Skip to main content
SpringerLink
Log in

Scheduling irrigation with a dynamic crop growth model and determining the relation between simulated drought stress and yield for peanut

  • Original Paper
  • Published:
Irrigation Science Aims and scope Submit manuscript

Abstract

A computer simulation model can be used as a tool to help explain the impact of drought stress on plant growth and development because it integrates the complex soil–plant-atmosphere system through a set of mathematical equations. The objectives of this study were to determine the impact of different irrigation scheduling regimes on peanut growth and development, to determine the capability of the CSM-CROPGRO-Peanut model to simulate growth and development of peanut, and to determine the relationship between yield and the two cumulative drought stress indices simulated by the peanut model. The CSM-CROPGRO-Peanut model was evaluated with experimental data collected during two field experiments that were conducted in four automated rainout shelters located at The University of Georgia, USA, in 2006 and 2007. Irrigation was applied when the simulated soil water content in the effective root zone dropped below a specific threshold value for the available soil water capacity (AWC). The irrigation treatments corresponded to irrigation thresholds (IT) of 30, 40, 60, and 90 % of AWC. The results showed that growth and development was reduced for the 30 and 40 % IT treatments which resulted in yield reductions that were 92 and 45 %, respectively, of the 90 % IT treatment. The Cropping System Model (CSM)-CROPGRO-Peanut model was able to accurately simulate growth and development of peanut grown under different irrigation treatments when compared to the observed data. We found an inverse relationship between the two simulated total cumulative drought stress indices for leaf growth (expansion) and photosynthesis and simulated pod yield. Knowing the cumulative drought stress value prior to harvest maturity could help with the prediction of potential harvestable yield.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Abou Kheira AA (2009) Macromanagement of deficit-irrigated peanut with sprinkler irrigation. Agric Water Manag 96:1409–1420

    Article  Google Scholar 

  • Allen RG, Pereira LA, Raes D, Smith M (1998) Crop evapotranspiration. FAO Irrigation and Drainage Paper 56. FAO, Rome, Italy, 293 p

  • Anothai J, Patanothai A, Pannangpetch K, Jogloy S, Boote KJ, Hoogenboom G (2008) Reduction in data collection for determination of cultivar coefficients for breeding applications. Agric Syst 96:195–206

    Article  Google Scholar 

  • Arunyanark A, Jogloy S, Akkasaeng C, Vorasoot N, Kesmala T, Nageswara Rao RC, Wright GC, Patanothai A (2008) Chlorophyll stability is an indicator of drought tolerance in peanut. J Agron Crop Sci 194:113–125

    Article  CAS  Google Scholar 

  • Bandyopadhyay PK, Mallick S, Rana SK (2005) Water balance and crop coefficients of summer-grown peanut (Arachis hypogaea L.) in a humid tropical region of India. Irrig Sci 23:161–169

    Article  Google Scholar 

  • Beasley JP (2007) Irrigation strategies. In: Prostko EP (ed) 2007 Peanut update. The University of Georgia Cooperative Extension Service. http://www.caes.uga.edu/commodities/fieldcrops/peanuts/2007peanutupdate/irrig.html. Accessed 10 July 2012

  • Bhatia VS, Singh P, Kesava Rao AVR, Srinivas K, Wani SP (2009) Analysis of water non-limiting and water limiting yields and yield gaps of groundnut in India using CROPGRO-peanut model. J Agron Crop Sci 195:455–463

    Article  Google Scholar 

  • Black CR, Tang TY, Ong CK, Solon A, Simmonds LP (1985) Effect of soil moisture stress on the water relations and water use of groundnut stands. New Phytol 100:313–328

    Article  Google Scholar 

  • Boote KJ, Ketring DL (1990) Peanut. In: Stewart BA, Nielsen DR (eds) Irrigation of agricultural crops. ASA, CSSA, and SSSA, Madison, pp 675–717

    Google Scholar 

  • Boriss H, Kreith M (2006) Commodity profile: peanuts. agricultural issues center. University of California. http://aic.ucdavis.edu/profiles/Peanuts-2006.pdf. Accessed 4 April 2012

