Water Resources

Modern Water Management Practices

Figure 1. Typical weather station used by producers to collect weather data for scheduling irrigation events.

Figure 2. Transmitter of a soil moisture sensor used to monitor soil moisture.

Since water is a limited resource, and due to economic constraints (costs of water, pumping and labor needed to apply irrigation), producers are very prudent in managing this resource. A number of approaches are used to decide when to irrigate, including:

• Computer models that predict water use based on the growth stage of the plant and weather data (Figure 1 shows a typical weather station);
• Soil moisture probes that determine if there is sufficient water present to meet crop needs (Figure 2 shows the transmitter for a wireless soil moisture sensor);
• Thermal infrared thermometers (IRTs) that measure the temperature of the cotton leaves – as the plant begins to run out of water, its leaf temperature will increase. Some companies are now offering thermal images so producers can see leaf temperatures across the entire farm.

Research to Continue to Improve Water Management for Cotton and Further Details on Cotton Water Management

Research continues to develop more accurate and easier ways to determine crop water needs. Figure 3 shows a cotton field outfitted to help researchers better understand how new instruments may be able to predict when cotton plants need water. The large pond of water in the picture serves as a reference point for research purposes.

Figure 3. Field instrumented in on-going research to find even better methods to determine when to apply water.

Improving water use necessitates creative and innovative technology in agricultural production systems. Numerous emerging technologies and management schemes have been developed. Research at the NESPAL Environmental Center at the University of Georgia has shown increased efficiency of precision water placement using a combination of center pivot irrigation, sensors to detect water needs, preset geographic maps and variable rate nozzles to vary water application in accordance with soil type and water holding capacity.¹ Adaptation was influenced by the declining ground water resources and the cost of pumping from increasingly deep wells. Additionally, in the past 25 years, low energy precision application (LEPA) using drop tubes in Texas has decreased water losses dramatically.

Installation of subsurface drip irrigation (SDI) has also increased and this is a desirable delivery method for supplemental crop irrigation because its installation below ground eliminates evaporation from the soil surface. In fact, studies have shown that cotton grown under SDI decreased daily crop evapo-transpiration by 75% and had the highest water use efficiency for lint production.² The potential for introduction of reusable water into these new age irrigation systems can increase the sustainability of SDI systems even further. The introduction of pulsed-flow waters from aquaculture is one method of achieving this goal.³ Wastewater effluent has also been utilized in SDI systems using both tapes with emitters⁴, and gravel trenches⁵. However, caution must be taken to monitor sodium and phosphorus levels, as well as salinity, sodicity, nutrients, trace elements, and microbial contamination⁶.

Salinization affects about 20-30 million ha of the world’s current 260 million ha of irrigated land and limits world food production⁷. No data is available for cotton land, specifically. However, cotton may have an advantage in this arena because it is more tolerant to high salt levels than other crops. For agriculture, salinity can be managed through drainage, leaching during the cool season and changes to more salt-tolerant crops¹⁵, such as cotton. Cultural practices such as more frequent irrigation, water source blending, land grading and timing of fertilization make salinity management easier.

An example of a measurement system that can directly reduce the use of water is the Biologically Identified Optimal Temperature Interactive Console (BIOTIC) developed by the scientists at the USDA-ARS. The system provides irrigation scheduling based upon measurements of canopy temperatures and the temperature optimum of a given crop species⁸. The threshold values to schedule an irrigation event are calculated from the thermal optimum for the plant and the amount of time that a given species can exceed a temperature threshold and adequately recover. In a three-year study of the BIOTIC for scheduling irrigation in cotton, it was determined that lint yields declined 343 kg/ha for each 1 hour that the temperature exceeded 28 C⁹. Information like this can help optimize productivity in relation to water use.

Agriculture biotech companies are currently developing drought tolerant crops that should be launched toward the end of this decade or early next decade. An example of an early success in this research is Monsanto’s first-generation drought-tolerant corn. Experiments during the past two growing seasons show that these drought-tolerant corns yield between 7 to 23 percent more that normal corn hybrid varieties when water stressed (Monsanto 2006 Annual Report). Drought-tolerant cottons are also being developed and are being grown in Mississippi. These crops will be extremely important because they will dramatically increase the stability of production in drought conditions.

New sensing techniques and pivots with the ability to control individual sections are leading to incredibly precise water management in cotton. Through these efforts we are working to insure that water resources will be available to sustain irrigated cotton production for the long-term.

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References:

• ¹ http://nespal.cpes.peachnet.edu/PrecAg/vri.asp
• ² Bhattarai, S.P., McHugh, A.D., Lotz, G., and Midmore, D.J. 2006. The response of cotton to subsurface drip and furrow irrigation in a vertisol. Experimental Agriculture. 42:29-49.
• ³ Sherif, S.M., Fox, R.W., and Maughan, O.E. 2002. Economic feasibility of introducing pulsed-flow aquaculture into the irrigation system of cotton farms in Arizona. Aquaculture Economics and Management. 6:349-361.
• ⁴ Oron, G., DeMalach, J., Hoffman, Z., and Cibotaru., R. 1991. Subsurface microirrigation with effluent. Journal of Irrigation and Drainage Engineering. 117:25-36.
• ⁵ Ben-Gal., A, Lazorovitch, N., and Shani, U. 2004. Subsurface drop irrigation in gravel-filled cavities. Vadose Zone Journal. Published online at: http://vzj.scijournals.org/.
• ⁶ http://www.fao.org/DOCREP/003/T0234E/T0234E09.htm
• ⁷ http://hopmans.lawr.ucdavis.edu/3_irrigation_water_management.htm
• ⁸ Mahan, J.R., Burke, J.J., Wanjura, D.F., and Upchurch, D.R. 2005. Determination of temperature and time thresholds for BIOTIC irrigation of peanut on the Southern High Plains of Texas. Irrigation Science. 23:145-152.
• ⁹ Wanjura, D.F., Upchurch, D.R., and Mahan, J.R. 2006. Behavior of temperature-based water stress indicators in BIOTIC-controlled irrigation. Irrigation Science. 24:223-232.

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