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Cited by 11Sugarcane Cultivation and ProcessingAgriculture, Soil, Plant ScienceCrop Yield and Soil Fertility

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Crop Models

Singels, Abraham·2013·Sugarcane: Physiology, Biochemistry, and Functional Biology
DOI 10.1002/9781118771280.ch20
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Abstract

This chapter reviews the mathematical modeling of crop growth and yield and its application to assist in the research and management of sugarcane production. It summarizes approaches to modeling key aspects of the soil-plant-atmosphere system. The chapter reviews sugarcane models with regard to (1) concepts of crop development and growth processes and how well they represent current knowledge and theory of the physiology of the crop and (2) their application in sugarcane research and crop production management. It talks about the potential of models for enhancing crop improvement by summarizing case studies for other crops and highlighting the modeling requirements. A brief review is also given on the empirical models that could be used to estimate productivity and support production management of sugarcane.

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Research works printed but not linked: 178

  1. Acock B., 1991, Modeling Crop Photosynthesis–From Biochemistry to Canopy, 41
  2. 10.1016/S0378-4290(96)01044-1 · DOI · Paper page
  3. Aggarwal P.K., 1996, Plant Adaptation and Crop Improvement, 403
  4. 10.1007/s00122-008-0856-6 · DOI
  5. Aitken K.S., 2006, Quantitative trait loci identified for sugar‐related traits in a sugarcane (Saccharum spp.) cultivar × Saccharum officinarum population, Theoretical and Applied Genetics, 112, 1306, 10.1007/s00122-006-0233-2 · DOI
  6. Annandale J.G., 1996, Proceedings of the International Conference on Evapotranspiration and Irrigation Scheduling, 944
  7. 10.1023/A:1026231904221 · DOI
  8. Bappel E., 2005, Assimilation of a biophysical parameter estimated by remote sensing using SPOT 4&5 data into a sugarcane yield forecasting model, Proceedings International Society of Sugar Cane Technologists, 25, 260
  9. Bernardes M.S., 2007, Evaluation of mosaics sugarcane growth model in Brazil, Proceedings International Society of Sugar Cane Technologists, 26, 589
  10. 10.1016/S0308-521X(02)00076-8 · DOI
  11. 10.1016/j.agsy.2006.02.001 · DOI
  12. 10.1016/j.agsy.2006.03.002 · DOI
  13. Bezuidenhout C.N., 2002, Whole farm harvesting strategy optimisation using the CANEGRO model: a case study for irrigated and dryland sugarcane, Proceedings of the South African Sugarcane Technologists' Association, 74, 250
  14. 10.1016/0002-1571(65)90012-9 · DOI
  15. Bissessur D., 2001, Analysing GXE interaction in sugar cane using additive main effects and multiplicative interaction model, Proceedings International Society of Sugar Cane Technologists, 24, 506
  16. Boote K.J., 1988, Agronomy Abstracts, 105
  17. 10.1007/978-94-017-0519-6_85 · DOI
  18. Boote K.J. Jones J.W. &Mishoe J.W.(1986)Modeling growth and yield of groundnut. ICRISAT Patancheru Andhra Pradesh India Agrometeorology of groundnut.Proceedings International Symposium pp.243–254.ICRISAT Sahelian Center 21–26 August 1985.