  • Branch W (2008) 2008 Peanut update. The University of Georgia Breeding Program. http://www.caes.uga.edu/commodities/fieldcrops/peanuts/2008peanutupdate/breeding.html#green. Accessed 4 April 2012

  • Chapman SC, Ludlow MM, Blamey FPC, Fischer KS (1993) Effect of drought during early reproductive development on growth of cultivars of groundnut (Arachis hypogaea L.). Field Crop Res 32:193–210

    Article  Google Scholar 

  • Chauhan YS, Wright GC, Holzworth D, Rachaputi RCN, Payero JO (2011) AQUAMAN: a web-based decision support system for irrigation scheduling in peanuts. Irrig Sci. doi:10.1007/s00271-011-0296-y

  • Chipanshi AC, Ripley EA, Lawford RG (1997) Early prediction of spring wheat yields in Saskatchewan from current and historical weather data using the CERES-Wheat model. Agric For Meteorol 84:223–232

    Article  Google Scholar 

  • Chipanshi AC, Ripley EA, Lawford RG (1999) Large-scale simulation of wheat yields in a semi-arid environment using a crop-growth model. Agric Syst 59:57–66

    Article  Google Scholar 

  • Collino DJ, Dardanelli JL, Sereno R, Racca RW (2000) Physiological responses of Argentine peanut varieties to water stress. Water uptake and water use efficiency. Field Crop Res 68:133–142

    Article  Google Scholar 

  • Da Assunção HF, Escobedo JF (2009) Use of an agrometeorological model to computing the peanut water requirements. Irriga 14:325–335

    Google Scholar 

  • Dangthaisong P, Banterng P, Jogloy S, Vorasoot N, Patanothai A, Hoogenboom G (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

    Article  Google Scholar 

  • Devi MJ, Sinclair TR, Vadez V (2010) Genotypic variability among peanut (Arachis hypogea L.) in sensitivity of nitrogen fixation to soil drying. Plant Soil 330:139–148

    Article  CAS  Google Scholar 

  • Dugan E, Adiku SGK (2006) Application of CROPGRO-peanut model to evaluate groundnut growth and yield in some farming zones of Ghana. The 18th world congress of soil science (July 9–15, 2006). Philadelphia, PA

  • El-Boraie FM, Abo-El-Ela HK, Gaber AM (2009) Water requirements of peanut grown in sandy soil under drip irrigation and biofertilization. Aust J Basic Appl Sci 3:55–65

    CAS  Google Scholar 

  • Garcia y Garcia A, Hoogenboom G, Guerra LC, Paz JO, Fraisse CW (2006) Analysis of the inter-annual variation of peanut yield in Georgia using a dynamic crop simulation model. Trans ASABE 49(6):2005–2015

    Google Scholar 

  • Gardner FP, Auma EO (1989) Canopy structure, light interception, and yield and market quality of peanut genotypes as influenced by planting pattern and planting date. Field Crop Res 20:13–29

    Article  Google Scholar 

  • Garrison MV, Batchelor WD, Kanwar RS, Ritchie JT (1999) Evaluation of the CERES-Maize water and nitrogen balances under tile-drained conditions. Agric Syst 62:189–200

    Article  Google Scholar 

  • Guerra LC, Hoogenboom G, Garcia y Garcia A, Hook JE (2004) Simulating peanut yield and irrigation applications with the CSM-CROPGRO-peanut model, paper # 043046. ASAE annual international meeting, Ottawa, Ontario, Canada

  • Haro RJ, Otegui ME, Collino DJ, Dardanelli JL (2007) Environmental effects on seed yield determination of irrigated peanut crops: links with radiation use efficiency and crop growth rate. Field Crop Res 103:217–228

    Article  Google Scholar 

  • Haro RJ, Dardanelli JL, Otegui ME, Collino DJ (2008) Seed yield determination of peanut crops under water deficit: soil strength effects on pod set, the source-sink ratio and radiation use efficiency. Field Crop Res 109:24–33

    Article  Google Scholar 

  • Haro JR, Mantese A, Otegui ME (2011) Peg viability and pod set in peanut: response to impaired pegging and water deficit. Flora Morphology, Distribution, Functional Ecology of Plants 206:865–871. doi:10.1016/j.flora.2011.05.003

