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
Items 201–300 of 1,006
Stochastic simulation of the cost of home-produced feeds for ruminant livestock systems
Finneran, E., Crosson, P., O’Kiely, P., Shalloo, L., Forristal, P. D., & Wallace, M. (2011) · The Journal of Agricultural Science · doi:10.1017/s002185961100061x · Cited by 139
Climate change impacts on rice productivity in the Mekong River Delta
Kontgis, C. P., Schneider, A., Özdoğan, M., Kucharik, C. J., Trí, V. P. Đ., Duc, N. H., & Schatz, J. D. (2018) · Applied Geography · doi:10.1016/j.apgeog.2018.12.004 · Cited by 150
Climate change impacts on agricultural suitability and yield reduction in a Mediterranean region
Abd‐Elmabod, S. K., Muñoz‐Rojas, M., Jordán, A., Romero, M. A., Phillips, J. D., Jones, L., Zhang, Z., Pereira, P. A. D. S., Fleskens, L., van der Ploeg, M. J., et al. (2020) · Geoderma · doi:10.1016/j.geoderma.2020.114453 · Cited by 144
Ding, Z., Ali, E. F., Elmahdy, A. M., Ragab, K., Seleiman, M. F., & Kheir, A. M. S. (2020) · Agricultural Water Management · doi:10.1016/j.agwat.2020.106626 · Cited by 140
Kassie, B. T., van Ittersum, M. K., Hengsdijk, H., Asseng, S., Wolf, J., & Rötter, R. P. (2014) · Field Crops Research · doi:10.1016/j.fcr.2014.02.010 · Cited by 140
Tao, F., Zhang, S., Zhang, Z., & Rötter, R. P. (2014) · Global Change Biology · doi:10.1111/gcb.12684 · Cited by 138
Using pattern recognition for estimating cultivar coefficients of a crop simulation model
Bannayan, M., & Hoogenboom, G. (2009) · Field Crops Research · doi:10.1016/j.fcr.2009.01.007 · Cited by 137
Self-citationClimate adaptation as mitigation: the case of agricultural investments
Lobell, D. B., Baldos, U. L. C., & Hertel, T. W. (2013) · Environmental Research Letters · doi:10.1088/1748-9326/8/1/015012 · Cited by 136
Implications of regional improvement in global climate models for agricultural impact research
Ramírez-Villegas, J., Challinor, A. J., Thornton, P. K., & Jarvis, A. (2013) · Environmental Research Letters · doi:10.1088/1748-9326/8/2/024018 · Cited by 144
A potato model intercomparison across varying climates and productivity levels
Fleisher, D. H., Condori, B., Quiroz, R. A., Ashok Kumar, A. L. V. A., Asseng, S., Barreda, C., Bindi, M., Boote, K. J., Ferrise, R., Franke, A. C., et al. (2016) · Global Change Biology · doi:10.1111/gcb.13411 · Cited by 145
Self-citationWebber, H. A., Gaiser, T., & Ewert, F. A. (2014) · Agricultural Systems · doi:10.1016/j.agsy.2013.12.006 · Cited by 134
Chakrabarti, S., Bongiovanni, T., Judge, J., Zotarelli, L., & Bayer, C. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2315999 · Cited by 133
Decision support systems and models for aiding irrigation and nutrient management of vegetable crops
Gallardo, M., Elia, A., & Thompson, R. B. (2020) · Agricultural Water Management · doi:10.1016/j.agwat.2020.106209 · Cited by 133
Tao, F., & Zhang, Z. (2010) · Climatic Change · doi:10.1007/s10584-010-9883-9 · Cited by 132
Can Egypt become self-sufficient in wheat?