  19. Boote K.J., 2001, Physiology and modeling of traits in crop plants: implications for genetic improvement, Agricultural Systems, 70, 395, 10.1016/S0308-521X(01)00053-1 · DOI
  20. Boote K.J., 1991, Modeling Crop Photosynthesis–From Biochemistry to Canopy, 109, 10.2135/cssaspecpub19 · DOI
  21. Bull T.A., 1974, Aspects of modeling sugarcane growth by computer simulations, Proceedings International Society of Sugar Cane Technologists, 15, 1021
  22. Campbell G.S., 1985, Developments in Soil Science
  23. 10.1007/978-1-4612-1626-1 · DOI · Paper page
  24. 10.1071/AR99021 · DOI
  25. 10.1016/S0378-4290(00)00069-1 · DOI
  26. Cheeroo‐Nayamuth F.B., 2001, Climate change and sucrose production in Mauritius, Proceedings International Society of Sugar Cane Technologists, 24, 107
  27. 10.1111/j.1365-3040.2007.01772.x · DOI · Paper page
  28. Chopart J.L., 2010, Sugarcane root system depth in three different countries, Proceedings International Society of Sugar Cane Technologists, 27, 36
  29. 10.1007/s11104-008-9683-4 · DOI
  30. Cooper M., 2005, Complex traits and plant breeding–can we understand the complexities of gene‐to‐phenotype relationships and use such knowledge to enhance plant breeding outcomes, Australian Journal of Agricultural Research, 56, 869, 10.1071/AR05151 · DOI
  31. de Lange J., 2003, Using an Internet based crop model to assist with crop estimation for the Umfolozi mill supply area, Proceedings South African Sugar Technologists' Association, 77, 592
  32. de Wit C.T., 1970, Prediction and measurement of photosynthetic productivity, 47
  33. de Wit C.T., 1978, Simulation Monographs, 141
  34. Dewitt T.J., 2004, Phenotypic Plasticity: Functional and Conceptual Approaches, 10.1093/oso/9780195138962.001.0001 · DOI
  35. 10.1007/1-4020-5906-X_13 · DOI
  36. 10.1071/AR05063 · DOI
  37. Donaldson R.A., 2008, Season effects on productivity of some commercial South African sugarcane cultivars, I: biomass and radiation use efficiency, Proceedings South African Sugar Technologists' Association, 81, 517
  38. Doorenbos J. Kassam A.H. &Bentvelsen C.L.M.(1979)Yield response to water. In:FAO Irrigation and Drainage PaperNo. 33 p.200.FAO Land and Water Development Division Rome Italy. · DOI
  39. 10.1016/j.rse.2009.04.009 · DOI
  40. 10.2135/cssaspecpub19.c1 · DOI
  41. 10.1007/BF00386231 · DOI · Paper page
  42. 10.2136/sssaj1995.03615995005900050029x · DOI
  43. 10.1097/00010694-196002000-00001 · DOI
  44. 10.2134/agronj2002.4620 · DOI · Paper page
  45. 10.1016/j.fcr.2005.02.006 · DOI
  46. Godwin D.C., 1991, Modeling Plant and Soil Systems
  47. 10.17660/ActaHortic.1998.456.1 · DOI
  48. 10.1007/1-4020-5906-X_5 · DOI
  49. 10.2135/cropsci1971.0011183X001100030025x · DOI
  50. Hoarau J.Y., 2002, Genetic dissection of a modern sugarcane cultivar (Saccharum spp.). II. Detection of QTLs for yield components, Theoretical & Applied Genetics, 105, 1027, 10.1007/s00122-002-1047-5 · DOI
  51. 10.2134/agronj1997.00021962008900040013x · DOI
  52. 10.1016/j.fcr.2004.07.014 · DOI
  53. 10.1080/02571862.1991.10634587 · DOI
  54. 10.1016/0378-4290(94)90051-5 · DOI
  55. Inman‐Bamber N.G., 1995, Climate and water as constraints to production in the South African sugar industry, Proceedings South African Sugar Technologists' Association, 69, 55
  56. Inman‐Bamber N.G., 2007, Economic impact of water stress on sugar production in Australia, Sugar Cane International, 25, 7
  57. Inman‐Bamber N.G., 2007, A web‐based system for scheduling irrigation in sugarcane, Proceedings International Society of Sugar Cane Technologists, 26, 459
  58. 10.1071/CP09262 · DOI
  59. Inman‐Bamber N.G., 1993, Predicting yield responses to irrigation of sugarcane from a growth model and field records, Proceedings South African Sugar Technologists' Association, 67, 66