    Article  Google Scholar 

  • Harris D, Matthews RB, Nageswara Rao RC, Williams JH (1988) The physiological basis for yield differences between four genotypes of groundnut (Arachis Hypogaea) in response to drought. III. Developmental processes. Exp Agric 24:215–226

    Article  Google Scholar 

  • Hoogenboom G, Jones JW, Wilkens PW, Porter CH, Batchelor WD, Hunt LA, Boote KJ, Singh U, Uryaseva O, Bowen WT, Gijsman AJ, Du Toit AS, White JW, Tsuji GY (2004) Decision support system for agrotechnology transfer version 4.0. [CD-ROM]. University of Hawaii, Honolulu, HI

  • Hoogenboom G, Jones JW, Wilkens PW, Porter CH, Boote KJ, Hunt LA, Singh U, Lizaso JL, White JW, Uryasev O, Royce FS, Ogashi R, Gijsman AJ, Tsuji GY (2010) Decision support system for agrotechnology transfer version 4.5. [CD–ROM]. University of Hawaii, Honolulu, HI

  • Hunt LA, Parajasingham S, Jones JW, Hoogenboom G, Imamura DT, Ogoshi RM (1993) Gencalc—software to facilitate the use of crop models for analyzing field experiments. Agron J 85:1090–1094

    Article  Google Scholar 

  • Ishag HM (1982) The influence of irrigation frequency on growth and yield of groundnuts (Arachis hypogaea L.) under arid conditions. J Agric Sci 99:305–310

    Article  Google Scholar 

  • Jain LL, Panda RK, Sharma CP (1997) Water stress response function for groundnut (Arachis hypogaea L.). Agric Water Manag 32:197–209

    Article  Google Scholar 

  • Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) DSSAT cropping system model. Eur J Agron 18:235–265

    Article  Google Scholar 

  • Jongrungklang N, Toomsan B, Vorasoot N, Jogloy S, Kesmala T, Patanothai A (2008) Identification of peanut genotypes with high water use efficiency under drought stress conditions from peanut germplasm of diverse origins. Asian Journal of Plant Sciences 7:628–638

    Article  Google Scholar 

  • Jongrungklang N, Toomsan B, Vorasoot N, Jogloy S, Boote KJ, Hoogenboom G, Patanothai A (2011) Rooting traits of peanut genotypes with different yield responses to pre-flowering drought stress. Field Crop Res 120:262–270

    Article  Google Scholar 

  • Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Huth NI, Hargreaves JNG, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes JP, Silburn M, Wang E, Brown S, Bristow KL, Asseng S, Chapman S, McCown RL, Freebairn DM, Smith CJ (2003) An overview of APSIM, a model designed for farming systems simulation. Eur J Agron 18:267–288

    Article  Google Scholar 

  • Ketring DL, Wheless TG (1989) Thermal time requirements for phenological development of peanut. Agron J 81:910–917

    Article  Google Scholar 

  • Kiniry JR, Simpson CE, Schubert AM, Reed JD (2005) Peanut leaf area index, light interception, radiation use efficiency, and harvest index at three sites in Texas. Field Crop Res 91:297–306

    Article  Google Scholar 

  • Lamb MC Jr, Davidson JI, Childre JW Jr, Martin NR (1997) Comparison of peanut yield, quality, and net returns between non irrigated and irrigated production. Peanut Sci 24:97–101

    Article  Google Scholar 

  • Liu HL, Yang JY, Tan CS, Drury CF, Reynolds WD, Zhang TQ, Bai YL, Jin J, He P, Hoogenboom G (2011) Simulating water content, crop yield and nitrate-N loss under free and controlled tile drainage with subsurface irrigation using the DSSAT model. Agric Water Manag 98:1105–1111

    Article  Google Scholar 

  • Mavromatis T, Jagtap SS, Jones JW (2002) El Niño-southern oscillation effects on peanut yield and nitrogen leaching. Climate Research 22:129–140

    Article  Google Scholar 

  • Nageswara Rao RC, Sardar S, Sivakumar MVK, Srivastava KL, Williams JH (1985) Effect of water deficit at different growth phases of peanut. I. Yield responses. Agron J 77:782–786

