Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

Optimizing Irrigation Rates for Cotton Production in an Extremely Arid Area Using RZWQM2-Simulated Water Stress

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Transactions of the ASABE. 60(6): 2041-2052. (doi: 10.13031/trans.12365) @2017
Authors:   Che Liu, Zhiming Qi, Zhe Gu, Dongwei Gui, Fanjiang Zeng
Keywords:   Cotton production, Optimum irrigation, RZWQM2, Soil water content, Water stress, WS-based regime.

Abstract. Quantifying crop water demand and optimizing irrigation management practices are essential to water resource management in arid desert oases. Agricultural systems modeling can serve to develop a better understanding of the hydrologic cycle under various irrigation and climate conditions. RZWQM2-simulated water stress can be used as an indicator for irrigation scheduling but has not been applied to extremely arid zones. The objectives of this study were to (1) evaluate the performance of RZWQM2 in simulating soil moisture content and crop production in an extremely arid area and (2) develop an optimal irrigation strategy using model-simulated crop water stress. In this study, RZWQM2 hybridized with DSSAT was calibrated and validated against soil moisture, cottield, and development stage data collected from 2006 to 2013 in a flood-irrigated cotton field located in an extremely dry oasis in Cele, situated in Xinjiang, China (mean annual precipitation 37 mm). The simulated water balance was analyzed to determine the actual crop water consumption, crop water requirements, and seepage loss. Subsequently, an optimal irrigation scheme was developed using RZWQM2 by averting crop water stress from planting to 90% open boll. In comparison to similar studies, the accuracy of soil moisture content simulations was deemed acceptable based on percent bias (PBIAS < ±15%), coefficient of determination (0.378 ≤ R2 ≤ 0.636), Nash-Sutcliffe model efficiency (0.130 ≤ ME ≤ 0.557), and root mean squared error (0.022 m3 m&-3 ≤ RMSE ≤ 0.031 m3 m&-3). The model performed well in simulating cotton yield (R2 = 0.79, ME = 0.75, RMSE = 417.0 kg ha-1, and relative RMSE (rRMSE) = 12.5%). Model-simulated plant emergence dates were generally six days late because of the model‘s lack of a component for mulching after seeding. Other phenological dates were closely matched, with a mean difference of ±4 days. On average, over eight years, the simulated growing season (planting to 90% open boll) water balance showed that the cotton crop consumed 532 mm year-1 of water under current irrigation practices, while 109 mm of water was lost through deep seepage. However, based on simulated PET, the crop water requirement was 641 mm year-1, suggesting water stress under current irrigation practices. Under these conditions, water stress occurred mainly during the late stages of cotton growth. The model-simulated actual evapotranspiration (ET) is comparable to the calculated ET using the water balance method, with percent error of -1.3%, indicating the rationality of applying model-simulated results in a water stress-based irrigation scheduling method. On average, the water stress-minimizing RZWQM2 irrigation schedule resulted in an apparent irrigation water savings of 32 mm year-1 (4.9%) and an annual yield increase of 527 kg ha-1 (16.3%). RZWQM2 was shown to be suitable for simulating soil hydrology and crop development in an agricultural system implemented in an extremely dry climate. Rescheduling of irrigation using a water stress-based method can be used to optimize irrigation water use and cotton production.

(Download PDF)    (Export to EndNotes)