Asseng, S., Kheir, A. M. S., Kassie, B. T., Hoogenboom, G., Abdel-Aal, A. I. N., Haman, D. Z., & Ruane, A. C. (2018) · Environmental Research Letters · doi:10.1088/1748-9326/aada50 · Cited by 132
Self-citationEvaluation of the RZWQM-CERES-Maize hybrid model for maize production
Ma, L., Hoogenboom, G., Ahuja, L. R., Ascough, J. C., & Saseendran, S. A. (2005) · Agricultural Systems · doi:10.1016/j.agsy.2005.02.001 · Cited by 131
Self-citationWeindl, I., Lotze‐Campen, H., Popp, A., Müller, C., Havlík, P., Herrero, M., Schmitz, C., & Rolinski, S. (2015) · Environmental Research Letters · doi:10.1088/1748-9326/10/9/094021 · Cited by 148
The Global Gridded Crop Model Intercomparison phase 1 simulation dataset
Müller, C., Elliott, J. W., Kelly, D. J. A., Arneth, A., Balkovič, J., Ciais, P., Deryng, D., Folberth, C., Hoek, S., Izaurralde, R. C., et al. (2019) · Scientific Data · doi:10.1038/s41597-019-0023-8 · Cited by 144
An integrated soil-crop system model for water and nitrogen management in North China
Liang, H., Hu, K., Batchelor, W. D., Qi, Z., & Li, B. (2016) · Scientific Reports · doi:10.1038/srep25755 · Cited by 133
Self-citationModeling organic carbon and carbon-mediated soil processes in DSSAT v4.5
Porter, C. H., Jones, J. W., Adiku, S. G. K., Gijsman, A. J., Gargiulo, O., & Naab, J. B. (2009) · Operational Research · doi:10.1007/s12351-009-0059-1 · Cited by 129
Self-citationEstimating DSSAT Cropping System Cultivar‐Specific Parameters Using Bayesian Techniques
Jones, J. W., He, J., Boote, K. J., Wilkens, P., Porter, C. H., & Hu, Z. (2011) · Advances in Agricultural Systems Modeling · doi:10.2134/advagricsystmodel2.c13 · Cited by 129
Self-citationCrop yields in a geoengineered climate
Pongratz, J., Lobell, D. B., Cao, L., & Caldeira, K. (2012) · Nature Climate Change · doi:10.1038/nclimate1373 · Cited by 128
White, J. W., Hunt, L. A., Boote, K. J., Jones, J. W., Koo, J., Kim, S., Porter, C. H., Wilkens, P. W., & Hoogenboom, G. (2013) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2013.04.003 · Cited by 129
Self-citationZhang, S., & Tao, F. (2012) · European Journal of Agronomy · doi:10.1016/j.eja.2012.10.005 · Cited by 128
Battisti, R., Sentelhas, P., & Boote, K. (2017) · Field Crops Research · doi:10.1016/j.fcr.2016.10.004 · Cited by 112
Self-citationBoonwichai, S., Shrestha, S., Babel, M. S., Weesakul, S., & Datta, A. (2018) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2018.10.201 · Cited by 128
Mugiyo, H., Chimonyo, V. G. P., Sibanda, M., Kunz, R. P., Masemola, C. R., Modi, A. T., & Mabhaudhi, T. (2021) · Land · doi:10.3390/land10020125 · Cited by 136
Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives
Tao, H., Xu, S., Tian, Y., Li, Z., Ge, Y., Zhang, J., Wang, Y., Zhou, G., Deng, X., Zhang, Z., et al. (2022) · Plant Communications · doi:10.1016/j.xplc.2022.100344 · Cited by 127
Modeling the impacts of climate change on irrigated corn production in the Central Great Plains
Islam, A., Ahuja, L. R., García, L. A., Ma, L., Anapalli, S. S., & Trout, T. J. (2012) · Agricultural Water Management · doi:10.1016/j.agwat.2012.04.004 · Cited by 127
Prediction of Field-Scale Wheat Yield Using Machine Learning Method and Multi-Spectral UAV Data
Bian, C., Shi, H., Wu, S., Zhang, K., Wei, M., Zhao, Y., Sun, Y., Zhuang, H., Zhang, X., & Chen, S. (2022) · Remote Sensing · doi:10.3390/rs14061474 · Cited by 126
Modelling cropping systems—highlights of the symposium and preface to the special issues
Donatelli, M., van Ittersum, M. K., Bindi, M., & Porter, J. R. (2002) · European Journal of Agronomy · doi:10.1016/s1161-0301(02)00104-1 · Cited by 126
