|Hunsaker, Douglas - Doug|
|Pinter Jr, Paul|
Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/14/2005
Publication Date: 8/29/2005
Citation: Hunsaker, D.J., Barnes, E.M., Clarke, T.R., Fitzgerald, G.J., Pinter Jr, P.J. 2005. Cotton irrigation scheduling using remotely-sensed and FAO-56 basal crop coefficients. Transactions of the ASAE. 48(4):1395-1407. Interpretive Summary: To improve water use efficiency, reliable methods are needed to help cotton farmers decide when to irrigate and how much water to apply. Cotton irrigation needs are presently being determined with crop coefficient (Kc) curves and weather information, which provide estimates of how much water is being used each day for crop evapotranspiration (ET). However, these generic Kc curves based on a crop calendar can often be inaccurate, resulting in imprecise estimates for actual ET, which can lead to poor irrigation scheduling and inefficient water use. This research tested a new method which adjusts the Kc for each day during the season depending on how fast or slow the cotton canopy is growing. The approach relies on remote sensing measurements of the cotton canopy reflectance to 'sense' the actual Kc and ET. Results indicate that the remote sensing technique estimated the ET of cotton accurately and successfully guided proper irrigation scheduling. Further improvements of this technique will ultimately enable cotton growers to customize irrigation schedules for more efficient water use management, which will also be of interest to farm irrigation consultants, government agencies, and the irrigation industry.
Technical Abstract: Multispectral vegetation indices calculated from canopy reflectance measurements have been used to simulate real-time basal crop coefficients (Kcb), which have been validated to improve crop coefficient evapotranspiration (ETc) estimation for several crops. In this paper, an application of the approach was evaluated for cotton using remote sensing observations of the normalized difference vegetation index (NDVI) to estimate Kcb from relationships describing Kcb as a function of NDVI. The dual crop coefficient procedures of the Food and Agricultural Organization, Paper 56 (FAO-56), were used to calculate ETc and determine irrigation scheduling using Kcb estimates from remote sensing (NDVI treatment) as well as from time-based Kcb curves (FAO treatment), which were developed locally for standard crop conditions using FAO-56 procedures. Two cotton experiments conducted in 2002 and 2003 in central Arizona included sub-treatments of three levels of plant density (sparse, typical, and dense at 5, 10, and 20 plants per m-squared, respectively) and two levels of nitrogen management (high and low) to impose a wide range of crop development and water use. The NDVI-Kcb relationships used for 2002, developed from previous data for a different cotton cultivar, row-orientation, and soil type, substantially underestimated ETc, resulting in significantly less irrigation water applied and lower lint yields for NDVI compared to the FAO treatment. The 2002 data were used to recalibrate the NDVI-Kcb relationships, which then estimated cotton ETc with a mean absolute error of 9% for all treatment conditions in 2003 compared to 22% in 2002. The FAO Kcb curve used in 2002 described ETc and irrigation scheduling reasonably well for sparse plots, but consistently underestimated water use and soil water depletion for the higher plant densities during the first half of the season. An adjust FAO Kcb curve used in 2003 predicted ETc for the typical planting density with a mean absolute error of 10% compared to 15% in 2002. Final lint yield for 2003 were not significantly different between the two Kcb methods. Although additional research is needed to validate remote sensing Kcb estimation for other conditions than those in these experiments, the approach has the potential to further extend our present crop coefficient capabilities when weather, plant density, or other factors cause cotton canopy development and water use patterns to depart from typical conditions.