Parameter and uncertainty estimation for maize, peanut and cotton using the SALUS crop model

Agricultural Systems

Volumn 135, Issue , 2015, Pages 31-47

  Dzotsi, K A ^a   Basso, B ^b   Jones, J W ^a  

^a J Crayton Pruitt Family Department of Biomedical Engineering   (United States)

^b MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

Bayesian parameter estimation; Crop modeling; DSSAT; Markov Chain Monte Carlo; Metropolis Hastings; SALUS

Indexed keywords

ANNUAL PLANT; BIOMASS; CROP PLANT; CROP YIELD; CROPPING PRACTICE; ECOLOGICAL MODELING; ESTIMATION METHOD; GROWTH RATE; MARKOV CHAIN; MONTE CARLO ANALYSIS; NUMERICAL MODEL; PARAMETERIZATION; PREDICTION; SUSTAINABILITY; UNCERTAINTY ANALYSIS;

ARACHIS HYPOGAEA; GOSSYPIUM HIRSUTUM; ZEA MAYS;

EID: 84920887712     PISSN: 0308521X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.agsy.2014.12.003     Document Type: Article

References (59)

1. Adam M., Van Bussel L., Leffelaar P., Van Keulen H., Ewert F. Effects of modelling detail on simulated potential crop yields under a wide range of climatic conditions. Ecol. Modell 2011, 222:131-143.
2. Angus J., Cunningham R., Moncur M., Mackenzie D. Phasic development in field crops I. Thermal response in the seedling phase. Field Crops Res 1981, 3:365-378.
3. Asseng S., Ewert F., Rosenzweig C., Jones J., Hatfield J., Ruane A., et al. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Chang 2013, 10.1038/nclimate1916.
4. Basso B., Ritchie J., Grace P., Sartori L. Simulation of tillage systems impact on soil biophysical properties using the SALUS model. Ital. J. Agron 2006, 4:677-688.
5. Bates B., Campbell E. A Markov chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall-runoff modeling. Water Resour. Res 2001, 37:937-947.
6. Bell M., Wright G. Groundnut growth and development in contrasting environments. 2. Heat unit accumulation and photo-thermal effects on harvest index. Exp. Agric 1998, 113-124.
7. Bell M., Wright G., Hammer G. Night temperature affects Radiation-Use Efficiency in peanut. Crop Sci 1992, 32:1329-1335. https://www.crops.org/publications/cs/abstracts/32/6/CS0320061329.
8. Bennett J., Jones J., Zur B., Hammond L. Interactive effects of nitrogen and water stresses on water relations of field-grown corn leaves. Agron. J. 1986, 78:273-280.
9. Bennett J., Sinclair T., Ma L., Boote K. Single leaf carbon exchange and canopy radiation use efficiency of four peanut cultivars. Peanut Sci 1993, 20:1-5.
10. Beven K., Binley A. The future of distributed models: model calibration and uncertainty prediction. Hydrol. Process 1992, 6:279-298.
11. Bondeau A., Smith P.P.C., Zaehle S., Schaphoff S., Lucht W., Cramer W., et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob. Chang. Biol 2007, 13(3):679-706. accessed 08.08.13. http://doi.wiley.com/10.1111/j.1365-2486.2006.01305.x.
12. Boote K., Jones J., Mishoe J., Wilkerson G. Modeling growth and yield of groundnut. Agrometeorology of Groundnut, International Symposium, 21-26 Aug 1985 1985, 243-254. ICRISAT Sahelian Center, Niamey, Niger.
13. Boote K., Jones J., Pickering N. Potential uses and limitations of crop models. Agron. J. 1996, 88:704-716.
14. Ceglar A., Črepinšek Z., Kajfež-Bogataj L., Pogačar T. The simulation of phenological development in dynamic crop model: the Bayesian comparison of different methods. Agric. For. Meteorol 2011, 151:101-115.
15. Challinor A., Wheeler T., Craufurd P., Slingo J., Grimes D. Design and optimization of a large-area process-based model for annual crops. Agric. For. Meteorol 2004, 124:99-120.
