Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-22T22:56:54.007Z Has data issue: false hasContentIssue false
  • English
  • Français

A sod-based cropping system for irrigation reductions

Published online by Cambridge University Press:  20 October 2015

Daniel Dourte ,
R.L. Bartel ,
S. George ,
J.J. Marois  and
D.L Wright
Daniel Dourte
Affiliation:
Department of Agricultural and Biological Engineering, University of Florida, 1741 Museum Road, Gainesville, FL, USA.
R.L. Bartel
Affiliation:
University of Florida-Institute of Food and Agricultural Sciences — North Florida Research and Education Center, 155 Research Road, Quincy, FL, USA.
S. George*
Affiliation:
University of Florida-Institute of Food and Agricultural Sciences — North Florida Research and Education Center, 155 Research Road, Quincy, FL, USA.
J.J. Marois
Affiliation:
University of Florida-Institute of Food and Agricultural Sciences — North Florida Research and Education Center, 155 Research Road, Quincy, FL, USA.
D.L Wright
Affiliation:
University of Florida-Institute of Food and Agricultural Sciences — North Florida Research and Education Center, 155 Research Road, Quincy, FL, USA.
*
*Corresponding author: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Cotton and peanut grown under irrigation make up over 769,000 ha in the Southeast USA. The consumptive use of water for irrigation has significantly impacted groundwater resources, spring flows and streamflows in many parts of this region, particularly during severe droughts. This situation is further complicated with extreme weather events and climate variability. In this study, we compare yields and water use in a non-irrigated sod-based rotation system (SBR; bahiagrass–bahiagrass–peanut–cotton) to an irrigated conventional rotation system (ICR; peanut–cotton–cotton). Root mass of oat cover crop following peanut or cotton in a SBR and ICR system was also measured. A soil water assessment model (SWAT) was used to simulate irrigation water demands over a 34 yr period (1980–2013) under different soil types to quantify water saving potential of SBR. The average peanut yield in ICR from 2002 to 2013 was 4509 kg ha−1, while that in SBR was 4874 kg ha−1. Likewise the average cotton yield in ICR during the same period was 1237 kg ha−1, while that in SBR was 1339 kg ha−1. Oats had greater root mass in SBR than ICR. Simulation results indicate that crops in SBR consistently had substantially lower irrigation requirements (between 11 and 22 cm yr−1) than those in ICR in dry years. The water-saving potential of SBR varies positively with increasing sand content in soil.

