., & van Diepen, K. (2018) · Agricultural Systems · doi:10.1016/j.agsy.2018.06.018 · Cited by 504
Assimilation of remote sensing into crop growth models: Current status and perspectives
Huang, J., Gomez-Dans, J. L., Huang, H., Ma, H., Wu, Q., Lewis, P., Liang, S., Chen, Z., Xue, J., Wu, Y., et al. (2019) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2019.06.008 · Cited by 518
Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery
Feng Gao, A., Anderson, M. C., Xiaoyang Zhang, A., Yang, Z., Alfieri, J. G., Kustas, W. P., Mueller, R., Johnson, D. M., & Prueger, J. H. (2016) · Remote Sensing of Environment · doi:10.1016/j.rse.2016.11.004 · Cited by 496
Jones, J. W., Antle, J. M., Basso, B., Boote, K. J., Conant, R. T., Foster, I. T., Godfray, H. C. J., Herrero, M., Howitt, R. E., Janssen, S. J. C., et al. (2016) · Agricultural Systems · doi:10.1016/j.agsy.2016.09.021 · Cited by 476
Self-citationClimate change impacts and adaptation in South Africa
Ziervogel, G., New, M. G., Archer, E. R. M., Midgley, G. F., Taylor, A., Hamann, R., Stuart‐Hill, S., Myers, J. E., & Warburton, M. L. (2014) · Wiley Interdisciplinary Reviews Climate Change · doi:10.1002/wcc.295 · Cited by 455
Ines, A. V. M., Das, N. N., Hansen, J., & Njoku, E. G. (2013) · Remote Sensing of Environment · doi:10.1016/j.rse.2013.07.018 · Cited by 443
Huang, J., Tian, L., Liang, S., Ma, H., Becker‐Reshef, I., Huang, Y., Su, W., Zhang, X., Zhu, D., & Wu, W. (2015) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2015.02.001 · Cited by 429
Modelling the impacts of pests and diseases on agricultural systems
Donatelli, M., Magarey, R. D., Bregaglio, S., Willocquet, L., Whish, J. P. M., & Savary, S. (2017) · Agricultural Systems · doi:10.1016/j.agsy.2017.01.019 · Cited by 425
Spatial variation of crop yield response to climate change in East Africa
Thornton, P. K., Jones, P. G., Alagarswamy, G., & Andresen, J. (2008) · Global Environmental Change · doi:10.1016/j.gloenvcha.2008.08.005 · Cited by 423
Sahoo, S., Russo, T. A., Elliott, J. W., & Foster, I. T. (2017) · Water Resources Research · doi:10.1002/2016wr019933 · Cited by 428
nasapower: A NASA POWER Global Meteorology, Surface Solar Energy and Climatology Data Client for R
Sparks, A. (2018) · The Journal of Open Source Software · doi:10.21105/joss.01035 · Cited by 422
A review on monitoring and advanced control strategies for precision irrigation
Abioye, A. E., Abidin, M. S. Z., Mahmud, M. S. A., Buyamin, S., Ishak, M. H. I., Rahman, M. K. I. A., Otuoze, A. O., Onotu, P., & Ramli, M. S. A. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105441 · Cited by 440
Is Cassava the Answer to African Climate Change Adaptation?
Jarvis, A., Ramírez-Villegas, J., Campo, B. V. H., & Racines, C. E. N. (2012) · Tropical Plant Biology · doi:10.1007/s12042-012-9096-7 · Cited by 430
Estimating crop yield potential at regional to national scales
Wart, J. V., Kersebaum, K. C., Peng, S., Milner, M., & Cassman, K. G. (2013) · Field Crops Research · doi:10.1016/j.fcr.2012.11.018 · Cited by 405
Seed biology and yield of grain crops
Egli, D. B. (2017) · CABI eBooks · doi:10.1079/9781780647708.0000 · Cited by 401
Hybrid-maize—a maize simulation model that combines two crop modeling approaches
Yang, H. S., Dobermann, A., Lindquist, J. L., Walters, D. T., Arkebauer, T. J., & Cassman, K. G. (2004) · Field Crops Research · doi:10.1016/j.fcr.2003.10.003 · Cited by 390
Grassini, P., van Bussel, L. G. J., Wart, J. V., Wolf, J., Claessens, L., Yang, H. S., Boogaard, H., de Groot, H., van Ittersum, M. K., & Cassman, K. G. (2015) · Field Crops Research · doi:10.1016/j.fcr.2015.03.004 · Cited by 380
Using the Soil and Water Assessment Tool (SWAT) to model ecosystem services: A systematic review
Francesconi, W., Srinivasan, R., Pérez-Miñana, E., Willcock, S., & Quintero, M. (2016) · Journal of Hydrology · doi:10.1016/j.jhydrol.2016.01.034 · Cited by 389
Comparing estimates of climate change impacts from process-based and statistical crop models
Lobell, D., & Asseng, S. (2017) · Environmental Research Letters · doi:10.1088/1748-9326/aa518a · Cited by 351
