CWSI_Deficit irrigation.pdf

8/9/2019 CWSI_Deficit irrigation.pdf

http://slidepdf.com/reader/full/cwsideficit-irrigationpdf 1/9

Agricultural Water Management 118 (2013) 113–121

Contents lists available at SciVerse ScienceDirect

Agricultural Water Management

journal homepage: www.elsevier .com/ locate /agwat

Yield, quality and crop water stress index relationships for deficit-irrigatedsoybean [Glycine max (L.) Merr.] in sub-humid climatic conditions

Burak Nazmi Candogana,∗, Mehmet Sincikb, Hakan Buyukcangaza, Cigdem Demirtasa,Abdurrahim Tanju Goksoy b, Senih Yazgan a

a Department of BiosystemsEngineering, Faculty of Agriculture, Uluda˘ g University, 16059 Campus of Görükle, Bursa,Turkeyb Department of Field Crops, Faculty of Agriculture, Uluda˘ g University, 16059 Campus of Görükle, Bursa, Turkey

a r t i c l e i n f o

Article history:

Received 26 January 2012Accepted 27 November 2012Available online 9 January 2013

Keywords:

Crop water stress indexIrrigation schedulingSoybeanSeed yieldQualityWater use efficiency

a b s t r a c t

Field experiments were conducted for 2 years to evaluate the use of the crop water stress index (CWSI)for irrigation scheduling of soybeans under a sub-humid climate of Bursa, Turkey. Additionally, statisticalrelationships between CWSI and seed yield, quality parameters, crop evapotranspiration (ETc) and wateruse efficiency (WUE) were investigated. Irrigations were scheduled based on the replenishment of 100(T 100), 75 (T 75), 50 (T 50), 25 (T 25), and 0% (T 0) of soil water depletion from a soil depth of 90 cm usinga 7-day irrigation interval. To compute CWSI, lower (nonstressed) and upper (stressed) baselines weredevelopedbased on the canopy temperature (T c) measurements of fully irrigated and rain-fed treatments,respectively. According to results, CWSI could be used to determine the irrigation time of soybean forsub-humid climate and 0.22 could be offered as a threshold value. Statistically significant relationshipswere determined between CWSI and seed yield, protein yield, oil yield, ETc and WUE. The polynomialrelationship between WUE and CWSI demonstrated that highest WUE could be obtained under CWSIclose to 0.6. Consequently, an irrigation schedule that considers water stress could be employed whenthe cost of water is high and/or water is scarce. However, because of the yield reduction, all economicaspects of water limited irrigation scheduling should be considered before making this decision. Weconclude that the CWSI could be used to evaluate crop water stress and improve irrigation scheduling

for soybeans under sub-humid climatic conditions.© 2012 Elsevier B.V. All rights reserved.

1. Introduction

Water stress is a major factor limiting soybean production inthe semi-arid andsub-humid regions of the world.In these regions,thefrequencyand amountof rainfall duringthe growing seasonareoftenquitevariable.Undernon-irrigatedconditionsinhumidareas,variability in seasonal rainfall leads to year-to-year variability inthe uptake of water and nutrients, and in the growth, developmentand yield of the crop(Scott et al., 1986). Under semi-arid and sub-humidclimate conditions, well-scheduled irrigationis necessary toincrease seed yield and ensure stability in yields.

Irrigation scheduling methods are generally based on mea-surement of soil water content or meteorological parameters for

∗ Correspondingauthorat: Uludag University, Faculty of Agriculture,Departmentof Biosystems Engineering, 16059 Campus of Görükle, Bursa, Turkey.Tel.: +90 2242941628; fax: +90 2242941628.

E-mail addresses:[email protected], [email protected](B.N. Candogan), [email protected] (M. Sincik), [email protected](H. Buyukcangaz), [email protected] (C. Demirtas), [email protected](A.T. Goksoy), [email protected] (S. Yazgan).