16. Chib S., Greenberg E. Understanding the metropolis-hastings algorithm. Am. Stat 1995, 49:327-335.
17. Dzotsi K., Basso B., Jones J. Development, uncertainty and sensitivity analysis of the simple SALUS crop model in DSSAT. Ecol. Modell 2013, 260:62-76.
18. Ellison A. An introduction to Bayesian inference for ecological research and environmental decision- making. Ecol. Appl 1996, 6:1036-1046.
19. Faivre R., Leenhardt D., Voltz M., Benoît M., Papy F., Dedieu G., et al. Spatialising crop models. Agronomie 2004, 24:205-217.
20. Geyer C. Practical Markov chain Monte Carlo. Stat. Sci 1992, 7:473-511.
21. Gupta S., Schneider E., Swan J. Planting depth and tillage interactions on corn emergence. Soil Sci. Soc. Am. J. 1988, 52:1122-1127.
22. Hammer G., Sinclair T., Boote K., Wright G., Meinke H., Bell M. A peanut simulation model: I. model development and testing. Agronomie 1995, 87:1085-1093.
23. Harmon R., Challenor P. A Markov chain Monte Carlo method for estimation and assimilation into models. Ecol. Modell 1997, 101:41-59.
24. Hastings W. Monte Carlo sampling methods using Markov Chains and their applications. Biometrika 1970, 57:97-109.
25. Hatfield J., Boote K., Fay P., Hahn L., Izaurralde C., Kimball B., et al. Agriculture. The Effects of Climate Change on Agriculture, Land Resources, Water Resources, and Biodiversity in the United States. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research 2008, 21-74. Washington, DC.
26. He J., Jones J., Graham W., Dukes M. Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method. Agric. Syst 2010, 103:256-264.
27. Iizumi T., Yokozawa M., Nishimori M. Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: application of a Bayesian approach. Agric. For. Meteorol 2009, 149:333-348.
28. Jones C., Bland W., Ritchie J., Williams J. Simulation of root growth. Modeling Plant and Soil Systems 1991, 91-123. ASA. J. Hanks, J.T. Ritchie (Eds.).
29. Jones J., Hoogenboom G., Porter C., Boote K., Batchelor W., Hunt L., et al. The DSSAT cropping system model. Eur. J. Agron 2003, 18:235-265.
30. Kiniry J., Bockholt A. Maize and sorghum simulation in diverse Texas environments. Agron. J. 1998, 90:682-687.
31. Kiniry J., Jones C., O'Toole J., Blanchet R., Cabelguenne M., Spanel D. Radiation use efficiency in biomass accumulation prior to grain filling for five grain crop species. Field Crops Res 1989, 20:51-64.
32. Kiniry J., Williams J., Gassman P., Debaeke P. A general, process-oriented model for two competing plant species. Trans. ASAE 1992, 35:801-810.
33. Kiniry J., Williams J., Vanderlip R., Atwood J., Reicosky D., Mulliken J., et al. Evaluation of two maize models for nine U. S. locations. Agron. J. 1997, 89:421-426.
34. Kiniry J., Simpson C., Schubert A., Reed J. Peanut leaf area index, light interception, radiation use efficiency, and harvest index at three sites in Texas. Field Crops Res 2005, 91:297-306.
35. Ko J., Piccinni G., Guo W., Steglich E. Parameterization of EPIC crop model for simulation of cotton growth in South Texas. J. Agric. Sci 2009, 147:169-178.
36. Kobayashi K., Salam M. Comparing simulated and measured values using mean squared deviation and its components. Agron. J. 2000, 92:345-352.
37. Lobell D., Burke M. On the use of statistical models to predict crop yield responses to climate change. Agric. For. Meteorol 2010, 150:1443-1452.
38. Major D., Beasley B., Hamilton R. Effect of maize maturity on radiation-use efficiency. Agron. J. 1991, 83:895-903.