Yang, J., Yang, J., Liu, S., & Hoogenboom, G. (2014) · Agricultural Systems · doi:10.1016/j.agsy.2014.01.008 · Cited by 359
Self-citationGlobal Gridded Crop Model evaluation: benchmarking, skills, deficiencies and implications
Müller, C., Elliott, J., Chryssanthacopoulos, J., Arneth, A., Balkovič, J., Ciais, P., Deryng, D., Folberth, C., Glotter, M., Hoek, S., et al. (2016) · Geoscientific model development · doi:10.5194/gmd-10-1403-2017 · Cited by 344
Rötter, R., Palosuo, T., Kersebaum, K., Angulo, C., Bindi, M., Ewert, F., Ferrise, R., Hlavinka, P., Moriondo, M., Nendel, C., et al. (2012) · Field Crops Research · doi:10.1016/j.fcr.2012.03.016 · Cited by 338
Evaluating agricultural trade-offs in the age of sustainable development
Kanter, D., Musumba, M., Wood, S. L. R., Palm, C. A., Antle, J. M., Balvanera, P., Dale, V. H., Havlík, P., Kline, K. L., Scholes, R. J., et al. (2016) · Agricultural Systems · doi:10.1016/j.agsy.2016.09.010 · Cited by 384
Nutrient acquisition from arable subsoils in temperate climates: A review
Kautz, T., Amelung, W., Ewert, F. A., Gaiser, T., Horn, R., Jahn, R., Javaux, M., Kemna, A., Kuzyakov, Y. V., Munch, J.-C., et al. (2012) · Soil Biology and Biochemistry · doi:10.1016/j.soilbio.2012.09.014 · Cited by 364
The uncertainty of crop yield projections is reduced by improved temperature response functions
Wang, E., Martre, P., Zhao, Z., Ewert, F. A., Maiorano, A., Rötter, R. P., Kimball, B. A., Ottman, M. J., Wall, G. W., White, J. W., et al. (2017) · Nature Plants · doi:10.1038/nplants.2017.102 · Cited by 330
Self-citationCrop modelling for integrated assessment of risk to food production from climate change
Ewert, F. A., Rötter, R. P., Bindi, M., Webber, H. A., Trnka, M., Kersebaum, K. C., Olesen, J. E., van Ittersum, M. K., Janssen, S. J. C., Rivington, M., et al. (2014) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2014.12.003 · Cited by 334
Contribution of Crop Models to Adaptation in Wheat.
Chenu, K., Porter, J., Martre, P., Basso, B., Chapman, S., Ewert, F., Bindi, M., & Asseng, S. (2017) · Trends in Plant Science · doi:10.1016/j.tplants.2017.02.003 · Cited by 262
Xiang, K., Li, Y., Horton, R., & Feng, H. (2020) · Agricultural Water Management · doi:10.1016/j.agwat.2020.106043 · Cited by 334
Dente, L., Satalino, G., Mattia, F., & Rinaldi, M. (2007) · Remote Sensing of Environment · doi:10.1016/j.rse.2007.05.023 · Cited by 309
Land-use change trajectories up to 2050: insights from a global agro-economic model comparison
Schmitz, C., van Meijl, H., Kyle, P., Nelson, G. C., Fujimori, S., Gurgel, A., Havlík, P., Heyhoe, E., Mason-D’Croz, D., Popp, A., et al. (2013) · Agricultural Economics · doi:10.1111/agec.12090 · Cited by 309
Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China
Han, J., Zhang, Z., Cao, J., Luo, Y., Zhang, L., Li, Z., & Zhang, J. (2020) · Remote Sensing · doi:10.3390/rs12020236 · Cited by 323
Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions
Bouman, B. A. M., & van Laar, H. H. (2005) · Agricultural Systems · doi:10.1016/j.agsy.2004.09.011 · Cited by 305
Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation
Adeyemi, O., Grove, I. G., Peets, S., & Norton, T. (2017) · Sustainability · doi:10.3390/su9030353 · Cited by 303
Vicente-Serrano, S. M., McVicar, T. R., Miralles, D. G., Yang, Y., & Tomàs‐Burguera, M. (2019) · Wiley Interdisciplinary Reviews Climate Change · doi:10.1002/wcc.632 · Cited by 328
Filippi, P., Jones, E. J., Wimalathunge, N. S., Somarathna, P. D. S. N., Pozza, L. E., Ugbaje, S. U., Jephcott, T. G., Paterson, S., Whelan, B. M., & Bishop, T. F. A. (2019) · Precision Agriculture · doi:10.1007/s11119-018-09628-4 · Cited by 297
Assessment across the United States of the Benefits of Altered Soybean Drought Traits
Sinclair, T. R., Messina, C. D., Beatty, A., & Samples, M. (2010) · Agronomy Journal · doi:10.2134/agronj2009.0195 · Cited by 296
The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0)