modeling or computing evapotranspiration. Irrigation schedulingbased upon crop water status should be more advantageous sincecrops respond to both the soil and aerial environment (evap-orative demand) (Yazar et al., 1999). With the development of infrared thermometry (IRT), it has been widely applied to mea-sure canopy temperature by IRTs to detect water stress of crops(Testi et al., 2008). Idso et al. (1981) developed the crop waterstress index (CWSI) to quantify crop water stress. These authorsutilized the relationships between the air and canopy surface tem-perature difference (T c − T a) and the vapor pressure deficit (VPD)under non-water-stressed and fully water-stressed conditions. Forwell-irrigated plants, which are assumed to be transpiring at thepotential rate, the relationship is linear, and the linear regres-sion is used as the lower non-water-stressed baseline. The upperbaseline represents T c − T a for fully water-stressed plants that arenot transpiring at a given VPD. The CWSI varies from 0 to 1 asmeasured canopy temperatures fall between the upper and lowerbaselines at any measured vapor pressure deficit. Jackson et al.(1981) presents a theoretical method based on the energy balancefor calculating CWSI by estimating or measuring the net radia-tion, aerodynamic resistance,T c −T a andVPD. More recently,a newnon-water-stressed baseline was proposed by Alves and Pereira

0378-3774/$ – see front matter © 2012 Elsevier B.V. All rights reserved.

http://dx.doi.org/10.1016/j.agwat.2012.11.021

http://localhost/var/www/apps/conversion/tmp/scratch_5/dx.doi.org/10.1016/j.agwat.2012.11.021


http://www.sciencedirect.com/science/journal/03783774

http://www.elsevier.com/locate/agwat

mailto:[email protected]
















http://www.elsevier.com/locate/agwat

http://www.sciencedirect.com/science/journal/03783774






B.N. Candogan et al./ AgriculturalWater Management 118 (2013) 113–121 115

Table 1

Mean air temperature, relative humidity, wind speed, solar radiation and total monthly rainfall during 2005–2006 and long-term measurements (1975–2003) at Bursa.

Year Month Air temperature (◦C) Relative h umidity ( %) Wind s peeda (m s−1) Solar r adiation ( MJ m−2 d−1) Rainfall (mm)

2005 April 13.7 60 1.8 13.1 56May 17.6 68 1.9 18.3 23 June 21.2 58 1.8 23.7 21 July 24.7 62 1.7 22.4 55August 25.1 63 1.6 19.3 4September 20.1 68 1.5 14.8 16

October 13.2 72 1.2 9.7 372006 April 12.1 74 1.9 16.9 20

May 16.6 61 1.8 22.2 9 June 21.5 64 1.7 23.2 43 July 23.8 52 2.3 24.2 2August 26.4 50 2.0 20.1 3September 19.9 65 1.5 14.3 91October 16.7 77 1.3 9.9 25

Long-term average April 12.9 68 2.0 14.0 70May 17.6 65 1.9 17.6 47 June 22.3 58 2.0 20.0 33 July 24.5 57 2.2 19.8 20August 24.1 60 2.1 17.8 15September 20.1 65 1.7 14.5 35October 15.3 72 1.5 9.7 72

a Average wind speed (at 2m height).

and protein percentages were defined using the Soxhlet extractiontechnique (Pomeranz and Clifton, 1994) and the Kjeldahl method(Ivanov, 1974), respectively. Oil and protein yields were calculatedas functionsof the seed yield andcrude oiland protein percentages.Water use efficiency (WUE) values were calculated as the ratio of seed yield to seasonal ETc foreach treatment separately (Bos, 1980;Howell, 2001; Pala et al., 2007).

The canopy temperature (T c) was measured with a hand-heldinfrared thermometer (Model 100.3 ZL, Everest Interscience, Inc.,USA). Thespectral band-passof theinfraredthermometer (IRT) was8–14m with a resolution of 0.1 ◦C and a field of view (FOV) of 4◦. Emissivity was set to 0.98. Before surface temperature mea-

surements, infrared thermometry device were controlled by thecommercial Everest black body surface. In the first year, the IRTdata were collected between day after planting (DAP) 81 and DAP130 (July 11, full bloom – August 29, full maturity) to determinethe CWSI of soybeans, but in the second year, the T c values weremeasured between DAP 75 and DAP 127 (July 17, full bloom –September 7, full maturity). Total rainfall recorded during T c mea-surements in 2005 and 2006 were 20 and 7mm, respectively. T cmeasurements were taken at 1300 h and 1400 h (Central StandardTime)on two daysper week, during which times the sun was unob-scured by clouds. Three instantaneous canopy temperatures weremeasured from the east, west, north and south directions of twocenter rows at 1m distance from crop canopy in each drip plot,and the IRT was hand-held at approximately 1.5 m above the soil

surface. The IRT was held at an oblique view angle of 30◦

–40◦

below horizontal to the crops to minimize the effect of the soilbackground that was in the field of view. The mean T c for eachplot was calculated as the average of 24 measurements per day.Dry and wet-bulb temperatures were measured using an aspiratedpsychrometer placed at a height of 1.5 m within the experimentfieldunder an agricultural meteorological shield. The VPDwas com-puted using the standard psychrometer equation based on dry andwet bulb temperatures (Allen et al., 1998).

