CSM-CROPGRO-Peanut

Peanut or groundnut (Arachis hypogaea L.) is a legume grown primarily for its edible seed, although its fodder is used as an animal feed especially in developing countries. Because of the high oil content of the seed, peanut is considered both a grain legume and an oil seed crop. As a legume, the crop can fix nitrogen via symbiosis and is valued for its low nitrogen fertilizer requirement and as a source of nitrogen in rotations. The crop is generally grown in tropical and sub-tropical regions. China, India, the United States, and Nigeria are the top producers.

The CSM-CROPGRO-Peanut model (Boote et al., 1998) evolved from the PNUTGRO model (Boote et al., 1986, 1989b, 1991) and similar models. Early versions were tested in India (Singh et al., 1994a, 1994b) and with on-farm trials in Florida (Boote et al., 1989a; Gilbert, 1992; Gilbert et al., 2002). Between 1990 and 1994, more mechanistic features of leaf-level photosynthesis, hedge-row canopy photosynthesis, explicit N2–fixation, explicit soil N uptake, and soil N balance were added to create the CROPGRO-legume model, which subsequently evolved to become the CSM-CROPGRO model (Hoogenboom et al., 1992, Jones et al., 2003).

Species parameters and cultivar traits are entered as external input files. Boote et al. (1998) described how CROPGRO simulates the daily processes of crop development, crop C balance, crop and soil N balance, and soil water balance. The model also has coupling points and procedures for entering pest damage to simulate hypothetical growth and yield reductions due to such factors as percentage leaf area defoliation or percentage necrosis (Batchelor et al., 1993; Boote et al., 1993).

As a process-oriented model, the CSM-CROPGRO-Peanut can be used to study how peanut crops respond to management (McClendon et al., 1996; Dangthaisong et al., 2006; Paz et al., 2007; Soler et al., 2013), environmental conditions (Garcia y Garcia et al., 2006; Paz et al., 2007; Phakamas et al., 2013; Woli et al., 2013), soil fertility (Naab et al., 2015), leafspot disease (Singh et al., 2013a), and plant breeding (Anothai et al., 2008, 2009; Banterng et al., 2003, 2004, 2006; Guerra et al., 2008; Narh et al., 2015; Phakamas et al., 2010; Putto et al, 2009, 2013; Singh et al., 2012; Suriharn et al., 2007, 2008, 2011). This includes evaluating causes for yield gaps (Naab  et al., 2004) and potential impacts of climate change (e.g., Boote et al., 2018; Singh et al., 2013b).

