Plant Response to Irrigation Treatments in Arkansas Cotton

Transcription

1 Plant Response to Irrigation Treatments in Arkansas Cotton Sreekala G. Bajwa and Earl D. Vories 1 INTRODUCTION Irrigation of cotton has been increasing throughout the mid-south. In Arkansas, 65% of the 1,065,000 total harvested acres in 2001 were irrigated (Anonymous, 2003). Although many mid-south producers question whether they receive a yield increase in some years over their non-irrigated crop, lending agencies often require irrigation to protect their investment before making crop loans. In the mid-south, however, knowing what the optimal irrigation strategy is, with climate that varies throughout the season and from year to year, is not simple. Studies in Arkansas have shown an inconsistent yield response to irrigation (Teague et al., 1999; Vories et al., 2000). Such varying results suggest that some crop-response-based indicator is needed. A new direction in irrigation management and scheduling is through early detection of water stress by monitoring the reflectance and/or emittance of light by the crop canopy. Some near infrared and temperature bands in the electromagnetic spectrum are very responsive to changes in canopy water content. Therefore, plant-response-based site-specific irrigation management could optimize water use on an as-needed basis and likely could incorporate seasonto-season or within-season variability in crop development. Canopy temperature and reflectance are useful measures to estimate crop water status (De-Lorenzi et al., 1993). Canopy transpiration and field evaporation were found to be directly related to remote sensing-based vegetative indices (Inoue and Moran, 1997). A crop-water stress index (CWSI) based on canopy temperature has been proven effective for early detection of crop-water stress (Kacira et al., 2002), but has never been adopted in the mid-south. Various remotely-sensed data have been found valuable for identifying irrigation timing. Remote sensing can also be integrated into crop models for improved accuracy in irrigation scheduling (Barnes et al., 2000). The project goal was to improve irrigation scheduling methods for mid-south cotton through refinement of a weather-based crop-water use model and evaluation of 1 Assistant professor, Department of Biological and Agricultural Engineering, Fayetteville; and agricultural engineer, USDA-ARS Cropping Systems and Water Quality Research Unit, Portageville, Mo., respectively. 126

2 Summaries of Arkansas Cotton Research 2004 possible crop-response-based indicators for water status. The specific objectives were to: (1) validate light reflectance/emittance from cotton canopy as an early indicator of water stress, and (2) develop and validate an irrigation strategy on an as-needed basis by incorporating season-to-season or within-season variability in crop development as indicated by canopy reflectance/emittance. RESEARCH DESCRIPTION A furrow-irrigated cotton field at the University of Arkansas Northeast Research and Extension Center in Keiser was used for conducting field experiments in Cotton was managed based on University of Arkansas recommendations. The study was designed as a randomized complete block split plot design with four replications. The whole-plot factor was irrigation and the split plot factor was cultivar. There were three irrigation treatments, based on the Arkansas Irrigation Scheduler, with WW irrigated at a 1.75-in. allowable deficit, MS irrigated at a 3.5-in. allowable deficit, and SS receiving no irrigation. The two cultivars used as a subplot factor were FM 960RR and FM 819RR. FM 960 was a standard mid-south cultivar, and FM 819 was an okra-leaf cultivar of similar maturity. The subplots (cultivar) were strip plots with 5.8 m (six 38- in. rows or 19 ft) width and approximately 244 m (800 ft) length, with 4 buffer rows (3.9 m) between each set of irrigation treatments. The fields were planted on 6 May and harvested on 16 October with a spindle picker. Subsamples were collected by hand harvest and ginned for lint content. Prior to planting, soil grid samples were collected from the top 15 cm (6 in.) soil at 12 m (40 ft) grid to account for any variability in soil fertility. The year 2004 was a relatively wet crop season, preventing development of significant water stress in the crop. Soil moisture tension was measured manually at both shallow (8-in. depth) and deep (16-in. depth) between 9:00 and 10:00 a.m. on July, 4, 9, 17, and 19 Aug, and 3 September with Watermark sensors (Irrometer, Riverside, Calif.). Infrared thermometers (Exergen Corporation, Watertown, Mass.) were installed in the plots and connected to data loggers for continuous monitoring of canopy temperature from 5 August to 7 September. Canopy temperature was recorded every 5 min, and averaged over each hour to obtain hourly data. Crop canopy reflectance was measured with an MSR16R spectro-radiometer (CropScan Inc., Rochester, Minn.). The spectro-radiometer was configured to have 8 narrow wavebands at 460, 510, 560, 610, 660, 710, 760, and 810 nm, respectively, in the color-infrared region. Initial configuration and calibration of the spectro-radiometer were performed on 28 April. Canopy reflectance was measured on 18 August. Canopy reflectance was calculated by measuring radiance of light reflected from cotton canopy and converting it into canopy reflectance with reference to the reflected radiance of a spectralon reference panel. Two sets of color infrared (CIR) images were acquired on 5 July and 19 August with a Duncan Tech camera (Redlake, San Diego, Calif.) at 2-m ground spatial resolution. Both spectral data and CIR images were used to calculate normalized difference vegetative index (NDVI = [NIR-R]/[NIR+R]) and green NDVI (GNDVI = [NIR- G]/[NIR+G]), where NIR stands for near infrared (NIR) reflectance, R stands for red reflectance and G stands for green reflectance. Three different NDVIs were calculated 127

