Eddy covariance quantification of soybean (Glycine max L.,) crop coefficients in a farmer’s field in a humid climate

Abstract

For sustainable irrigated agriculture, scheduling irrigations based on accurate estimates of crop water requirements (ETc, crop evapotranspiration) are critical. ETc was estimated as a product of a reference crop evapotranspiration computed from weather data and a crop coefficient (Kc) in weather-based irrigation scheduling. In this investigation, an eddy covariance (EC) method was used for quantifying soybean (cv. Asgro 46X4) Kc in a farmer’s field under a humid climate. ETc quantified using the EC method was used for developing Kc for alfalfa (Kcr) and grass (Kco) reference crops computed from measured weather data. Experiments were conducted during three crop seasons (2017–2019) in a 500-ha furrow-irrigated soybean field—planted in silt loam soil in late April to early May and harvested in September. Harvested soybean yields were 4771, 5783, and 4909 kg ha−1, consuming 584, 640, and 593 mm ETc (average 605 mm), respectively, in 2017, 2018, and 2019. Monthly averaged daily ETc across the crop seasons varied between 2.1 mm in May 2019 to 6.2 mm in June 2018. Seasonally averaged daily ETc across the three crop seasons varied between 4.3 and 5.2 mm with an average of 4.8 mm. Across the crop seasons, ETc was 22% less and 2% greater than computed grass (ETo) and alfalfa (ETr) reference crop evapotranspiration. Monthly averaged daily Kco varied between 0.79 and 1.18, and Kcr ranged between 0.65 and 0.97. The Kc established can help develop soybean irrigation schedules, across climates and soils, based on ETo or ETr computed from real-time weather data.

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References

  1. Allen RG, Pereira LS, Raes D, Smith M, (1998) Crop evapotranspiration: guidelines for computing crop water requirement. United Nations Food and Agriculture Organization, Irrigation and Drainage Paper 56, Rome, Italy

  2. Allen RG, Wright JL, Pruitt WO, Pereira LS (2007) Water requirements. In: Design and operation of farm irrigation systems, chap 8, 2nd Edn. ASAE Monograph

  3. Anapalli SS, Fisher DK, Reddy KN, Pettigrew WT, Sui R, Ahuja LR (2016a) Vulnerability and adaptation of cotton to climate change in the Mississippi Delta. Climate 4(55):1–20

    Google Scholar 

  4. Anapalli SS, Pettigrew WT, Reddy KN, Ma L, Fisher DK, Sui R (2016b) Climate optimized planting windows for cotton in the Lower Mississippi Delta Region. Agronomy 6(46):1–15

    Google Scholar 

  5. Anapalli SS, Fisher DK, Reddy KN, Wagle P, Gowda PH, Sui R (2018) Quantifying soybean evapotranspiration using an eddy covariance approach. Agric Water Manag 209:228–239

    Article  Google Scholar 

  6. Anapalli SS, Fisher DK, Reddy KN, Krutz JL, Pinnamaneni SR, Sui R (2019) Quantifying water and CO2 fluxes and water use efficiencies across irrigated C3 and C4 crops in a humid climate. Sci Total Environ 63:338–350

    Article  CAS  Google Scholar 

  7. Anapalli SS, Fisher DK, Pinnamaneni SR, Reddy KN (2020) Quantifying evapotranspiration and crop coefficients for cotton (Gossypium hirsutum L.) using an eddy covariance approach. Agric Water Manag 223:228–239. https://doi.org/10.1016/j.agwat.2020.106091

    Article  Google Scholar 

  8. Anderson RG, Wang D (2014) Energy budget closure observed in paired eddy covariance towers with increased and continuous daily turbulence. Agric for Meteorol 184:204–209. https://doi.org/10.1016/j.agrformet.2013.09.012

    Article  Google Scholar 

  9. ASCE-EWRI (2005) The ASCE standardized reference evapotranspiration equation. In: Allen RG, Walter IA, Elliot RL, Howell TA, Itenfisu D, Jensen ME, Snyder RL (eds.) Standardization of reference evapotranspiration task committee final report, ASCE-EWRI, pp 1–11

  10. Ayars J (2008) Water requirement of irrigated garlic. Trans ASABE 51(5):1683–1688

    Article  Google Scholar 

  11. Baldocchi DD (2003) Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: the past, present, and future. Glob Chang Biol 9:479–492

    Article  Google Scholar 

  12. Burba, G., Anderson, D., 2005. Introduction to the eddy covariance method: General guidelines and conventional workflow. Li-Cor Biosciences.

  13. Clark BR, Hart RM (2009) The Mississippi Embayment Regional Aquifer Study (MERAS): Documentation of a groundwater-flow model constructed to assess water availability in the Mississippi Embayment. U.S. Geological Survey Scientific Investigations Report 2009–5172, 61 p.