  60. 10.1016/S0378-4290(02)00044-8 · DOI
  61. Inman‐Bamber N.G., 1999, Boosting yields with limited irrigation water, Proceedings Australian Society of Sugar Cane Technologists, 21, 203
  62. 10.1016/S0022-5193(84)80131-9 · DOI
  63. 10.1016/0308-521X(91)90057-H · DOI
  64. 10.1016/S1161-0301(02)00107-7 · DOI · Paper page
  65. Jones C.A., 1986, CERES‐Maize: A Simulation Model of Maize Growth and Development
  66. Kang M., 2007, Functional‐Structural Plant Modeling in Crop Production
  67. 10.1016/S1161-0301(02)00108-9 · DOI · Paper page
  68. Keating B.A., 1999, Modeling sugarcane production systems. I. Development and performance of the sugarcane module, Field Crops Research, 61, 253, 10.1016/S0378-4290(98)00167-1 · DOI · Paper page
  69. Keating B.A., 1997, Intensive Sugarcane Production: Meeting the Challenges Beyond 2000, 221
  70. 10.1016/j.agsy.2009.09.002 · DOI
  71. Langellier P., 2007, Crop modeling assessment of the potential regional irrigated sugarcane production increase, Sugar Cane International, 25, 11
  72. Lecler N., 2003, A model for the evaluation of irrigation and water management systems in the lowveld of Zimbabwe: model development and verification, Proceedings South African Sugar Technologists' Association, 77, 322
  73. 10.1016/S0308-521X(02)00112-9 · DOI
  74. 10.1016/j.fcr.2005.01.010 · DOI
  75. 10.1016/0304-3800(95)00223-5 · DOI
  76. Liu D.L., 1996, QCANE and armyworms: to spray or not to spray, that is the question, Proceedings Australian Society of Sugar Cane Technologists, 18, 106
  77. 10.1016/S0304-3800(01)00372-6 · DOI
  78. Liu D.L., 1995, Research and Modeling Approaches to Assess Sugarcane Production Opportunities and Constraints, 144
  79. 10.2135/cropsci2004.0370 · DOI
  80. 10.2135/cropsci1963.0011183X000300010021x · DOI
  81. 10.13031/2013.32749 · DOI
  82. Lumsden T.G., 1998, Simulation of sugarcane yield at the scale of a mill supply area, Proceedings South African Sugar Technologists' Association, 72, 12
  83. 10.1071/FP05266 · DOI
  84. Marcelis L.F.M., 2007, Functional‐Structural Plant Modeling in Crop Production, 269
  85. Martiné J.F., 2007, Analysis and forecasting of the sucrose content of sugarcane crops during the harvest period in Reunion Island, Proceedings International Society of Sugar Cane Technologists, 26, 607
  86. 10.1051/agro:19990101 · DOI
  87. Martiné J.F., 2004, Le modèle de croissance MOSICAS et sa plateforme de simulation simulex: état des lieux et perspectives, Revue agricole et sucrière de l'île Maurice, 80, 133
  88. 10.2135/cropsci2001.41140x · DOI
  89. 10.1111/j.1469-8137.2006.01785.x · DOI
  90. 10.1016/0308-521X(94)00055-V · DOI
  91. McCree K.J., 1970, Prediction and Measurement of Photosynthetic Productivity, 221
  92. McDonald L.M.&Lisson S.N.(2001)The effect of planting and harvest time on sugarcane productivity.Proceedings of the 10th Australian Agronomy Conference.Hobart Australia.
  93. McGlinchey M.G., 1999, Computer crop model applications. Developments in Swaziland, Proceedings South African Sugar Technologists' Association, 73, 35
  94. McGlinchey M.G., 1996, Evapotranspiration and Irrigation scheduling, 592
  95. McGlinchey M.G., 1996, Effect of irrigation scheduling on water use efficiency and yield, Proceedings South African Sugar Technologists' Association, 70, 55
  96. McGlinchey M.G., 1995, An irrigation scheduling model based on a crop model and an automatic weather station, Proceedings South African Sugar Technologists' Association, 69, 69
  97. Messina C.D. Jones J.W. &Boote K.W.(2004)Linking biophysical models plant genomics and optimization algorithms for plant ideotype design p.53.Biological Systems Simulation Conference Gainesville Florida March 8–10.