3 AAES Research Series 533 by using three specific red wavelengths of 710 nm (NDVI-1), 660 nm (NDVI-2), and 610 nm (NDVI-3). Analysis of variance (ANOVA) was conducted on all three NDVIs, GNDVI, cotton yield, soil moisture tension, and canopy temperature with respect to the treatment variables. All data were analyzed using the Statistical Analysis System (SAS Inc., Cary, N.C.). F-tests were considered significant at the 0.05 level of probability. The Fisher s Least Significant Difference test was used for means separation. 128 RESULTS AND DISCUSSION Overall, the 2004 crop season did not have many dry days for developing significant water stress in cotton. Field data were collected mostly when researchers had a few days of dry conditions. The standard FM960 cultivar produced significantly higher seedcotton yield compared to the okra leaf cultivar, FM819 (Table 1, Fig. 1). The moderately stressed (MS) and well-watered (WW) treatments were significantly different from severely stressed (SS) treatment. The SS treatment resulted in higher yields that were 200 to 450 kg/ha more than the MS and WW treatments, demonstrating that excess irrigation can lead to reduced yields. Therefore, an accurate irrigation-scheduling program is necessary for maximizing cotton yield. ANOVA on yield with respect to the treatment variables resulted in a model R 2 of 0.96 with p-value less than 5%. Both irrigation treatment and cultivar were highly significant (p-value < 5%) variables in determining seedcotton yield. On 17 August, soil moisture tension showed significant difference between MS and WW treatment at 8-in. depth, but there was no difference between irrigation treatments at 8-in. depth on 19 August (Table 2). On both days, the SS treatment showed significantly higher soil-moisture tension (0.81 and 0.84 bar respectively) than the MS and WW treatments ( bars) at 40-cm depth. Since the root system of cotton crop is expected to be well established by the middle of August, a higher soil-moisture tension at shallow depth (20 cm) may not have impacted the crop significantly. However, the significantly lower soil-moisture tension at 16 in. for WW and MS treatment was reflected in the yield, as these two treatments resulted in lower yields. Average canopy temperature between 9:00 and 10:00 a.m. showed a significant difference between the SS and WW treatments on 17 August (Table 2). Relative humidity is an important factor that affects the expression of canopy temperature by waterstressed plants. High relative humidity can mask differences in canopy temperature with respect to ambient temperature. The relative humidity on 17 August was low to moderate, varying between 61 and 75% between 9:00 and 10:00 a.m. and 56% at noon. On 19 August, the relative humidity was comparable to 17 August with a value of 66 to 73% between 9:00 and 10:00 a.m. and 50% at noon. However, there was no significant difference between canopy temperatures of different irrigation treatments on 19 August. Noontime canopy temperature was also significantly different for both SS and WW treatments on 17 and 19 August. The daily average (6:00 a.m. to 6:00 p.m.) did not show any significant difference between irrigation treatments. Canopy temperature was not significantly different between the two cultivars at P = The vegetative indices, NDVI and GNDVI, computed from canopy reflectance data in 2004 exhibited significantly different (p < 0.05) values for the two cultivars,

4 Summaries of Arkansas Cotton Research 2004 FM 960RR and FM 819RR (Table 1, Fig. 1). Two (NDVI-2 and NDVI-3) out of the three different NDVIs calculated by combining 810 nm with three different red bands of 710 (NDVI-1), 660 (NDVI-2), and 610 nm (NDVI-3), and GNDVI calculated with 810 and 560 nm showed significantly different values for SS and WW treatments at the 5% significance level (Table 1). However, NDVI-1(810,710nm) showed no significant difference between the three irrigation treatments. GNDVI showed that SS treatment was significantly different from both MS and WW treatments. The difference in the behavior of the three NDVIs calculated with three different red bands can be attributed to the difference in response of each red band to excess water. Since red bands are primarily pigment absorption bands, their different responses to excess water should be further investigated. The GNDVI appeared to have most accurately captured the trend observed in yield response to irrigation treatments. Seedcotton yield showed a linear relationship with vegetative indices, when data for the two cultivars were combined (Fig. 2). However, the R 2 values were relatively low with 0.45 for NDVI-1 and 0.23 for GNDVI. When the various vegetative indices were modeled with proc GLM on irrigation treatments, cultivar, and block, cultivar resulted as the most significant variable at p-values less than 5%. Irrigation treatment was significantly related to NDVI-2 (810, 660) and NDVI-3 (810, 610) at 10% level. Both irrigation treatment and cultivar were significant in an ANOVA of GNDVI on treatments (p-value < 5%). The results from CropScan data can be visibly validated by the CIR image data as shown in Figure 2. The NDVI calculated from a color-infrared image acquired with a DuncanTech camera appeared to show differences between adjacent plots that can be visually validated. PRACTICAL APPLICATION Seedcotton yield was significantly affected by the treatments, with excess water causing a decrease in yield. The SS treatment showed significantly higher soil-moisture tension at the 16-in. depth compared to MS and WW treatments. The soil-moisture tension at the 8-in. depth did not show a consistent pattern over the period of data collection, primarily due to an excessively wet season in Both crop reflectance and canopy temperature showed potential to indicate water stress in cotton. Vegetative indices, NDVIs and GNDVI, indicated significant differences between water treatment and cultivar, even though water treatment was not significant at all wavelengths. Canopy temperature showed significant differences between the SS and WW plots even when the treatments did not result in A significant amount of water stress. Yield showed a linear trend with both NDVI and GNDVI. These results hold good only for the narrow range of water stress that existed in the field. The study showed that crop response (canopy temperature, canopy reflectance) has the potential to be an effective indicator of water stress to develop a modified irrigation scheduling strategy. Such a strategy could have the potential to incorporate real-time monitoring and irrigation scheduling. 129