  14. Dalin C, Wada Y, Kastner T, Puma MJ (2017) Groundwater depletion embedded in international food trade. Nat Lett 543:700–706

    CAS  Article  Google Scholar 

  15. De Roo F, Zhang S, Huq S, Mauder M (2018) A semi-empirical model of the energy balance closure in the surface layer. PLoS ONE 13(12):209022

    Google Scholar 

  16. Desclaux D, Roumet P (1996) Impact of drought stress on the phenology of two soybeans (Glycine max L. Merr) cultivars. Field Crop Res 46:61–70

    Article  Google Scholar 

  17. Doorenbos J, Pruit WO (1977) Crop water requirements. Irrigation and drainage paper No. 24 (revised). Rome, Italy: Food and Agricultural Organization of the United Nations (FAO)

  18. FAO (2017) Water for sustainable food and agriculture. A report produced for the G20 Presidency of Germany. FAO, Rome. ISBN 978-92-5-109977-3. http://www.fao.org/3/a-i7959e.pdf.

  19. FAOSTAT (2006) FAO data for agriculture: statistics database. http://faostat.fao.org/faostat/collections?versionZext&hasbulkZ0&subsetZagriculture

  20. Farahani HJ, Oweis TY, Izzi G (2008) Crop coefficient for drip-irrigated cotton in a Mediterranean environment. Irrig Sci 26:375–383

    Article  Google Scholar 

  21. Farg E, Arafat SM, Abd El-Wahed MS, El-Gindy AM (2012) Estimation of Evapotranspiration ETc and Crop Coefficient Kc of Wheat, in south Nile Delta of Egypt, using integrated FAO-56 approach and remote sensing data. Egypt J Remote Sens Sp Sci 15:83–89

    Google Scholar 

  22. Fehr WR, Caviness CE (1977) Stages of soybean development. Special Rep. 80, Iowa State University, Ames

  23. Foken T (2008) The energy balance closure problem—an overview. Ecol Appl 18:1351–1367

    PubMed  Article  PubMed Central  Google Scholar 

  24. Foken T, Wimmer F, Mauder M, Thomas C, Liebethal C (2006) Some aspects of the energy balance closure problem. Atmos Chem Phys Discuss 6:3381–3402

    Google Scholar 

  25. Fratini G, Matthias M (2014) Towards a consistent eddy-covariance processing: an intercomparison of EddyPro and TK3. Atmos Meas Tech 7(1):2273–2281

    Article  Google Scholar 

  26. Fratini G, Mauder M (2014) Towards a consistent eddy-covariance processing: an intercomparison of EddyPro and TK3. Atmos Meas Tech 7:2273–2281. https://doi.org/10.5194/amt-7-2273-2014

    Article  Google Scholar 

  27. Gao Z, Liu H, Katul GG, Foken T (2017) Non-closure of the surface energy balance explained by phase difference between vertical velocity and scalars of large atmospheric eddies. Environ Res Lett 12:34025

    Article  Google Scholar 

  28. Heatherly LG (2014) Irrigation water conservation for the Mississippi Delta, MSPB, Rv. Nov. 2014. http://www.mssoy.org/

  29. Hodges T, French V (1985) Soyphen: soybean growth stages modeled from temperature, water availability, and daylength. Agron J 77:500–505

    Article  Google Scholar 

  30. Howell TA, Evett SR, Tolk JA, Schneider AD (2004) Evapotranspiration of full, deficit-irrigated, and dryland cotton on the Northern Texas High Plains. J Irrig Drain Eng 130:277–285

    Article  Google Scholar 

  31. Howell TA, Evett SR, Tolk JA, Copeland KS, Dusek DA, Colaizzi PD (2006)Crop coefficients developed at Bushland, Texas for corn, wheat, sorghum, soybean, cotton, and alfalfa. In: Proceedings of the World Water and Environ-mental Resources Congress. Examining the Confluence of Environmental and Water Concerns, May 21–25, 2006

  32. Hunsacker DJ, Pinter PJ, Barnes EM, Kimball BA (2003) Estimating cotton evapotranspiration crop coefficients with a multispectral vegetation index. Irrig Sci 22:95–104

    Article  Google Scholar 

  33. Irmak S, Kabenge I, Skaggs K, Mutiibwa D (2012) Trend and magnitude of changes in climate variables and reference evapotranspiration over a 116-year period in the Platte River basin, central Nebraska, USA. J Hydrol 420–421:228–244

    Article  Google Scholar 

  34. Irmak S, Odhiambo LO, Specht JE, Djaman K (2013) Hourly and daily single and basal evapotranspiration crop coefficients as a function of growing degree days, days after emergence, leaf area index, fractional green canopy cover, and plant phenology for soybean. Trans ASABE 56(5):1785–1803. https://doi.org/10.13031/trans.56.10219