Impacts of climate change on farm income security in Central Asia: An integrated modeling approach
Bobojonov, I., & Aw-Hassan, A. A. (2014) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2014.02.033 · Cited by 125
Yu, Y., Huang, Y., & Zhang, W. J. (2012) · Field Crops Research · doi:10.1016/j.fcr.2012.07.021 · Cited by 124
Kephe, P., Ayisi, K., & Petja, B. (2021) · Agriculture & Food Security · doi:10.1186/s40066-020-00283-5 · Cited by 106
The influence of aerosols on crop production: A study using the CERES crop model
Greenwald, R., Bergin, M. H., Xu, J., Cohan, D. S., Hoogenboom, G., & Chameides, W. L. (2005) · Agricultural Systems · doi:10.1016/j.agsy.2005.10.004 · Cited by 123
Self-citationTesfaye, K., Kruseman, G., Cairns, J. E., Zaman‐Allah, M., Wegary, D., Zaidi, P. H., Boote, K. J., Rahut, D. B., & Erenstein, O. (2017) · Climate Risk Management · doi:10.1016/j.crm.2017.10.001 · Cited by 123
Self-citationBhadra, S., Sagan, V., Sarkar, S., Braud, M., Mockler, T. C., & Eveland, A. L. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.02.020 · Cited by 134
Modelling Crop Improvement in a G×E×M Framework via Gene–Trait–Phenotype Relationships
Messina, C., Hammer, G., Dong, Z., Podlich, D., & Cooper, M. (2009) · Crop Physiology · doi:10.1016/b978-0-12-374431-9.00010-4 · Cited by 121
Bryan, E. M., Ringler, C., Okoba, B. O., Koo, J., Herrero, M., & Silvestri, S. (2012) · Climatic Change · doi:10.1007/s10584-012-0640-0 · Cited by 121
Shrestha, S., Chapagain, R., & Babel, M. S. (2017) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2017.05.028 · Cited by 120
Sandhu, R., & Irmak, S. (2019) · Agricultural Water Management · doi:10.1016/j.agwat.2019.105687 · Cited by 121
Timsina, J., Godwin, D. C., Humphreys, E., Yadvinder‐Singh, Sıngh, B., Kukal, S. S., & Smith, D. (2008) · Agricultural Water Management · doi:10.1016/j.agwat.2008.04.009 · Cited by 121
Adhikari, P., Ale, S., Bordovsky, J. P., Thorp, K. R., Modala, N. R., Rajan, N., & Barnes, E. M. (2015) · Agricultural Water Management · doi:10.1016/j.agwat.2015.10.011 · Cited by 120
Mandal, A., Majumder, A., Dhaliwal, S. S., Toor, A. S., Mani, P. K., Naresh, R. K., Gupta, R. K., & Mitran, T. (2020) · Critical Reviews in Environmental Science and Technology · doi:10.1080/10643389.2020.1811590 · Cited by 128
A preliminary assessment of climate change impacts on sugarcane in Swaziland
Knox, J. W., Díaz, J. A. R., Nixon, D. J., & Mkhwanazi, M. (2009) · Agricultural Systems · doi:10.1016/j.agsy.2009.09.002 · Cited by 119
The land-network problem: ecosystem carbon balance in planning sustainable agro-food supply chains
Accorsi, R., Cholette, S., Manzini, R., Pini, C., & Penazzi, S. (2015) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2015.06.082 · Cited by 119
DSSAT modelling of conservation agriculture maize response to climate change in Malawi
Ngwira, A. R., Aune, J. B., & Thierfelder, C. (2014) · Soil and Tillage Research · doi:10.1016/j.still.2014.05.003 · Cited by 119
Parameterization and evaluation of predictions of DSSAT/CANEGRO for Brazilian sugarcane
Marin, F. R., Jones, J. W., Royce, F., Suguitani, C., Donzeli, J. L., Filho, W. J. P., & Nassif, D. S. P. (2011) · Agronomy Journal · doi:10.2134/agronj2010.0302 · Cited by 118
Self-citationProtecting the Amazon forest and reducing global warming via agricultural intensification
Marin, F. R., Zanon, A. J., Monzon, J. P., Andrade, J. F., Silva, E. H. F. M., Richter, G. L., Antolin, L. A. S., Ribeiro, B. S. M. R., Ribas, G. G., Battisti, R., et al. (2022) · Nature Sustainability · doi:10.1038/s41893-022-00968-8 · Cited by 122