  98. 10.1101/gr.198801 · DOI
  99. Ming R., 2002, Molecular dissection of complex traits in autopolyploids: mapping QTLs affecting sugar yield and related traits in sugarcane, Theoretical & Applied Genetics, 105, 332, 10.1007/s00122-001-0861-5 · DOI
  100. 10.13031/2013.35202 · DOI
  101. 10.1098/rstb.1977.0140 · DOI · Paper page
  102. 10.1002/qj.49710745102 · DOI
  103. Muchow R.C., 1997, Intensive Sugarcane Production: Meeting the Challenges Beyond 2000, 37
  104. 10.1071/AR9940037 · DOI
  105. 10.1016/S0378-4290(00)00112-X · DOI
  106. 10.1016/j.fcr.2005.01.025 · DOI
  107. 10.2134/agronj1996.00021962008800050002x · DOI
  108. 10.1098/rspa.1948.0037 · DOI
  109. 10.1016/0022-5193(74)90119-2 · DOI
  110. 10.1590/S0006-87051986000100010 · DOI
  111. 10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2 · DOI
  112. Promburom P., 2001, Estimating sugarcane yields with Oy‐Thai interface, Proceedings International Society Sugar Cane Technologists, 24, 81
  113. Ramburan S., 2009, Effects of variety, harvest age and Eldana on coastal sugarcane production in South Africa, Proceedings South African Sugar Technologists' Association, 82, 580
  114. Redshaw K.A., 2001, Yield and quality differences between irrigated sugarcane varieties, Proceedings South African Sugar Technologists' Association, 75, 160
  115. Redshaw K.A., 2002, Investigating two statistical techniques used in the analysis of sugarcane variety trials, Proceedings South African Sugar Technologists' Association, 76, 219
  116. 10.1104/pp.013839 · DOI · Paper page
  117. Ritchie J.T., 1985, CERES‐Wheat: A Simulation Model of Wheat Growth and Development
  118. 10.1071/A98051 · DOI
  119. 10.1016/0378-4290(96)00041-X · DOI
  120. 10.1016/B978-0-12-244350-3.50010-0 · DOI
  121. Schulze R.E., 1995, Hydrology and agrohydrology: A Text to Accompany the ACRU 3.00 Agrohydrological Modeling System
  122. 10.1016/S0065-2113(08)60914-1 · DOI
  123. 10.2134/agronj1996.00021962008800050004x · DOI
  124. Singels A., 2007, A new approach to implementing computer‐based decision support for sugarcane farmers and extension staff. The case of My Canesim, Proceedings International Society Sugar Cane Technologists, 26, 211
  125. 10.1016/S0378-4290(02)00118-1 · DOI
  126. Singels A., 1999, Evaluating strategies for scheduling supplementary irrigation of sugarcane in South Africa, Proceedings Australian Society Sugar Cane Technologists, 21, 219
  127. 10.1016/0308-521X(91)90045-C · DOI
  128. Singels A., 2000, A simple model of unstressed sugarcane canopy development, Proceedings South African Sugar Technologists' Association, 74, 151
  129. 10.1016/j.fcr.2005.01.022 · DOI
  130. Singels A., 2008, DSSAT v4.5 Canegro Sugarcane Plant Module: Scientific Documentation, 34
  131. Singels A., 2010, The DSSAT4.5 Canegro model: a useful decision support tool for research and management of sugarcane production, Proceedings International Society of Sugar Cane Technologists, 27
  132. Singels A., 1998, IRRICANE: a simple computerized irrigation scheduling method for sugarcane, Proceedings South African Sugar Technologists' Association, 72, 117
  133. Singels A., 1999, Weather based decision support through the Internet for the agronomic management of sugarcane, Proceedings South African Sugar Technologists' Association, 73, 30
  134. Singels A., 2010, Review of South African sugarcane production in the 2009–2010 season from an agricultural perspective, Proceedings South African Sugar Technologists' Association, 83, 29
  135. 10.1016/j.fcr.2009.04.015 · DOI
  136. Singels A., 2010, Identifying quantitative trait alleles for physiological traits in sugarcane: an exploratory study, Sugar Cane International, 28, 53
  137. 10.1016/j.fcr.2005.01.028 · DOI
  138. 10.1002/ird.231 · DOI
  139. 10.1016/j.fcr.2010.02.003 · DOI
  140. 10.1007/BF00270883 · DOI
  141. 10.1016/j.fcr.2005.12.009 · DOI
  142. Smit M.A., 2007, Quantifying the effects of environment and genotype on stalk elongation rate in sugarcane, Proceedings International Society of Sugar Cane Technologists, 26, 568
  143. 10.1007/s00271-007-0064-1 · DOI
  144. 10.2134/agronj2008.0139s · DOI · Paper page
  145. 10.1016/j.agwat.2005.05.002 · DOI
  146. 10.1016/S1161-0301(02)00109-0 · DOI · Paper page
  147. 10.1016/0308-521X(92)90067-X · DOI
  148. Supit I. Hooijer A.A. &Diepen C.A.(1994)System description of the WOFOST 6.0 crop simulation model implemented in CGMS.Volume 1: Theory and Algorithms p.146.Joint Research Centre of the European Commission Luxembourg: Office for Official Publications of the European Communities EUR 15956.