CWSI was calculated according to Idso et al. (1981) by usingmeasured T c −T a and VPD. The non-water-stressed baseline forsoybeans was determined from the data collected after irrigationin T 100 treatment, in which the crop was irrigated well. The dataset was obtained from data collected from 0700h to 1900h at 1-h

intervals on DAP 100 for both 2005 and 2006, and on data collected

at 1300h and1400 h during measurement periods betweenDAP 81and DAP 130 in2005,and DAP 75 and DAP 127 in 2006. The upperbaseline was determined usingthe canopy temperatures measuredfrom fully stressed plants(in theT 0 treatment) at1300h and 1400hon two days per week during the measurement periods in bothyears.

The empirical CWSI was calculated as (Eq. (3)):

CWSI =(T c − T a) − (T c − T a)ll

(T c − T a)ul − (T c − T a)ll(3)

where (T c −T a)ul is the upper limit of the canopy temperatureminus the air temperature (non-transpiring crop) and (T c −T a)ll is

the lower limit of the canopy temperature minus the air tempera-ture (well-irrigated crop).

Analysis of variance (ANOVA) was performed on seed yield andquality data. The effects of the irrigation treatments were evalu-ated at the 0.05 and 0.01 probability levels using the F -test. TheF -protected least significant difference (LSD) was calculated at the0.05 probabilitylevel according to Steel and Torrie(1980). The rela-tionships between CWSI and each of ETc, seed yield, protein yield,oilyieldand WUEwere foundusingregressionanalysis. Linear, log-arithmic, polynomial, power and exponential regression analyseswere carried out. The analyses generated values for slope, inter-cept, coefficient of determination (R2) and root mean square error(RMSE), and the most appropriate regression equation was definedbased on the values of R2 and RMSE.

3. Results and discussion

3.1. The amount of irrigation water applied and crop water use

Table 2 lists the irrigation depths applied to each treatment,the received seasonal rainfall and the estimated total ETc basedon soil water balance. The maximum irrigation water amountsapplied in the T 100 treatment were 554mm (2005) and 679 mm(2006), andthe T 0 treatment was rainfed. However, although insuf-ficient soil moisture occurred during the sowing period of 2006,15mm of irrigation water was applied to the T 0 treatment usinga sprinkler irrigation system. Seasonal ETc values varied between394 and 802mm in 2005 and between 351 and 841mm in 2006.

TotalET0 valuesfor2005and2006growingseasonswereestimated



116 B.N. Candogan et al. / AgriculturalWaterManagement 118 (2013) 113–121

Table 2

Theseed yield, total amount of irrigation water, and seasonal crop evapotranspiration (ETc) for soybeansin 2005 and 2006.

Year Treatment Seed yield (t ha−1) Total irrigation (mm) Rainfalla (mm) Seasonal ETc (mm)

2005 T100 3.93 554 156 802T75 3.42 416 687T50 3.23 277 576T25 3.01 139 480T0 2.16 0 394

2006 T100 3.59 679 150 841

T75 3.23 513 702T50 2.94 347 590T25 2.45 181 462T0 1.98 15 351

a Rainfall received from planting to harvest.

as 703mm and 730mm, respectively. Soybean K c values understandard conditions given by Allen et al. (1998) were 0.40,1.15and0.50 for initial (K c ini), mid (K c mid) and end (K c end) season, respec-tively. Based on these values andEq. (2), adjusted K c values forlocalclimatic conditions were obtained. Consequently, in 2005, adjustedK c ini, K c mid and K c end values were 0.40, 1.13and 0.48, respectively,and similarly in 2005, respective adjusted K c values were 0.40,

1.14 and 0.47. Based on ET0 values and adjusted K c for standardconditions ETc values could be estimated as 633mm (2005) and666 mm (2006). Crop water use values of this study calculated fornon-water stressed soybeans were higher than estimated ETc val-ues. Thesource of these differences could be depended on variety of the crop, length of growing periodand sub-surface flows, which wecannot, able to define. In addition to this, probably after irrigationevents higher evaporation occurred from the wetted and exposedsoil surface.