Using CERES-Wheat to simulate durum wheat production and phenology in Southern Sardinia, Italy
Dettori, M., Cesaraccio, C., Motroni, A., Spano, D., & Duce, P. (2010) · Field Crops Research · doi:10.1016/j.fcr.2010.09.008 · Cited by 118
Eitzinger, J., Thaler, S., Schmid, E., Strauss, F., Ferrise, R., Moriondo, M., Bindi, M., Palosuo, T., Rötter, R., Kersebaum, K., et al. (2012) · Journal of Agricultural Sciences · doi:10.1017/s0021859612000779 · Cited by 115
The Plant's Capacity in Regulating Resource Demand
Matyssek, R., Agerer, R., Ernst, D., Munch, J. C., Oßwald, W., Pretzsch, H., Priesack, E., Schnyder, H., & Treutter, D. (2005) · Plant Biology · doi:10.1055/s-2005-872981 · Cited by 117
Improving evapotranspiration simulations in the CERES-Maize model under limited irrigation
DeJonge, K. C., Ascough, J. C., Andales, A. A., Hansen, N. C., García, L. A., & Arabi, M. (2012) · Agricultural Water Management · doi:10.1016/j.agwat.2012.08.013 · Cited by 117
An integrative index of Ecosystem Services provision based on remotely sensed data
Paruelo, J. M., Texeira, M. A., Staiano, L., Mastrángelo, M. E., Amdan, L., & Gallego, F. (2016) · Ecological Indicators · doi:10.1016/j.ecolind.2016.06.054 · Cited by 127
Zhang, X., Izaurralde, R. C., Arnold, J. G., Williams, J. R., & Srinivasan, R. (2013) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2013.06.056 · Cited by 117
Shelia, V., Šimůnek, J., Boote, K. J., & Hoogenbooom, G. (2018) · Journal of Hydrology and Hydromechanics · doi:10.1515/johh-2017-0055 · Cited by 119
Self-citationTariq, M., Ahmad, S., Fahad, S., Abbas, G., Hussain, S., Fatima, Z., Nasim, W., Mubeen, M. A., Rahman, M. H. U., Khan, M. A., et al. (2018) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2018.03.015 · Cited by 116
Self-citationClimate change impact on Mexico wheat production
Hernández-Ochoa, I., Asseng, S., Kassie, B., Xiong, W., Robertson, R., Pequeno, D. N. L., Sonder, K., Reynolds, M., Babar, M. A., Milan, A. M., et al. (2018) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2018.09.008 · Cited by 92
Self-citationDecomposing crop model uncertainty: A systematic review
Chapagain, R., Remenyi, T. A., Harris, R. M. B., Mohammed, C. L., Huth, N. I., Wallach, D., Rezaei, E. E., & Ojeda, J. J. (2022) · Field Crops Research · doi:10.1016/j.fcr.2022.108448 · Cited by 122
Bocca, F. F., & Rodrigues, L. H. A. (2016) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2016.08.015 · Cited by 115
Field warming experiments shed light on the wheat yield response to temperature in China
Zhao, C., Piao, S. L., Huang, Y., Wang, X., Ciais, P., Huang, M., Zeng, Z., & Peng, S. (2016) · Nature Communications · doi:10.1038/ncomms13530 · Cited by 115
Yield gap analysis of field crops: Methods and case studies
Sadras, V. O., Cassman, K. G., Grassini, P., Bastiaanssen, W. G. M., Laborte, A. G., Milne, A. E., Sileshi, G. W., & Steduto, P. (2015) · Lincoln (University of Nebraska) · Cited by 116
Nitrogen mineralization from organic fertilizers and composts: Literature survey and model fitting
Geisseler, D. J., Smith, R., Cahn, M. D., & Muramoto, J. (2021) · Journal of Environmental Quality · doi:10.1002/jeq2.20295 · Cited by 116
Meta-analysis of climate impacts and uncertainty on crop yields in Europe
Knox, J., Daccache, A., Hess, T., & Haro, D. (2016) · Environmental Research Letters · doi:10.1088/1748-9326/11/11/113004 · Cited by 97
Tan, S., Wang, Q., Zhang, J., Chen, Y., Shan, Y., & Xu, D. (2017) · Agricultural Water Management · doi:10.1016/j.agwat.2017.11.001 · Cited by 114