    Article  Google Scholar 

  • Pandey RK, Herrera WAT, Pendleton JW (1984) Drought response of grain legumes under irrigation gradient: I. Yield and yield components. Agron J 76:549–553

    Article  Google Scholar 

  • Patel MS, Golakia BA (1988) Effect of water stress on yield attributes and yield of groundnut (Arachis hypogaea L.). Indian J Agric Sci 58:701–703

    Google Scholar 

  • Paz JO, Fraisse CW, Hatch LU, Garcia y Garcia A, Guerra LC, Uryasev O, Bellow JG, Jones JW, Hoogenboom G (2007) Development of an ENSO-based irrigation decision support tool for peanut production in the southeastern US. Computers and Electronics in Agriculture 55:28–35

    Article  Google Scholar 

  • Phakamas N, Patanothai A, Jogloy S, Pannangpetch K, Hoogenboom G (2008a) Physiological determinants for pod yield of peanut lines. Crop Sci 48:2351–2360

    Article  Google Scholar 

  • Phakamas N, Patanothai A, Pannangpetch K, Jogloy S, Hoogenboom G (2008b) Dynamic patterns of components of genotype × environment interaction for pod yield of peanut over multiple years: a simulation approach. Field Crop Res 106:9–21

    Article  Google Scholar 

  • Pimratch S, Jogloy S, Vorasoot N, Toomsan B, Patanothai A, Holbrook CC (2008) Relationship between biomass production and nitrogen fixation under drought-stress conditions in peanut genotypes with different levels of drought resistance. J Agron Crop Sci 194:15–25

    Article  Google Scholar 

  • Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100:81–92

    Article  Google Scholar 

  • Putnam DH, Oplinger ES, Teynor TM, Oelke1 EA, Kelling KA, Doll JD (1991) Peanut. Field crops manual. University of Wisconsin-Extension, Cooperative Extension; University of Minnesota, Center for Alternative Plant and Animal Products and the Minnesota Extension Service. http://www.hort.purdue.edu/newcrop/afcm/peanut.html. Accessed 4 April 2012

  • Reddy TY, Reddy VR, Anbumozhi V (2003) Physiological responses of groundnut (Arachis hypogaea L.) to drought stress and its amelioration: a critical review. Plant Growth Regul 41:75–88

    Article  CAS  Google Scholar 

  • Reynolds SG (1970) The gravimetric method of soil moisture determination: part I a study of equipment, and methodological problems. J Hydrol 11:258–273

    Article  Google Scholar 

  • Ritchie JT (1998) Soil water balance and plant stress. In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding options for agricultural production. Kluwer, Dordrecht, pp 41–54

    Chapter  Google Scholar 

  • Rowland D, Blankenship P, Puppala N, Beasley J, Burow M, Gorbet D, Jordan D, Melouk H, Simpson C, Bostick J (2004) Variation in water-use efficiency of peanut varieties across peanut production regions. In: Fischer T et al. (ed) New directions for a diverse planet: proceedings for the 4th international crop science congress, Brisbane, Australia, 26 September–1 October 2004

  • Rowland D, Faircloth WH, Dang PM, Powell Jr JL (2008) Acclimation of peanut (Arachis hypogaea L.) to water stress through exposure to differing periods of early season drought. ASA-CSSA-SSSA Annual Meeting Abstracts

  • Sarma PS, Sivakumar MVK (1989) Response of groundnut to drought stress in different growth phases. Agric Water Manag 15:301–310

    Article  Google Scholar 

  • SAS Institute (2001) SAS system, 8th edn. SAS Inst, Cary

    Google Scholar 

  • Smith NB, Smith AR (2011) 2011 Peanut outlook and cost analysis. In: 2011 peanut update. http://www.caes.uga.edu/commodities/fieldcrops/peanuts/2011peanutupdate/documents/2011PeanutUpdate.pdf. Accessed 4 April 2012

  • Soler CMT, Sentelhas PC, Hoogenboom G (2007) Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment. Eur J Agron 27:165–177

    Article  Google Scholar 

  • Songsri P, Jogloy S, Kesmala T, Vorasoot N, Akkasaeng C, Patanothai A, Holbrook CC (2008) Heritability of drought resistance traits and correlation of drought resistance and agronomic traits in peanut. Crop Sci 48:2245–2253