3.2. Seed yield and quality components

Seed yield and the quality components for 2005 and 2006 aresummarizedin Table3. Basedontheanalysisofvarianceresults,the

differencesbetween the irrigationtreatmentswere statistically sig-nificant at the 0.01confidence level forthe seed yield; protein yieldand oil yield in eachyear(2005 and 2006).In addition, the effects of the irrigation treatments on the protein and oil percentages weresignificant at the 0.05 confidence level in each year.

Seed yield declined with decreasing ETc values and thus, withincreasing water deficit (Fig. 1). Based on statistical analysis, therelationship between soybean seed yield and ETc was polynomial(P < 0.01, RMSE= 0.18) for 2 years. The coefficient of determinationwas calculated as 0.90. Seed yields varied from 2.16t ha−1 (T 0) to3.93tha−1 (T 100) in 2005 and from 1.98t ha−1 (T 0) to 3.59 t h a−1

(T 100)in2006(Table3). Comparedwiththenon-irrigatedtreatment(T 0), all irrigation treatments significantly increased the seed yield.Crop yield values especially under no water stress conditions were

similar to previously reported ones (Ashley and Ethridge, 1978;Specht et al., 1989; Foroud et al., 1993; Evett et al., 2000; Karamet al., 2005; Dogan et al., 2007; O’Shaughnessy et al., 2011). In2005, the seed yield reductions observed forthe T 0, T 25, T 50,and T 75

treatments were 45, 23, 18, and 13% of the non-stressed treatmentT 100 , respectively. Similarly in 2006, the yield reductions were 45,32, 18, and 10% for the T 0, T 25, T 50 , and T 75 respective treatments.A decrease in soybean yield under water deficit or drought-stressconditions has been reported previously (Eck et al., 1987; De Costaand Shanmugathasan, 2002; Karam et al., 2005; Rosadi et al., 2005;Dogan et al., 2007). Nielsen (1990) reported a linear response of soybean yield to ETc (Yield (kg ha−1)= 6.53 ETc (mm)−17). Thisrelationshippredicts nearlythe same yields as the polynomial rela-tionship found in the current study for ETc less than 500 mm, but

much greater yields for ETc greater than 500mm.

The proteinpercentage values varied from 31.3to 35.0% in 2005and from 30.7 to 33.2% in 2006. The lowest protein percentagewas obtained in the seeds obtained in the T 100 treatment, whichproduced the greatest yield (3.93 and 3.59t ha−1 in 2005 and2006, respectively) (Table 3). Our results are in agreement withthose of Xiaobing et al. (2004), who reported that protein contentdecreased as soybean yield was maximized using irrigation and

the application of manure. A minimal effect on seed protein anda variable effect on oil content were reported when using a singleirrigation period at the reproductive stage (Sweeney et al., 2003),but a significant decline in seed protein content was observedwith high yields of soybeans in this study, indicating that main-taining a higher level of field water capacity during the growingseason is detrimental to seed protein accumulation in soybeans.A study conducted by Bellaloui and Mengistu (2008) showedthat whereas the percentage of protein was higher in the cultivar‘Dwight’ under full-season and reproductive stage irrigation thanunder non-irrigation, the percentage of protein was higher in thecultivar ‘Freedom’ under non-irrigation than under full-seasonor reproductive stage irrigation. As a result, the authors reportedthat the tested cultivars responded differently to irrigation. This

response may underline maturity or/and genotype differences andtheir adaptations to environmental stress factors such as soil watercontentand temperature (Bellaloui and Mengistu, 2008). However,calculated protein yield values varied from 0.76 to 1.23t ha−1 andfrom0.66to1.10tha−1 in 2005and 2006, respectively.The highestprotein yield was obtained in the T 100 treatment in both years.

Fig.1. Therelationshipbetweenseedyield(Y )andseasonalcropevapotranspiration

(ETc).R2

:determinationcoefficient;**

Significantatthe1%probabilitylevel(P <0.01).




Table 3

Seed yield, quality components, water useefficiency (WUE),seasonal mean CWSI, and mean CWSI beforesoybean irrigations in 2005 and 2006.