5 AAES Research Series 533 LITERATURE CITED Anonymous Arkansas county estimates Arkansas Agricultural Statistics Service. Available at: Accessed on 4 June, Barnes, C.M., P.J. Pinter, B.A. Kimball, D.J. Hunsaker, G.W. Wall, and R.L. LaMorte Precision irrigation management using and modeling and remote sensing approaches. Proc. 4 th Decennial National Irrigation Symposium, Phoenix, Ariz., pp De-Lorenzi, F., C. Stanghellini, and A. Pitacco Water shortage sensing through infrared canopy temperature: timely detection is imperative. Acta-horticulture, International Society for Horticultural Science, Wageningen 335: Inoue,Y. and M.S. Moran A simplified method for remote sensing of daily canopy transpiration-a case study with direct measurements of canopy transpiration in soybean canopies. International Journal of Remote Sensing 18: Kacira, M., P.P. Ling, and T.H. Short Establishing crop water stress index (CWSI) threshold values for early, non-contact detection of plant water stress. Transactions of ASAE 45(3): Vories, E.D. and R.E. Glover Effect of irrigation timing on cotton yield and earliness. Proc. Beltwide Cotton Prod. Res. Conf., National Cotton Council, Memphis, Tenn. pp

6 Summaries of Arkansas Cotton Research 2004 Fig. 1. Lint yield plotted against vegetative indices NDVI (810,710 nm) and GNDVI (810,560 nm) for the two cultivars FM819 (okra leaf) and FM960 (standard). The plot shows significant differences in yield and NDVI between the two cultivars. Fig. 2. An NDVI image derived from the color-infrared image of the experiment field, acquired on July 17. The NDVI image appears to demarcate both irrigation treatments and cultivars. 131

7 AAES Research Series 533 Table 1. Summary of field data including yield and vegetative indices obtained from CropScan data for the irrigation study field in Keiser, Ark., in The two cultivars used are FM819, an okra-leaf cultivar, and FM960, a standard cultivar. Irrigation Seedcotton yield NDVI1 z NDVI2 y NDVI3 x GNDVI w treatment FM819 FM960 FM819 FM960 FM819 FM960 FM819 FM960 FM819 FM (kg/ha) SS v 3451 a u a a a a 0.75 MS 3056 b a ab ab b 0.75 WW 3059 b a b b b 0.77 LSD (cultivar) c t d c d c d c d c d z NDVI1 calculated as (R 810 -R 710 )/(R 810 +R 710 ) where R XX stands for reflectance at wavelength xx. y NDVI2 calculated as (R 810 -R 660 )/(R 810 -R 660 ) where R XX stands for reflectance at wavelength xx. x NDVI3 calculated as (R 810 -R 610 )/(R 810 +R 610 ) where R XX stands for reflectance at wavelength xx. w GNDVI calculated as (R 810 -R 560 )/(R 810 -R 560 ) where R XX stands for reflectance at wavelength xx. v SS = severely stressed; MS = moderately stressed; and WW = well-watered. u Same letters within a column indicate that there is no significant difference between the irrigation treatments, whereas different letters indicate significant difference at P=0.05. t Although the mean values for each parameter are separated by irrigation treatment and cultivar, the letters a and b show mean separation by Fisher s LSD based on irrigation treatment alone. 132

8 Summaries of Arkansas Cotton Research 2004 Table 2. Summary of soil-moisture tension and average canopy temperature data collected on 17 and 19 Aug from the irrigation study field in Keiser, Ark., in The two cultivars used are FM819, an okra-leaf cultivar, and FM960, a standard cultivar. Soil-moisture tension Average canopy temp Irrigation Date of data 20cm 40cm between 9-10 am Average noon temp treatment collection FM819 FM960 FM819 FM960 FM819 FM960 FM819 FM (bars) ( F) SS z Aug ab y a 0.87 a a a MS Aug a b 0.47 b ab a WW Aug b* b 0.43 b b b SS Aug a a 0.87 a a a MS Aug a b 0.56 b a ab WW Aug a b 0.52b a b z SS = severely stressed, MS = moderately stressed, and WW = well-watered. y Same letters within a column under the same date indicate that there is no significant difference between the irrigation treatments for the same dates, whereas different letters indicate significant difference at P=