39. Makowski D., Wallach D., Temblay M. Using a Bayesian approach to parameter estimation; comparison of the GLUE and MCMC methods. Agronomie 2002, 22:191-203.
40. Marshall L., Nott D., Sharma A. A comparative study of Markov chain Monte Carlo methods for conceptual rainfall-runoff modeling. Water Resour. Res 2004, 40:1-11.
41. Mauney J., Kimball B., Pinterjr P., Lamorte R., Lewin K., Nagy J., et al. Growth and yield of cotton in response to a free-air carbon dioxide enrichment (FACE) environment. Agric. For. Meteorol 1994, 70:49-67.
42. Metropolis N., Rosenbluth A., Rosenbluth M., Teller A. Equation of state calculations by fast computing machines. J. Chem. Phys 1953, 21:1087-1092.
43. Monsi M., Saeki T. Uber den lichtfaktor in den panzengesellschaften und seine bedeutung fur die sto produktion. Jpn. J. Bot 1953, 14:22-52.
44. Monteith J. Climate and the efficiency of crop production in Britain. Philos. Trans. R. Soc. Lond. B. Biol Sci 1977, 281:277-294.
45. Monteith J. The quest for balance in crop modeling. Agron. J. 1996, 88:695-697.
46. Pathak T., Jones J., Fraisse C., Wright D., Hoogenboom G. Uncertainty analysis and parameter estimation for the CSM-CROPGRO-Cotton model. Agron. J. 2012, 104(5):1363-1373.
47. Putze A., Derome L., Maurin D., Perotto L., Taillet R. A Markov chain Monte Carlo technique to sample transport and source parameters of Galactic cosmic rays. Astron. Astrophys 2009, 497:991-1007.
48. Putze A., Derome L., Maurin D. A Markov chain Monte Carlo technique to sample transport and source parameters of Galactic cosmic rays. II. Results for the diffusion model combining B/C and radioactive nuclei. Astron. Astrophys 2010, 516:1-19.
49. Robertson M., Carberry P., Huth N., Turpin J., Probert M., Poulton P., et al. Simulation of growth and development of diverse legume species in APSIM. Aust. J. Agric. Res 2002, 53:429-446.
50. Rosenzweig C., Elliott J., Deryng D., Ruane A., Muller C., Arneth A., et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. PNAS 2013, (accepted).
51. Sinclair T., Bennett J., Boote K. Leaf nitrogen content, photosynthesis and radiation use efficiency in peanut. Peanut Sci 1993, 20:40-43.
52. Stehfest E., Heistermann M., Priess J., Ojima D., Alcamo J. Simulation of global crop production with the ecosystem model DayCent. Ecol. Modell 2007, 209:203-219.
53. Stockle C., Kemanian A. Crop radiation capture and use efficiency: a framework for crop growth analysis. Crop Physiology: Applications for Genetic Improvement and Agronomy 2009, 145-170. Academic Press, Elsevier, San Diego. V. Sadras, D. Calderini (Eds.).
54. Valentinuz O., Tollenaar M. Vertical profile of leaf senescence during the grain-filling period in older and newer maize hybrids. Crop Sci 2004, 44:827-834.
55. Van Noordwijk M., Brouwer G. Review of quantitative root length data in agriculture. Plant Roots and their Environment 1991, 515-527. Elsevier, New York, USA. B. McMichael, H. Persson (Eds.).
56. Van Oijen M., Rougier J., Smith R. Bayesian calibration of process-based forest models: bridging the gap between models and data. Tree Physiol 2005, 25:915-927.
57. Wallach D. Evaluating crop models. Working with Dynamic Crop Model: Evaluation, Analysis, Parameterization, and Applications 2006, 11-50. Elsevier. first ed. D. Wallach, D. Makowski, J. Jones (Eds.).
58. Wallach D. Crop model calibration: a statistical perspective. Agron. J. 2011, 103(4):1144-1151.
59. Willmott C. Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc 1992, 63(11):1309-1313.