Year Treatment Seed yield(tha−1)

Proteincontent (%)

Oil content(%)

Protein yield(tha−1)

Oil yield(tha−1)

WUE (kgm−3) Mean CWSI Mean CWSIbefore irrigation

2005 T100 3.93 aa 31.3 c 19.4 a 1.23 a 0.77 a 0.49 0.18 0.23T75 3.42 b 32.1 bc 18.7 ab 1.10 ab 0.64 b 0.50 0.25 0.33T50 3.23 bc 32.8 abc 17.5 bc 1.06 b 0.56 c 0.56 0.34 0.42T25 3.01 c 33.7 ab 17.3 bc 1.01 b 0.52 c 0.63 0.54 0.61T0 2.16 d 35.0 a 16.5 c 0.76 c 0.36 d 0.55 0.92 –

LSD0.05 0.3479 2.343 1.596 0.1570 0.06066 – – –F -test ** * * ** ** – – –

2006 T100 3.59 a 30.7 b 20.5 a 1.10 a 0.73 a 0.43 0.15 0.20T75 3.23 ab 32.0 ab 19.5 ab 1.03 ab 0.63 b 0.46 0.21 0.30T50 2.94 b 32.3 a 19.1 b 0.95 bc 0.56 b 0.50 0.35 0.44T25 2.45 c 33.1 a 18.9 b 0.81 c 0.46 c 0.53 0.59 0.68T0 1.98 d 33.2 a 18.9 b 0.66 d 0.37 d 0.56 1.04 –LSD0.05 0.4397 1.599 1.054 0.1456 0.08935 – – –F -test ** * * ** ** – – –

a The valuesmarked with the same letterare statistically homogeneous in theLSD test.* Significant at the5% probability level (P <0.05).

** Significant at the1% probability level (P <0.01).

Whereas the percentage of protein decreased as the amount of applied water increased, the yield of soybean protein increased. A

polynomial relationship with an R2

of 0.85 (P < 0.01, RMSE= 0.07)existed between the protein yield and ETc (Fig. 2).In both years the lowest oil percentages were obtained from

the soybeans that received the T 0 treatment (16.5% and 18.9%,respectively), and the highest oil percentages were obtained fromthe soybeans that received the T 100 treatment (19.4% and 20.5,respectively) (Table 3). A previous study with soybeans showedthat the oil content was greatest and the soybean yield was maxi-mal with irrigation and the application of manure (Xiaobing et al.,2004). Because the maximum seed yield in our study was obtainedfrom treatment T 100 in both years, our results agreed with thoseof Xiaobing et al. (2004). Another study, which was conducted toinvestigate the effect of irrigation regimes andsoybean cultivars onseed composition, demonstratedthat whereas the cultivar ‘Dwight’accumulated a higher oil content without irrigation comparedwith that accumulated under full-season and reproductive stageirrigation, the cultivar ‘Freedom’ accumulated a lower oil contentwithout irrigation compared with that accumulated under full-season or reproductive stage irrigation (Bellaloui and Mengistu,2008). This result suggests that oilcontent may depend on the level

Fig. 2. The relationship between protein yield (PY) and seasonal crop evapotrans-piration (ETc). R2: determinationcoefficient; **Significant at the 1% probability level

(P <0.01).

of water stress (drought) (Specht et al., 2001), genotype (Maestriet al., 1998; Piper and Boote, 1999), and the timing to full maturity

of these cultivars (Zhang et al., 2005). Other studies have demon-strated that yield andoil contentare generallypositively correlatedand that thehigh yield potential of soybeans is achieved when suf-ficient water and fertilizer are available (Calpten, 1986; Schonerand Fehr, 1979; Xu and Zhang, 1995). Oil yields varied from 0.36to 0.77tha−1 in 2005, and similarly, oil yields ranged from 0.37to 0.73t ha−1 in 2006. The highest oil yield was calculated for T 100

treatment similar to the oil percentage. The seasonal ETc valueswere linearly related to soybean oil yield (Fig. 3) (R2 =0.96, P <0.01and RMSE= 0.03). Our results showed that the oil yield increasedlinearly with increasing irrigation water amount or ETc.