Keywords

crop rotationcrop water demandirrigationperennial grassesrooting depth
Type
Research Papers
Information
Renewable Agriculture and Food Systems , Volume 31 , Issue 6 , December 2016 , pp. 485 - 494
Check for updates
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1 FAO. 2007. Water at a glance, Food and Agriculture Organization of the United Nations. Available at Web site http://www.fao.org/nr/water/docs/waterataglance.pdf (verified 8/1/2014).Google Scholar
2 Schiermeier, Q. 2014. Water on tap. Nature 510:326–328.Google Scholar
3 Walthall, C. L., Hatfield, J., Lengnick, L., Marshall, E., Backlund, P., and Walsh, M. 2012. Climate Change and Agriculture in the United States: Effects and Adaptation. USDA Technical Bulletin 1935. Washington, DC. p. 186. http://www.usda.gov/oce/climate_change/effects.htm Google Scholar
4 Hatfield, J., Takle, G., Grotjahn, R., Holden, P., Izaurralde, R.C., Mader, T., Marshall, E., and Liverman, D. 2014. Ch. 6: Agriculture. In Melillo, J.M., Richmond, T.C., and Yohe, G.W. (eds). Climate Change Impacts in the United States: The Third National Climate Assessment. U.S. Global Change Research Program. Cambridge University Press, New York. p. 150–174. doi: 10.7930/J02Z13FR Google Scholar
5 Wright, D.L., Marois, J.J., Katsvairo, T.W., Wiatrak, P.J., and Rich, J.R. 2004. Value of perennial grasses in conservation cropping systems. In Proceedings of the 26th Southern Conservation Tillage Conference of Sustainable Agriculture, Raleigh, NC. p. 135–142.Google Scholar
6 Wright, D.L., Marois, J.J., Anguelov, G., and Mackowiak, C.M. 2010. Enhanced crop, soil, economic, and environmental benefits with sod-based rotations. In ASA-CSSA-SSSA International Annual Meetings, Long Beach, CA.Google Scholar
7 Elkins, C.B. 1985. Plant roots as tillage tools. In Tillage machinery systems as related to cropping systems. In Proceedings of International Conference on Soil Dynamics. 17–19 June, Auburn University, Auburn, AL. p. 519–523.Google Scholar
8 Elkins, C.B., Haaland, R.L., and Hoveland, C.S. 1977. Grass roots as a tool for penetrating soil hardpans and increasing crop yields. In Proceedings of the 34th Southern Pasture and Forage Crop Improvement Conference. Auburn University, Auburn, AL. USDA-ARS, New Orleans, LA. p. 21–26.Google Scholar
9 Reeves, D. 1997. The role of soil organic matter in maintaining soil quality in continuous cropping systems. Soil and Tillage Research 43:131–167.Google Scholar
10 Katsvairo, T.W., Wright, D.L., Marois, J.J., Hartzog, D.L., Rich, J.R., and Wiatrak, P.J. 2006. Sod-livestock integration into the peanut-cotton rotation: A systems farming approach. Agronomy Journal 98:1156–1171.Google Scholar
11 Dickson, D. and Hewlett, T.F. 1989. Effects of bahiagrass and nematicides on Meloidogyne arenaria on peanut. Supplementary Journal of Nematology 21(4S):671–676.Google Scholar
12 Brenneman, T.B., Summer, D.R., Baird, R.E., Burton, G.W., and Minton, N.A. 1995. Suppression of foliar and soil borne peanut diseases in bahiagrass rotations. Phytopathology 85:948–952.Google Scholar
13 Tsigbey, F.K., Rich, J.R., Marois, J.J., and Wright, D.L. 2009. Effect of bahiagrass on nematode populations in the field and their behavior in greenhouse and laboratory conditions. Nematropica 39:111–119.Google Scholar
14 Katsvairo, T., Wright, D.L., Marois, J.J., Hartzog, D.L., Balkcom, K.B., Wiatrak, P.J., and Rich, J.R. 2007. Cotton roots, earthworms, and infiltration characteristics in sod-peanut-cotton cropping systems. Agronomy Journal 99:390–398.Google Scholar
15 Zhao, D., Wright, D.L., Marois, J.J., Mackowiak, C.M., and Katsvairo, T. 2008. Yield and water-use efficiency of cotton and peanut in conventional and sod-based cropping systems. In Proceedings of the Southern Conservation Agricultural System Conference. Tifton, GA. p. 53–57.Google Scholar
16 Anguelov, G., Wright, D.L., and Marois, J.J. 2010. Soil-solution N under conservation tillage: A tension lysimeter (ceramic cup) study on conventional and sod-based crop rotations. In ASA–CSSA–SSSA Annual Meetings. Long Beach, CA.Google Scholar
