Advertisement

Water Resources Management

, Volume 32, Issue 9, pp 2969–2983 | Cite as

Pond and Irrigation Model (PIM): a Tool for Simultaneously Evaluating Pond Water Availability and Crop Irrigation Demand

  • Ying Ouyang
  • Gary Feng
  • Theodor D. Leininger
  • John Read
  • Johnie N. Jenkins
Article
  • 166 Downloads

Abstract

Agricultural ponds are an important alternative source of water for crop irrigation to conserve surface and ground water resources. In recent years more such ponds have been constructed in Mississippi and around the world. There is currently, however, a lack of a tool to simultaneously estimate crop irrigation demand and pond water availability. In this study, a Pond-Irrigation Model (PIM) was developed to meet this need using STELLA (Structural Thinking, Experiential Learning Laboratory with Animation) software. PIM simulated crop land and agricultural pond hydrological processes such as surface runoff, soil drainage, and evapotranspiration as well as crop irrigation demand and pond water availability. More importantly, PIM was able to decide when to conduct crop irrigation based on management allowable depletion (MAD) root zone soil water content and to determine optimal ratios of agricultural pond size to crop land with sufficient pond water available for crop irrigation. As a case demonstration, the model was applied to concomitantly estimate row crops (i.e., corn, cotton, and soybeans) water irrigation demand and pond water availability in a farm located at East-central Mississippi. Simulations revealed that corn used more soil water for growth than soybeans, whereas soybeans needed more irrigation water than corn and occurred due to less rainwater available for soybeans growth. We also found that there was one time for corn, zero time for cotton, and two times for soybeans when the pond water level was drawn to near zero for irrigation from 2005 to 2014. PIM developed in this study is a useful tool for estimating crop irrigation demand and pond water availability simultaneously.

Keywords

Crop irrigation demand Mississippi Pond-irrigation model Pond water availability 

Notes

Acknowledgements

This project (62-2015) was funded by Mississippi Soybean Promotion Board.

References

  1. Abtew W (1996) Evapotranspiration measurements and modeling for three wetland systems in South Florida. J Am Water Resour Assoc 32:465–473CrossRefGoogle Scholar
  2. Abtew W (2005) Evapotranspiration in the Everglades: Comparison of Bowen Ratio measurements and model estimations. 2005 ASAE Annual International Meeting, Tampa, Florida. Paper Number: 052188Google Scholar
  3. Ahuja LR, Ma L, Lascano RJ, editors (2016) Practical applications of agricultural system models to optimize the use of limited water: ASA, CSSA, SSSA, Madison, WIGoogle Scholar
  4. Carvajal F, Agüera F, Sánchez-Hermosilla J (2014) Water balance in artificial on-farm agricultural water reservoirs forthe irrigation of intensive greenhouse crops. Agric Water Manag 131:146–155CrossRefGoogle Scholar
  5. Chow VT (1959) Open-Channel Hydraulics. McGraw-Hill, New YorkGoogle Scholar
  6. Feng G, Ouyang Y, Adeli A, Read J, Jenkins J (2017) Rainfall deficit and irrigation demand for major row crops in the Blackland Prairie of Mississippi. Soil Sci Soc Am J.  https://doi.org/10.2136/sssaj2017.06.0190
  7. 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 18:235–265CrossRefGoogle Scholar
  8. Kebede H, Fisher DK, Sui RX, 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 5:2917–2928CrossRefGoogle Scholar
  9. Konikow LF (2013) Groundwater depletion in the United States (1900–2008): U.S. Geological Survey Scientific Investigations Report 2013–5079, 63 p., http://pubs.usgs.gov/sir/2013/5079
  10. MSU Extension Service (2014) Mississippi Agricultural Statistics Service, Jackson, MS. http://www.dafvm.msstate.edu/factbook.pdf
  11. Mullins JA, Carsel RF, Scarbrough JE, Ivery AM (1993) PRZM-2, a model for predicting pesticides fate in the crop root and unsaturated soil zones: User manual for release 2.0, US-EPA, Athens, GAGoogle Scholar
  12. Ouyang Y, Feng G, Read J, Leininger TD, Jenkins JN (2016) Estimating the ratio of pond size to irrigated soybean land in Mississippi: A case study. Water Sci Technol Water Supply 16:1639–1647CrossRefGoogle Scholar
  13. Ouyang Y, Paz JO, Feng G, Read J, Adeli A, Jenkins JN (2017) A model to estimate hydrological processes and water budget from an irrigation farm pond in Mississippi. Water Resour Manag 31:2225–2241CrossRefGoogle Scholar
  14. Ozdogan M, Rodell M, Beaudoing HK, Toll DL (2010) Simulating the effects of irrigation over the United States in a land surface model based on satellite-derived agricultural data. J Hydrometeorol 11:171–184CrossRefGoogle Scholar
  15. Powers S (2007) Agricultural Water Use in the Mississippi Delta. 37th Annual Mississippi Water Resources Conference, Jackson, MississippiGoogle Scholar
  16. Rawls WJ, Ahuja LR, Brakensiek DL, Shirmohammadi A (1992) Infiltration and soil water movement. Chapter. 5. In: Maidment DR (ed) Handbook of Hydrology. McGraw-Hill, Inc, New York, pp 5.1–5.51Google Scholar
  17. Saxton KE, Stockle CO, Bluhm GC (1992) Soil water and nitrate budgets with the enhanced SPAW model. Proc. Amer. Water Res. Assoc., Nov.1–5, 1992, Reno, NV, pp. 269–270Google Scholar
  18. Vories ED, Evett SR (2014) Irrigation challenges in the sub-humid US Mid-South. Int J Water 8:259–274CrossRefGoogle Scholar
  19. YMD (Yazoo Mississippi Delta Joint Water Management District) 2015 Groundwater monitoring, a primary YMD activity for 25 years. (http://myemail.constantcontact.com/ YMD-Joint-Water-Management-District-May-21--2015.html?soid=1120967545567 &aid= AuOD6KeG9Sc. E-Newsletters, May 12, 2015

Copyright information

© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2018

Authors and Affiliations

  • Ying Ouyang
    • 1
  • Gary Feng
    • 2
  • Theodor D. Leininger
    • 3
  • John Read
    • 2
  • Johnie N. Jenkins
    • 2
  1. 1.USDA Forest ServiceCenter for Bottomland Hardwoods ResearchMississippi StateUSA
  2. 2.USDA-ARSGenetic and Sustainable Agricultural Research UnitMississippi StateUSA
  3. 3.USDA Forest ServiceCenter for Bottomland Hardwoods ResearchStonevilleUSA

Personalised recommendations