Elliott, J. W., Müller, C., Deryng, D., Chryssanthacopoulos, J. P., Boote, K. J., Büchner, M., Foster, I. T., Glotter, M. J., Heinke, J., Iizumi, T., et al. (2015) · Geoscientific model development · doi:10.5194/gmd-8-261-2015 · Cited by 311
Self-citationAn agent-based simulation model of human–environment interactions in agricultural systems
Schreinemachers, P., & Berger, T. (2011) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2011.02.004 · Cited by 295
Current warming will reduce yields unless maize breeding and seed systems adapt immediately
Challinor, A. J., Koehler, A.-K., Ramirez-Villegas, J., Whitfield, S., & Das, B. (2016) · Nature Climate Change · doi:10.1038/nclimate3061 · Cited by 292
Integrated phenology and climate in rice yields prediction using machine learning methods
Guo, Y., Fu, Y. H., Hao, F., Zhang, X., Wu, W., Jin, X., Bryant, C. R., & Senthilnath, J. (2020) · Ecological Indicators · doi:10.1016/j.ecolind.2020.106935 · Cited by 315
Berger, K., Machwitz, M., Kycko, M., Kefauver, S. C., Wittenberghe, S. V., Gerhards, M., Verrelst, J., Atzberger, C., van der Tol, C., Damm, A., et al. (2022) · Remote Sensing of Environment · doi:10.1016/j.rse.2022.113198 · Cited by 292
Soler, C. M. T., Sentelhas, P. C., & Hoogenboom, G. (2007) · European Journal of Agronomy · doi:10.1016/j.eja.2007.03.002 · Cited by 284
Self-citationTripathi, A. K., Tripathi, D. K., Chauhan, D. K., Kumar, N., & Singh, G. S. (2015) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2015.09.034 · Cited by 295
Deryng, D., Elliott, J. W., Folberth, C., Müller, C., Pugh, T. A. M., Boote, K. J., Conway, D., Ruane, A. C., Gerten, D., Jones, J. W., et al. (2016) · Nature Climate Change · doi:10.1038/nclimate2995 · Cited by 288
Self-citationPerformance of CERES-Rice and CERES-Wheat models in rice–wheat systems: A review
Timsina, J., & Humphreys, E. (2006) · Agricultural Systems · doi:10.1016/j.agsy.2005.11.007 · Cited by 273
Läderach, P., Martinez-Valle, A. I., Schroth, G., & de Castro, N. (2013) · Climatic Change · doi:10.1007/s10584-013-0774-8 · Cited by 273
Paustian, K. H., Collier, S. M., Baldock, J., Burgess, R., Creque, J., DeLonge, M. S., Dungait, J. A. J., Ellert, B. H., Frank, S., Goddard, T. W., et al. (2019) · Carbon Management · doi:10.1080/17583004.2019.1633231 · Cited by 302
Todorović, M., Albrizio, R., Životić, L., Saab, M. T. A., Stöckle, C., & Steduto, P. (2009) · Agronomy Journal · doi:10.2134/agronj2008.0166s · Cited by 272
Agriculture and climate change in global scenarios: why don't the models agree
Nelson, G. C., van der Mensbrugghe, D., Ahammad, H., Blanc, É., Calvin, K., Hasegawa, T., Havlík, P., Heyhoe, E., Kyle, P. G., Lotze‐Campen, H., et al. (2013) · Agricultural Economics · doi:10.1111/agec.12091 · Cited by 287
Evaluation of the APSIM model in cropping systems of Asia
Gaydon, D. S., Singh, B., Wang, E., Poulton, P. L., Ahmad, B., Ahmed, F., Akhter, S., Ali, I., Amarasingha, R. P. R. K., Chaki, A. K., et al. (2017) · Field Crops Research · doi:10.1016/j.fcr.2016.12.015 · Cited by 277
Contribution of Remote Sensing on Crop Models: A Review
Kasampalis, D. A., Alexandridis, T., Deva, C. R., Challinor, A. J., Moshou, D., & Zalidis, G. C. (2018) · Journal of Imaging · doi:10.3390/jimaging4040052 · Cited by 269
Sensing Soil Properties in the Laboratory, In Situ, and On-Line
Kuang, B., Mahmood, H. S., Quraishi, M. Z., Hoogmoed, W. B., Mouazen, A. M., & Henten, E. J. V. (2012) · Advances in agronomy · doi:10.1016/b978-0-12-394275-3.00003-1 · Cited by 268
Baret, F., Houlès, V., & GUERIF, M. O. (2006) · Journal of Experimental Botany · doi:10.1093/jxb/erl231 · Cited by 267
Assessment of the quality of meta-analysis in agronomy
Philibert, A., Loyce, C., & Makowski, D. (2011) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2011.12.003 · Cited by 272
Understanding the impact and adoption of conservation agriculture in Africa: A multi-scale analysis
Corbeels, M., de Graaff, J., Ndah, H. T., Penot, É., Baudron, F., Naudin, K., Andrieu, N., Chirat, G., Schuler, J., Nyagumbo, I., et al. (2013) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2013.10.011 · Cited by 265
Putting mechanisms into crop production models.