17 George, S., Marois, J.J., and Wright, D.L. 2011. Soil microbial and biochemical properties in sod-based and conventional peanut–cotton rotations. In ASA, CSSA, SSSA International Annual Meetings, San Antonio, TX.Google Scholar
18 Marois, J.J. and Wright, D.L. 2003. Effect of tillage system, phorate, and cultivar on tomato spotted wilt of peanut. Agronomy Journal 95:386–389.Google Scholar
19 Franzluebbers, A.J. 2005. Soil organic carbon sequestration and agricultural greenhouse gas emissions in the southeastern USA. Soil and Tillage Research 83:120–147.CrossRefGoogle Scholar
20 Kumar, S. and Lal, R. 2011. Mapping the organic carbon stocks of surface soils using local spatial interpolation. Journal of Environmental Monitoring 13:3128–3135.Google Scholar
21 Torak, L.J. and Painter, J.A. 2006. Geohydrology of the Lower Apalachicola–Chattahoochee–Flint River Basin, Southwestern Georgia, Northwestern Florida, and Southeastern Alabama. U.S. Geological Survey Scientific Investigations Report 2006–5070.Google Scholar
22 United States Department of Agriculture, National Agricultural Statistical Service. 2009. 2007 Census of Agriculture: United States Summary and State Data, Volume 1, Geographic Area Series, Part 51, Washington, DC.Google Scholar
23 Dourte, D.R., Fraisse, C.W., and Uryasev, O. 2014. WaterFootprint on AgroClimate: A dynamic, web-based tool for comparing agricultural systems. Agricultural Systems 125:33–41.Google Scholar
24 Menne, M.J., Durre, I., Vose, R.S., Gleason, B.E., and Houston, T.G. 2012. An overview of the global historical climatology network-daily database. Journal of Atmospheric and Oceanic Technology 29:897–910. doi: 10.1175/JTECH-D-11-00103.1.Google Scholar
25 Williams, J.R., Jones, C.A., Kiniry, J.R., and Spanel, D.A. 1989. The EPIC crop growth model. Transactions of the American Society of Agricultural and Biological Engineers 32:497–511.Google Scholar
26 Arnold, J.G., Srinivasan, R., Muttiah, R.S., and Williams, J.R. 1998. Large-area hydrologic modeling and assessment: Part I. Model development. Journal of the American Water Resources Association 34(1):73–89.Google Scholar
27 Nelson, G.C., Rosegrant, M.W., Koo, J., Robertson, R., Sulser, T., Zhu, T., and Ringler, C. 2009. Climate Change: Impact on Agriculture and Costs of Adaptation. International Food Policy Research Institute, Washington, DC.Google Scholar
28 Vadez, V., Rao, J.S., Bhatnagar-Mathur, P., and Sharma, K.K. 2013. DREB1A promotes root development in deep soil layers and increases water extraction under water stress in groundnut. Plant Biology 15:45–52.Google Scholar
29 Reddy, T.Y., Reddy, V.R. and Anbumozhi, V. 2003. Physiological responses of groundnut (Arachis hypogea) to drought stress and its amelioration: A critical review. Plant Growth Regulation 41:75–88.CrossRefGoogle Scholar
30 McMichael, B.L., Oosterhuis, D.M., and Zak, J.C. 2011. Stress response in cotton root systems. In Oosterhuis, D.M. (ed.). Stress Physiology in Cotton, Vol. 7 of the Cotton Foundation Book Cordova, TN. p. 97–112.Google Scholar
31 Hann, C.T. 1977. Statistical Methods in Hydrology, Probability Plotting and Frequency Analysis. Iowa State University Press, Ames, IA. Chapter 7. pp. 128–158.Google Scholar
32 Minitab 17 Statistical Software. 2010. State College, PA: Minitab, Inc. (www.minitab.com).Google Scholar
33 IPCC. 2007a. Summary for policy makers. In Solomon, S. et al. (ed.). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.Google Scholar
34 Zhang, X., Zwiers, F.W., Hegerl, G.C., Lambert, F.H., Gillett, N.P., Solomon, S., Stott, P.A., and Nozawa, T. 2007. Detection of human influence on twentieth century precipitation trends. Nature 448(7152):461–465.Google Scholar
35 Seager, R., Tzanova, A., and Nakamura, J. 2009. Drought in the southeastern United States: Causes, variability over the last millennium, and the potential for future hydroclimate change. Journal of Climate 22(19):5021–5045.Google Scholar
36 Perry, C. and Yager, R. 2011. Irrigation water conservation efforts at the C.M. Stripling Irrigation Park. In Proceedings of the 2011 Georgia Water Resources Conference. Held April 11–13, 2011 at the University of Georgia. Available at Web site http://www.gwri.gatech.edu/sites/default/files/files/docs/2011/7.5.2Perry.pdf Google Scholar