    Article  Google Scholar 

  35. Irmak S, Specht JE, Odhiambo LO, Rees JM, Cassman KG (2014) Soybean yield, water productivity, evapotranspiration, and soil–water extraction response to subsurface drip irrigation. Trans ASABE 57(3):729–748. https://doi.org/10.13031/trans.57.10085

    Article  Google Scholar 

  36. Isaac P, Cleverly J, McHugh I, van Gorsel E, Ewenz C, Beringer J (2017) OzFlux data: network integration from collection to curation. Biogeosciences 14(12):2903–2928. https://doi.org/10.5194/bg-14-2903-2017

    Article  Google Scholar 

  37. Jagtap SS, Jones JW (1989) Stability of crop coefficients under different climate and irrigation management practices. Irrig Sci 10:231–244

    Article  Google Scholar 

  38. Karam F, Lahoud R, Masaad R, Kabalan R, Breidi J, Chalita C, Rouphael Y (2007) Evapotranspiration, seed yield, and water use efficiency of drip-irrigated sunflower under full and deficit irrigation conditions. Agric Water Manag 90:213–223

    Article  Google Scholar 

  39. Kebede H, Fisher DK, Sui R, Reddy KN (2014) Irrigation methods and scheduling in the delta region of Mississippi: current status and strategies to improve irrigation efficiency. Am J Plant Sci 5:2917–2928. https://doi.org/10.4236/ajps.2014.520307

    Article  Google Scholar 

  40. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World Map of the Köppen-Geiger climate classification updated. Meteorol Z 15:259–263. https://doi.org/10.1127/0941-2948/2006/0130

    Article  Google Scholar 

  41. Liu X, Yang S, Xu J, Zhang J, Liu J (2017) Effects of heat storage and phase shift correction on energy balance closure of paddy fields. Atmosfera 30(1):39–52

    CAS  Article  Google Scholar 

  42. López-Urrea R, Montoro A, López-Fuster P, Fereres E (2009) Evapotranspiration and responses to irrigation of broccoli. Agric Water Manag 96(7):1155–1161

    Article  Google Scholar 

  43. Mauder M, Foken T (2006) Impact of post-field data processing on eddy covariance flux estimates and energy balance closure. Meteorol Z 15:597–609

    Article  Google Scholar 

  44. Mauder M, Oncley SP, Vogt R, Weidinger T, Ribeiro L, Bernhofer C, Foken T, Kohsiek W, de Bruin HAR, Liu H (2007) The energy balance experiment EBEX-2000. Part II: intercomparison of eddy-covariance sensors and post-field data processing methods. Bound-Lay Meteorol 123:29–54. https://doi.org/10.1007/s10546-006-9139-4

    Article  Google Scholar 

  45. McMaster GS, Wilhelm WW (1997) Growing degree-days: one equation, two interpretations. Agric for Meteorol 87(4):291–300

    Article  Google Scholar 

  46. Meyers TP, Hollinger SE (2004) An assessment of storage terms in the surface energy balance of maize and soybean. Agric for Meteorol 125:105–115

    Article  Google Scholar 

  47. Monteith JL (1965) Evaporation and environment. Symp Soc Exp Biol 19:205–234

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Morison JI, Baker NR, Mullineaux PM, Davies WJ (2008) Improving water use in crop production. Philos Trans R Soc B 363:639–658

    CAS  Article  Google Scholar 

  49. Parent AC, Anctil F (2012) Quantifying evapotranspiration of a rainfed potato crop in South-eastern Canada using eddy covariance techniques. Agric Water Manag 113:45–56

    Article  Google Scholar 

  50. Passioura JB (2002) Environmental plant biology and crop improvement. Funct Plant Biol 29:537–546

    PubMed  Article  PubMed Central  Google Scholar 

  51. Passioura JB (2004) Water-use efficiency in farmers’ fields. In: Bacon M (ed) Water-use efficiency in plant biology. Blackwell, Oxford, pp 302–321

    Google Scholar 

  52. Payero JO, Irmak S (2013) Daily energy fluxes, evapotranspiration, and crop coefficient of soybean. Agric Water Manag 129:31–43

    Article  Google Scholar 

  53. Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proc R Soc Lond 193:120–145

    CAS  Google Scholar 

  54. Powers S (2007) Agricultural water use in the Mississippi Delta. Delta groundwater. In: 37th Annual Mississippi water resources conference proceedings, p 47–51