Boote, K., Jones, J. W., White, J., Asseng, S., & Lizaso, J. (2013) · Plant, Cell and Environment · doi:10.1111/pce.12119 · Cited by 247
Self-citationAquaCrop-OS: An open source version of FAO's crop water productivity model
Foster, T., Brozović, N., Butler, A. P., Neale, C. M. U., Raes, D., Steduto, P., Fereres, E., & Hsiao, T. C. (2016) · Agricultural Water Management · doi:10.1016/j.agwat.2016.11.015 · Cited by 252
Rahman, M. H. U., Ahmad, A., Wang, X., Wajid, A., Nasim, W., Hussain, M., Ahmad, B., Ahmad, I., Ali, Z., Ishaque, W., et al. (2018) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2018.02.008 · Cited by 252
Self-citationIrrigation Scheduling Approaches and Applications: A Review
Gu, Z., Qi, Z., Burghate, R., Yuan, S., Jiao, X., & Xu, J. (2020) · Journal of Irrigation and Drainage Engineering · doi:10.1061/(asce)ir.1943-4774.0001464 · Cited by 244
Impact of biochar addition on the physical and hydraulic properties of a clay soil
Castellini, M., Giglio, L., Niedda, M., Palumbo, A. D., & Ventrella, D. (2015) · Soil and Tillage Research · doi:10.1016/j.still.2015.06.016 · Cited by 242
Feng, P., Wang, B., Liu, D. L., Waters, C., & Yu, Q. (2019) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2019.05.018 · Cited by 241
Bouma, J. (2014) · Journal of Plant Nutrition and Soil Science · doi:10.1002/jpln.201300646 · Cited by 239
Janssen, S. J. C., Porter, C. H., Moore, A. D., Athanasiadis, I. N., Foster, I. T., Jones, J. W., & Antle, J. M. (2016) · Agricultural Systems · doi:10.1016/j.agsy.2016.09.017 · Cited by 248
Self-citationClimate change impact on global potato production
Raymundo, R., Asseng, S., Robertson, R., Petsakos, A., Hoogenboom, G., Quiroz, R., Hareau, G., & Wolf, J. (2017) · European Journal of Agronomy · doi:10.1016/j.eja.2017.11.008 · Cited by 207
Self-citationHe, J., Jones, J. W., Graham, W. D., & Dukes, M. D. (2010) · Agricultural Systems · doi:10.1016/j.agsy.2010.01.006 · Cited by 224
Self-citationBrilli, L., Bechini, L., Bindi, M., Carozzi, M., Cavalli, D., Conant, R., Dorich, C. D., Doro, L., Ehrhardt, F., Farina, R., et al. (2017) · Science of The Total Environment · doi:10.1016/j.scitotenv.2017.03.208 · Cited by 244
Metadata
- Authors
- Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., Wilkens, P. W., Singh, U., Gijsman, A. J., & Ritchie, J. T.
- Affiliations
- University of Florida (US)
- University of Georgia (US)
- Iowa State University (US)
- University of Guelph (CA)
- International Fertilizer Development Center (US)
- Michigan State University (US)
- Corresponding author
- Jones, J.W
- Venue
- European Journal of Agronomy
- Publisher
- Elsevier BV
- Published
- 2003-01
- Volume
- 18
- Issue
- 3-4
- Pages
- 235-265
- ISSN
- 1161-0301 (ISSN-L)
- OpenAlex
- W2158883105
- Cited by
- 4,465
- References
- 216
- Citations by year
- 2026: 349 · 2025: 341 · 2024: 296 · 2023: 286 · 2022: 328 · 2021: 313 · 2020: 329 · 2019: 269 · 2018: 241 · 2017: 254 · 2016: 198 · 2015: 191 · 2014: 190 · 2013: 175 · 2012: 178
- FWCI
- 26.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- European Journal of Agronomy (publishedVersion)
- European Journal of Agronomy
- Topics
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Climate change impacts on agriculture
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Agricultural Productivity and Crop Improvement
- Life Sciences › Agricultural and Biological Sciences › Soil Science › Irrigation Practices and Water Management
- Keywords
- DSSAT, crop growth simulation, modular software design, soil model, model evaluation, agricultural technology transfer, crop yield simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