Gaiser, T., Perkons, U., Küpper, P. M., Kautz, T., Uteau, D., Ewert, F. A., Enders, A., & Krauss, G. (2013) · Ecological Modelling · doi:10.1016/j.ecolmodel.2013.02.016 · Cited by 113
Bastiaanssen, W. G. M., & Steduto, P. (2016) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2016.09.032 · Cited by 113
Farina, R., Sándor, R., Abdalla, M., Álvaro-Fuentes, J., Bechini, L., Bolinder, M. A., Brilli, L., Chenu, C., Clivot, H., Migliorati, M. D. A., et al. (2020) · Global Change Biology · doi:10.1111/gcb.15441 · Cited by 84
Bert, F., Podestá, G., Rovere, S. L., Menéndez, Á. N., NORTH, M. J., Tatara, E. R., Laciana, C. E., Weber, E. U., & Toranzo, F. R. (2011) · Ecological Modelling · doi:10.1016/j.ecolmodel.2011.08.007 · Cited by 112
Sultana, H., Ali, N., Iqbal, M. M., & Khan, A. M. (2009) · Climatic Change · doi:10.1007/s10584-009-9559-5 · Cited by 112
Han, M., Zhao, C., Šimůnek, J., & Feng, G. G. (2015) · Agricultural Water Management · doi:10.1016/j.agwat.2015.06.028 · Cited by 111
Domínguez, A., de Juan, J. A., Martín‐Benito, J. M. T., Martínez, R. S., & Martínez-Romero, A. (2012) · Agricultural Water Management · doi:10.1016/j.agwat.2012.04.002 · Cited by 110
Neural network soil moisture model for irrigation scheduling
Gu, Z., Zhu, T., Jiao, X., Xu, J., & Qi, Z. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105801 · Cited by 111
Using the WISE database to parameterize soil inputs for crop simulation models
Gijsman, A. J., Thornton, P. K., & Hoogenboom, G. (2007) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2007.01.001 · Cited by 110
Self-citationImpacts of climate change on irrigated potato production in a humid climate
Daccache, A., Weatherhead, E. K., STALHAM, M. A., & Knox, J. W. (2011) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2011.06.018 · Cited by 110
Moradi, R., Koocheki, A., Mahallati, M. N., & Mansoori, H. (2012) · Mitigation and Adaptation Strategies for Global Change · doi:10.1007/s11027-012-9410-6 · Cited by 110
How efficiently do corn‐ and soybean‐based cropping systems use water? A systems modeling analysis
Dietzel, R. N., Liebman, M., Ewing, R., Helmers, M. J., Horton, R., Jarchow, M. E., & Archontoulis, S. V. (2015) · Global Change Biology · doi:10.1111/gcb.13101 · Cited by 115
Ruane, A., Cecil, L., Horton, R., Gordón, R., McCollum, R., Brown, D. W., Killough, B., Goldberg, R., Greeley, A. P., & Rosenzweig, C. (2013) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2011.10.015 · Cited by 118
Delgoda, D. K., Malano, H. M., Saleem, S. K., & Halgamuge, M. N. (2015) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2015.12.012 · Cited by 109
Monzón, J. P., Calviño, P., Sadras, V. O., Zubiaurre, J. B., & Andrade, F. H. (2018) · European Journal of Agronomy · doi:10.1016/j.eja.2018.06.011 · Cited by 109
Remote sensing-based estimation of rice yields using various models: A critical review
Torre, D. M. G. D., Gao, J., & Macinnis‐Ng, C. (2021) · Geo-spatial Information Science · doi:10.1080/10095020.2021.1936656 · Cited by 109
Ceglar, A., Črepinšek, Z., Kajfež-Bogataj, L., & Pogačar, T. (2010) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2010.09.007 · Cited by 108
Impacts of 1.5 versus 2.0 °C on cereal yields in the West African Sudan Savanna
Faye, B., Webber, H. A., Naab, J. B., MacCarthy, D. S., Adam, M., Ewert, F. A., Lamers, J. P. A., Schleussner, C., Ruane, A. C., Geßner, U., et al. (2018) · Environmental Research Letters · doi:10.1088/1748-9326/aaab40 · Cited by 108
Self-citationBaigorria, G. A., Jones, J. W., Shin, D., Mishra, A., & O’Brien, J. J. (2007) · Climate Research · doi:10.3354/cr00703 · Cited by 108