References

  • Anothai, J., A. Patanothai, S. Jogloy, K. Pannangpetch, K.J. Boote, and G. Hoogenboom. 2008. A sequential approach for determining the cultivar coefficients of peanut lines using end-of-season data of crop performance trials. Field Crops Research 108:169-178.
  • Anothai, J., A. Patanothai, K. Pannangpetch, S. Jogloy, K.J. Boote, and G. Hoogenboom. 2009. Multi environment evaluation of peanut lines by model simulation with the cultivar coefficients derived from a reduced set of observed field data. Field Crops Research 110(2):111-122.
  • Banterng, P., A. Patanothai, K. Pannangpetch, S. Jogloy and G. Hoogenboom. 2003. Applicability of the CROPGRO-Peanut model in assisting multi-location evaluation of peanut breeding lines. Thai Journal of Agricultural Science 37(7):407-418.
  • Banterng, P., A. Patanothai, K. Pannangpetch, S. Jogloy, and G. Hoogenboom. 2004. Determination of genetic coefficients of peanut lines for breeding applications. European Journal of Agronomy 21(3):297-310.
  • Banterng, P., A. Patanothai, K. Pannangpetch, S. Jogloy, and G. Hoogenboom. 2006. Yield stability evaluation of peanut lines: a comparison of an experimental versus a simulation approach. Field Crops Research 96(1):168-175.
  • Batchelor, W.D., J.W. Jones, K.J. Boote, and H.O. Pinnschmidt. 1993. Extending use of crop models to study pest damage. Trans. ASAE 36, 551–58.
  • Boote, K.J., J.W. Jones, J.W. Mishoe, and G.G. Wilkerson. 1986. Modeling growth and yield of groundnut. p. 243–254. In Agrometeorology of groundnut. Proc. Int. Symp., ICRISAT Sahelian Center, Niamey, Niger. 21–26 Aug. 1985. ICRISAT, Patancheru, Andhra Pradesh, India.
  • Boote, K.J., J.W. Jones, G. Hoogenboom, G.G. Wilkerson, and S.S. Jagtap. 1989b. PNUTGRO V1.02. Peanut crop growth simulation model. User’s guide. Florida Agric. Exp. Stn., Journal no. 8420. Univ. of Florida, Gainesville.
  • Boote, K.J., J.W. Jones, and P. Singh. 1991. Modeling growth and yield of groundnut—state of the art. p. 331–343. In S.N. Nigam (ed.) Groundnut—a global perspective. Proc. Int. Workshop, ICRISAT Center, Patancheru, AP, India. 25–29 Nov. 1991. ICRISAT, Patancheru, AP, India.
  • Boote, K.J., Batchelor, W.D., Jones, J.W., Pinnschmidt, H. and Bourgeois, G., 1993. Pest damage relations at the field level. In Systems approaches for agricultural development (pp. 277-296). Springer, Dordrecht.
  • Boote, KJ, JW Jones, G Hoogenboom, and NB Pickering. 1998. Understanding Options for Agricultural Production. In The CROPGRO Model for Grain Legumes, 99–128. Springer, 1998.
  • Boote, K.J., Prasad, V., Allen Jr, L.H., Singh, P. and Jones, J.W., 2018. Modeling sensitivity of grain yield to elevated temperature in the DSSAT crop models for peanut, soybean, dry bean, chickpea, sorghum, and millet. European Journal of Agronomy, 100, 99-109.
  • Dangthaisong, P., P. Banterng, S. Jogloy, N. Vorasoot, A. Patanothai, and G. Hoogenboom. 2006. Evaluation of the CSM-CROPGRO-Peanut model in simulating responses of two peanut cultivars to different moisture regimes. Asian Journal of Plant Sciences 5(6):913-922.
  • Garcia y Garcia, A., G. Hoogenboom, L.C. Guerra, J.O. Paz, and C.W. Fraisse. 2006. Analysis of the interannual variation of peanut yield in Georgia using a dynamic crop simulation model. Transactions of the American Society of Agricultural and Biological Engineers 49(6):2005-2015.
  • Gilbert, R.A. 1992. On-farm testing of the PNUTGRO crop model in Florida. M.S. thesis. Univ. of Florida, Gainesville.
  • Gilbert, RA, Boote, KJ and Bennett, JM. 2002. On-farm testing of the PNUTGRO crop growth model in Florida. Peanut Sci. 29:58–65.
  • Guerra, L.C., G. Hoogenboom, A. Garcia y Garcia, P. Banterng, and J.P. Beasley, Jr. 2008. Determination of cultivar coefficients for the CSM-CROPGRO-Peanut model using variety trial data. Transactions of the American Society of Agricultural and Biological Engineers 51(4):1471-1481.
  • Hoogenboom, G. J. W. Jones, and K. J. Boote. 1992. Modeling growth, development and yield of grain legumes using SOYGRO, PNUTGRO, and BEANGRO: A Review. Transactions of the ASAE 35(6):2043-2056.
  • Jones, J.W., G. Hoogenboom, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, and J.T. Ritchie. 2003. The DSSAT Cropping System Model. European Journal of Agronomy 18:235–65.
  • McClendon, R. W., G. Hoogenboom, and I. Seginer. 1996. Optimal control and neural networks applied to peanut irrigation management. Transactions of the ASAE 39(1):275-279.