    Article  Google Scholar 

  • Songsri P, Jogloy S, Holbrook CC, Kesmala T, Vorasoot N, Akkasaeng C, Patanothai A (2009) Association of root, specific leaf area and SPAD chlorophyll meter reading to water use efficiency of peanut under different available soil water. Agric Water Manag 96:790–798

    Article  Google Scholar 

  • Staggenborg SA, Vanderlip RL (2005) Crop simulation models can be used as dryland cropping systems research tools. Agronomy Journal 97:378–384

    Article  Google Scholar 

  • Stirling CM, Black CR, Ong CK (1989) The response of groundnut (Arachis hypogaea L.) to timing of irrigation: II. 14C partitioning and plant water status. J Exp Bot 40:1363–1373

    Article  CAS  Google Scholar 

  • Stockle CO, Donatelli M, Nelson R (2003) CropSyst, a cropping systems simulation model. Eur J Agron 18:289–307

    Article  Google Scholar 

  • Suleiman AA, Soler CMT, Hoogenboom G (2011) Determining FAO-56 crop coefficients for peanut under different water stress levels. Irrig Sci. doi:10.1007/s00271-011-0301-5

  • Suriharn B, Patanothai A, Boote KJ, Hoogenboom G (2011) Designing a peanut ideotype for a target environment using the CSM-CROPGRO-Peanut model. Crop Sci 51:1887–1902

    Article  Google Scholar 

  • White JW, Hoogenboom G (2010) Crop response to climate: ecophysiological models. In: Lobell D, Burke M (eds) Climate change and food security, vol 37. Springer, The Netherlands, pp 59–83. doi:10.1007/978-90-481-2953-9_4

  • Willmott CJ, Akleson GS, Davis RE, Feddema JJ, Klink KM, Legates DR, Odonnell J, Rowe CM (1985) Statistics for the evaluation and comparison of models. J Geophys Res 90:8995–9005

    Google Scholar 

  • Wright GC, Hubick KT, Farquhar GD (1991) Physiological analysis of peanut cultivar response to timing and duration of drought stress. Aust J Agric Res 42:453–470

    Article  Google Scholar 

  • Zhao H, Gao G, Yan X, Zhang Q, Hou M, Zhu Y, Tian Z (2011) Risk assessment of agricultural drought using the CERES-Wheat model: a case study of Henan Plain, China. Climate Research 50:247–256

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially supported by State and Federal funds allocated to the Georgia Agricultural Experiment Stations Hatch project GEO01654 and a special grant from the U.S. Department of Agriculture-Coperative State Research, Education and Extension Service (CSREES).

Author information

Author notes

  1. Ian Flitcroft

    Present address: Department of Crop and Soil Sciences, The University of Georgia, 1109 Experiment Street, Griffin, GA, 30223-1797, USA

  2. Cecilia M. Tojo Soler, Jakarat Anothai & Gerrit Hoogenboom

    Present address: AgWeatherNet, Washington State University, 24106 North Bunn Road, Prosser, WA, 99350-8694, USA

  3. Ayman Suleiman

    Present address: Department of Land, Water and Environment, Faculty of Agriculture, University of Jordan, Amman, Jordan

Authors and Affiliations

  1. Department of Biological and Agricultural Engineering, The University of Georgia, 1109 Experiment Street, Griffin, GA, 30223-1797, USA

    Cecilia M. Tojo Soler, Ayman Suleiman, Jakarat Anothai & Gerrit Hoogenboom

Authors
  1. Cecilia M. Tojo Soler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayman Suleiman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jakarat Anothai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ian Flitcroft
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gerrit Hoogenboom
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cecilia M. Tojo Soler.

Additional information

Communicated by S. O. Shaughnessy.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tojo Soler, C.M., Suleiman, A., Anothai, J. et al. Scheduling irrigation with a dynamic crop growth model and determining the relation between simulated drought stress and yield for peanut. Irrig Sci 31, 889–901 (2013). https://doi.org/10.1007/s00271-012-0366-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00271-012-0366-9

Keywords

Search

Navigation