  149. Tanner C.B., 1983, Limitations to Efficient Water Use in Crop Production
  150. 10.1046/j.1469-8137.1999.00433.x · DOI
  151. Thompson G.D., 1976, Water use by sugarcane, South African Sugar Journal, 60, 593
  152. Thompson G.D., 1986, A computerized model for evaluating irrigation schemes for sugarcane, Proceedings South African Sugar Technologists' Association, 60, 177
  153. 10.1016/j.agee.2009.12.014 · DOI
  154. Thorburn P.J., 2001, Nitrate in groundwaters in the Australian sugar industry, Proceedings International Society of Sugar Cane Technologists, 24, 131
  155. 10.1007/s00271-003-0086-2 · DOI
  156. 10.1016/j.fcr.2005.01.016 · DOI
  157. Thorburn P.J., 1999, Impacts of trash retention on soil nitrogen and water: an example from the Australian sugarcane industry, Proceedings South African Sugar Technologists' Association, 73, 75
  158. Thorburn P.J., 2001, Impact of trash blanketing on soil nitrogen fertility: Australian and South African experience, Proceedings International Society of Sugar Cane Technologists, 24, 33
  159. Thorburn P.J., 2002, The impact of trash management on soil carbon and nitrogen. I: modeling long‐term experimental results in the South African sugar industry, Proceedings South African Sugar Technologists' Association, 76, 260
  160. 10.1038/227304b0 · DOI
  161. Thornley J.H.M., 1976, Mathematical Models in Plant Physiology, 318
  162. Tovey D.A., 1977, SUCROWS I: a simulation model of sugar‐cane growth, Proceedings of the Queensland Society of Sugar Cane Technologists, 44, 149
  163. 10.1007/978-94-017-3624-4_18 · DOI
  164. van Antwerpen R., 2002, The impact of trashing on soil carbon and nitrogen: II: implications for sugarcane production in South Africa, Proceedings South African Sugar Technologists' Association, 76, 269
  165. 10.1039/df9480300146 · DOI
  166. 10.1093/jxb/erq144 · DOI
  167. van Keulen H., 1982, Simulation of Plant Growth and Crop Production, 78
  168. van Keulen H., 1987, Simulation Monographs
  169. van Keulen H., 1986, Simulation Monographs, 479
  170. 10.1046/j.1365-313X.1997.12051011.x · DOI
  171. Wagenet R.J., 1988, Modeling Agricultural, Forest and Rangeland Hydrology. American Society of Agricultural Engineers, 1
  172. 10.1071/MF08348 · DOI
  173. 10.1016/S0065-2113(08)60282-5 · DOI · Paper page
  174. White J.W., 1996, Simulating effects of genes for physiological traits in a process‐oriented crop model, Agronomy Journal, 88, 416, 10.2134/agronj1996.00021962008800030009x · DOI
  175. 10.13031/2013.33877 · DOI
  176. 10.1093/aob/mch078 · DOI
  177. Yin X., 2003, Crop modeling, QTL mapping, and their complementary role in plant breeding, Agricultural Journal, 95, 90
  178. 10.1016/j.tplants.2004.07.007 · DOI

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Metadata

Authors
Singels, A.
Affiliations
South African Sugarcane Research Institute (ZA)
Corresponding author
Singels, Abraham
ORCID
Singels, Abraham
Venue
Sugarcane: Physiology, Biochemistry, and Functional Biology
Publisher
Wiley
Published
2013-12-06
Pages
541-577
DOI
10.1002/9781118771280.ch20
OpenAlex
W2915028857
Cited by
11
References
3
Citations by year
2023: 1 · 2021: 2 · 2020: 2 · 2019: 3 · 2018: 1 · 2016: 2
Type
other
Language
en
Open access
closed
Topics
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Sugarcane Cultivation and Processing
  • Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Agriculture, Soil, Plant Science
  • Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Crop Yield and Soil Fertility
Keywords
crop models, sugarcane, crop growth simulation, crop yield simulation, soil-plant-atmosphere system, crop physiology, crop development, production management, empirical yield model, crop improvement

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