  • Naab, J. B., K.J. Boote, J. W. Jones, and C. H. Porter.  2015.  Adapting and evaluating the CROPGRO-peanut model for response to phosphorus on a sandy-loam soil under semi-arid tropical conditions. Field Crops Research 176:71–86.
  • Naab, J. B., P. Singh, K. J. Boote, J. W. Jones, and K. O. Marfo.  2004.  Using the CROPGRO-peanut model to quantify yield gaps of peanut in the Guinean savanna zone of Ghana.  Agron. J. 96:1231-1242.
  • Narh, S., K. J. Boote, J. B. Naab, J. W. Jones, B. L. Tillman, M. Abudulai, P. Sankara, Z. M. Bertin, M. D. Burow, R. L. Brandenburg and D. L. Jordan.  2015.  Genetic improvement of peanut cultivars for West Africa evaluated with the CSM-CROPGRO-Peanut Model.  Agron. J. 106:2213-2229.
  • Paz, J.O., C.W. Fraisse, L.U. Hatch, A. Garcia y Garcia, L.C. Guerra, O. Uryasev, J.G. Bellow, J.W. Jones, and G. Hoogenboom. 2007. Development of an ENSO based irrigation decision support tool for peanut production in the southeastern US. Computers and Electronics in Agriculture 55(1):28-35.
  • Phakamas, N., A. Patanothai, K. Pannangpetch, S. Jogloy and G. Hoogenboom. 2010.Determination of adaptive responses of peanut genotype * location interaction using the CSM-CROPGRO-Peanut model. International Journal of Plant Production 4(3): 223 233.
  • Phakamas, N., A. Jintrawet, A. Patanothai, P. Sringam, and G. Hoogenboom. 2013. Estimation of solar radiation based on air temperature and application with the DSSAT v4.5 peanut and rice simulation models in Thailand. Agricultural and Forest Meteorology 180(1):182-193.
  • Putto, W., A. Patanothai, S.Jogloy, K. Pannangpetch, K.J. Boote, and G. Hoogenboom. 2009. Determination of efficient test sites for evaluation of peanut breeding lines using the CSM-CROPGRO-Peanut model. Field Crops Research 110(3):272-281.
  • Putto, C., A. Patanothai, S.Jogloy, K.J. Boote, and G. Hoogenboom. 2013. Determination of plant traits that affect genotype x location (G x L) interaction in peanut using the CSM-CROPGRO-Peanut model. International Journal of Plant Production 7(3):537-568.
  • Singh, M. P., J. E. Erickson, K. J. Boote, J. W. Jones, B. L. Tillman, and A. H. C. van Bruggen.  2013a.  Using the CSM-CROPGRO-Peanut model to simulate late leaf spot effects on peanut cultivars of differing resistance.  Agron. J. 105:1307-1316.
  • Singh, P., K. J. Boote, A. Yogeswara Rao, M. R. Iruthayaraj, A. M. Sheikh, S. S. Hundal. R. S. Narang, and Phool Singh.  1994a.  Evaluation of the groundnut model PNUTGRO for crop response to water availability, sowing dates, and seasons.  Field Crops Res. 39:147-162.
  • Singh, P., K. J. Boote, and S. M. Virmani.  1994b.  Evaluation of the groundnut model PNUTGRO for crop response to plant population and row spacing.  Field Crops Res. 39:163-170.
  • Singh, P., K. J. Boote, U. Kumar, K. Srinivas, S. N. Nigam, and J. W. Jones.  2012.  Evaluation of genetic traits for improving productivity and adaptation of groundnut to climate change in India.  J. of Agronomy and Crop Sci. 198:399-413.  
  • Singh, P., S. Nedumaran, B. R. Ntare, K. J. Boote, N. P. Singh, K. Srinivas, and M. C. S. Bantilan.  2013b.  Potential benefits of drought and heat tolerance in groundnut for adaptation to climate change in India and West Africa.  Mitig. Adapt. Strateg. Glob. Change 19:509-529 (doi 10.1007/s1027-012-9446-7).
  • Soler, C.M.T., A. Suleiman, J. Anothai, I. Flitcroft, and G. Hoogenboom. 2013. Scheduling irrigation with a dynamic crop growth model and determining the relation between simulated drought stress and yield for peanut. Irrigation Science 31(5):889-901. (doi:10.1007/s00271 012 0366 9).
  • Suriharn, B., A. Patanothai, K. Pannangpetch, S. Jogloy, and G. Hoogenboom. 2007. Determination of cultivar coefficients of peanut lines for breeding applications of the CSM-CROPGRO-Peanut model. Crop Science 47(2):607-619.
  • Suriharn, B., A. Patanothai, K. Pannangpetch, S. Jogloy, and G. Hoogenboom. 2008. Yield performance and stability evaluation of peanut breeding lines with the CSM CROPGRO Peanut model. Crop Science 48(4):1365-1372.
  • Suriharn, B., A. Patanothai, K.J. Boote, and G. Hoogenboom. 2011. Designing a peanut ideotype for a target environment using the CSM CROPGRO Peanut model. Crop Science 51(5):1887-1902.
  • Woli, P., J.O. Paz, G. Hoogenboom, A. Garcia y Garcia, and C.W. Fraisse. 2013. The ENSO effect on peanut yield as influenced by planting date and soil type. Agricultural Systems 121(1):1-8.