  55. Reichstein M, Falge E, Baldocchi D, Papale D, Aubinet M, Berbigier P, Bernhofer C, Buchmann N, Gilmanov T, Granier A, Grünwald T, Havránková K, Ilvesniemi H, Janous D, Knohl A, Laurila T, Lohila A, Loustau D, Matteucci G, Meyers T, Miglietta F, Ourcival JM, Pumpanen J, Rambal S, Rotenberg E, Sanz M, Tenhunen J, Seufert G, Vaccari F, Vesala T, Yakir D, Valentini R (2005) On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Glob Change Biol 11(9):1424–1439. https://doi.org/10.1111/j.1365-2486.2005.001002.x

    Article  Google Scholar 

  56. Robinson S, Burian A, Couturier E, Landrein B, Louveaux M, Neumann ED, Peaucelle A, Weber A, Nakayama N (2013) Mechanical control of morphogenesis at the shoot apex. J Exp Bot 64:4729–4744

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  57. Rosenberg NJ, Blad BL, Verma SB (1983) Microclimate: the biological environment, 2nd edn. John Wiley & Sons, New York

    Google Scholar 

  58. Runkle BRK, Rigby JR, Reba ML, Anapalli SS, Bhattacharjee J, Krauss KW, Liang L (2017) Delta-Flux: an eddy covariance network for a climate-smart Lower Mississippi Basin. Agric Environ Lett 2:170003. https://doi.org/10.2134/ael2017.01.0003

    Article  Google Scholar 

  59. Sammis T, Mapel C, Lugg DG, Lansford RR, McGuckin JT (1985) Evapotranspiration crop coefficients predicted using growing-degree-days. Trans ASABE 28(3):7730780

    Google Scholar 

  60. Sánchez JM, Lopez-Urrea R, Doña C, Caselles V, Gonzalez-Piqueras, Niclos R (2015) Modeling evapotranspiration in a spring wheat from thermal radiometry: crop coefficients and E/T partitioning. Irrig Sci 33:399–410

    Article  Google Scholar 

  61. Scholberg J, McNeal BL, Jones JW, Boote KJ, Stanley CD, Obreza TA (2000) Growth and canopy characteristics of field-grown tomato. Agron J 92(1):152–159

    Google Scholar 

  62. Schulze ED, Hall EA (1982) Stomatal responses, water loss, and CO2 assimilation rates of plants in contrasting environments. In: Lange OL, Nobel PS, Osmond CB, Ziegler H (eds) Encyclopedia of plant physiology, physiological ecology II: water relations and carbon assimilation. Springer, Berlin, pp 181–230

    Google Scholar 

  63. Shiklomanov IA (2000) Appraisal and assessment of world water resources. Water Int 25(1):11–32

    Article  Google Scholar 

  64. Shurpali NJ, Biasi C, Jokinen S, Hyvönen N, Martikainen PJ (2013) Linking water vapor and CO2 exchange from a perennial bioenergy crop on a drained organic soil in eastern Finland. Agric for Meteorol 168:47–58

    Article  Google Scholar 

  65. Shuttleworth WJ, Wallace JS (1985) Evaporation from sparse crops—an energy combination theory. Quart J Roy Meteorol Soc 111:839–855

    Article  Google Scholar 

  66. Tallec T, Béziat P, Jarosz N, Rivalland V, Ceschia E (2013) Crop’s water use efficiencies in a temperate climate: comparison of stand, ecosystem, and agronomical approaches. Agr for Meteorol 168:69–81. https://doi.org/10.1016/j.agrformet.2012.07.008

    Article  Google Scholar 

  67. Turner NC (2004a) Sustainable production of crops and pastures under drought in a Mediterranean environment. Ann Appl Biol 144:139–147

    Article  Google Scholar 

  68. Turner NC (2004b) Agronomic options for improving rainfall use efficiency of crops in dryland farming systems. J Exp Biol 55(407):2413–2425. https://doi.org/10.1093/jxb/erh154

    CAS  Article  Google Scholar 

  69. Uddin J, Hancock NH, Smith RJ, Foley JP (2013) Measurement of evapotranspiration during sprinkler irrigation using a precision energy budget (Bowen ratio, eddy covariance) methodology. Agric Water Manag 116:89–100

    Article  Google Scholar 

  70. Way DA, Katul GG, Manzoni S, Vico G (2014) Increasing water use efficiency along the C3 to C4 evolutionary pathway: a stomatal optimization perspective. J Exp Bot 65(13):3683–3693. https://doi.org/10.1093/jxb/eru205

    Article  PubMed  PubMed Central  Google Scholar 

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Anapalli, S.S., Krutz, J.L., Pinnamaneni, S.R. et al. Eddy covariance quantification of soybean (Glycine max L.,) crop coefficients in a farmer’s field in a humid climate. Irrig Sci (2021). https://doi.org/10.1007/s00271-021-00742-2

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