Self-citationRuiz‐Ramos, M., Ferrise, R., Rodríguez, A., Lorite, I. J., Bindi, M., Carter, T. R., Fronzek, S., Palosuo, T., Pirttioja, N., Baranowski, P., et al. (2017) · Agricultural Systems · doi:10.1016/j.agsy.2017.01.009 · Cited by 107
Quantification of Climate Warming and Crop Management Impacts on Cotton Phenology
Ahmad, S., Abbas, Q., Abbas, G., Fatima, Z., Atique-ur-Rehman, Naz, S., Younis, H., Khan, R., Nasim, W., Rahman, M. H. U., et al. (2017) · Plants · doi:10.3390/plants6010007 · Cited by 107
Nouri, M., Homaee, M., Bannayan, M., & Hoogenboom, G. (2017) · Agricultural Water Management · doi:10.1016/j.agwat.2017.03.004 · Cited by 114
Self-citationWater productivity of rainfed maize and wheat: A local to global perspective
Edreira, J. I. R., Guilpart, N., Sadras, V. O., Cassman, K. G., van Ittersum, M. K., Schils, R. L. M., & Grassini, P. (2018) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2018.05.019 · Cited by 113
Adaptation strategies for maize production under climate change for semi-arid environments
Ahmad, I., Ahmad, B., Boote, K. J., & Hoogenboom, G. (2020) · European Journal of Agronomy · doi:10.1016/j.eja.2020.126040 · Cited by 107
Self-citationA Protocol for Parameterization and Calibration of RZWQM2 in Field Research
Ma, L., Ahuja, L. R., Saseendran, S. A., Malone, R. W., Green, T. R., Nolan, B. T., Bartling, P. N. S., Flerchinger, G. N., Boote, K. J., & Hoogenboom, G. (2011) · Advances in agricultural systems modeling · doi:10.2134/advagricsystmodel2.c1 · Cited by 107
Self-citationLi, Z. T., Yang, J. Y., Drury, C. F., & Hoogenboom, G. (2015) · Agricultural Systems · doi:10.1016/j.agsy.2014.12.006 · Cited by 106
Self-citationThorp, K. R., Wang, G., West, A. L., Moran, M. S., Bronson, K. F., White, J. W., & Mon, J. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2012.05.013 · Cited by 106
Crop science experiments designed to inform crop modeling
Craufurd, P., Vadez, V., Jagadish, S. V. K., Prasad, P. V. V., & Zaman‐Allah, M. (2011) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2011.09.003 · Cited by 112
Mangin, B., Casadebaig, P., Cadic, E., Blanchet, N., Boniface, M., Carrère, S., Gouzy, J., Legrand, L., Mayjonade, B., Pouilly, N., et al. (2017) · Plant Cell & Environment · doi:10.1111/pce.12961 · Cited by 110
Franke, J., Müller, C., Elliott, J. W., Ruane, A. C., Jägermeyr, J., Balkovič, J., Ciais, P., Dury, M., Falloon, P., Folberth, C., et al. (2020) · Geoscientific model development · doi:10.5194/gmd-13-2315-2020 · Cited by 115
Thaler, S., Eitzinger, J., Trnka, M., & Dubrovský, M. (2012) · The Journal of Agricultural Science · doi:10.1017/s0021859612000093 · Cited by 106
Smart and Climate-Smart Agricultural Trends as Core Aspects of Smart Village Functions
Adesipo, A. A., Fadeyi, O. O., Kuča, K., Krejcar, O., Marešová, P., Selamat, A., & Adenola, M. (2020) · Sensors · doi:10.3390/s20215977 · Cited by 105
Rahman, M. H. U., Ahmad, A., Wajid, A., Hussain, M., Rasul, F., Ishaque, W., Islam, M. A., Shelia, V., Awais, M., Ullah, A., et al. (2019) · Field Crops Research · doi:10.1016/j.fcr.2017.07.007 · Cited by 104
Connecting Biochemical Photosynthesis Models with Crop Models to Support Crop Improvement
Wu, A. C., Song, Y., van Oosterom, E. J., & Hammer, G. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.01518 · Cited by 109
An assessment of yield gains under climate change due to genetic modification of pearl millet
Singh, P., Boote, K. J., Kadiyala, M. D. M., Nedumaran, S., Gupta, S. K., Srinivas, K., & Bantilan, M. C. S. (2017) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2017.06.002 · Cited by 103
Self-citation
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