3.3. Water use efficiency

Water use efficiency values were varied from 0.49kgm−3

to0.63kgm−3 and from0.43 kgm−3 to0.56kgm−3 in 2005 and 2006,respectively (Table 3). In both years, the WUE values were higherin treatment T 25 than that of the other irrigation treatments havehigher water level. The quadratic relationship obtained throughcomparison between WUE and ETc was showed this trend clearly(Fig. 4) which has a determination coefficient of 0.63 (P <0.01,

Fig.3. Therelationshipbetweenoil yield(OY)and seasonalcrop evapotranspiration

(ETc).R2

:determinationcoefficient;**

Significantatthe1%probabilitylevel(P <0.01).



118 B.N. Candogan et al. / AgriculturalWaterManagement 118 (2013) 113–121

Fig. 4. The relationship between water use efficiency (WUE) and seasonal cropevapotranspiration (ETc). R2: determination coefficient; **Significant at the 1% prob-ability level (P <0.01).

RMSE = 0.04).These findings were in agreement with that of Burriroet al. (2002), who reported that WUE increases with soil moisturestress. AlthoughT 25 irrigation treatmenthas the highest WUElevel,this type of irrigation scheduling could cause 27.5% yield decrease.However, we can offer the irrigation strategy of T 25 for either watershortage or high water fee conditions. Based on the ETc, yield andWUE findings of this study and recent economic situations finaldecision related to irrigation water management for soybean couldbe made.

3.4. Crop water stress index and baselines

AsshowninFig.5, thevalueof(T c −T a)ul was calculated as 3.2◦C(n=33)usingthecombineddataofthe2years.Also,thelinearequa-

tionforthelowerlimitbaselineswasdefinedas(T c −T a)ll =−1.8549VPD+ 0.6499 (R2 =0.7561, n=33, P < 0.001, RMSE = 0.73, andS yx =0.76). The intercept and slope of the lower baseline in thisstudy were somewhat different from previously published valuesfor soybean. Idso (1982) reported that the relationship between

Fig. 5. The canopy–air temperature differential (T c −T a) versus the air vapor pres-sure deficit (VPD) for non-water stressed and maximally stressed soybeans. R2:

determination coefficient; **

Significant at the1% probability level (P <0.01).

Fig.6. Variationsof thecrop water stress index(CWSI)for eachirrigation treatmentduringthe (a)2005 and (b)2006 experiments.

T c −T a and VPD for well-watered soybeans was T c −T a =−1.34VPD+ 1.44 (R=0.897, and S yx =0.83) for sunlit conditions. Thislower baseline equation was derived from climate data collectedover soybeans at Manhattan, Kansas and Fargo, North Dakota.The intercept and slope obtained in that study were higher thanthose obtained in our study. The equation determined by Nielsen

(1990) f or non-water stressed soybean crops was T c −T a =−2.02VPD+ 2.51 at Akron, Colorado. The intercept in that study washigherand slope lower than the values obtained in ourstudy.Thesedifferences may be attributed differences in the climatic conditionsand plant variety used. Gardner and Shock (1989) suggested thatthe VPD range from 1 to 6kPa to define a baseline that could besuitable foruse in CWSI calculationsat other locations. In ourstudy,the range of VPD observed was approximately 1–4kPa, narrowerthan that suggested by Gardner and Shock (1989). Hattendorf et al.(1988) noted that it might be more difficult to establish a mean-ingful non-water-stressed baseline due to climatic variability overseveral years and due to thelimitedrange of VPD in wetter years inhumid and sub-humid climates. However, we were able to defineabaseline with a high coefficient of determination (R2 = 0.7561, and

P < 0.001). Additionally, Lebourgeois et al. (2010) showed that theempirical approach of Idso et al. (1981), which was optimal in aridor semi-arid climates, could be used effectively to measure plantwater status, even in humid conditions with low VPD.

Fig. 6(a) and (b) shows the seasonal course of CWSI values com-puted for each irrigation treatment in 2005 and 2006, respectively.Thehighest CWSI valuecalculated forT 100 treatmentwas 0.36. Dur-ing the growing seasons of 2005 and 2006, CWSI values reached0.47 and 0.37 for T 75; 0.50 and 0.57 for T 50; 0.68 and 0.80 for T 25,respectively. Throughoutboth growing seasons CWSIof T 0 (rainfed)fluctuated close to 1. Although CWSI was calculated based on thelower and upper limits derived from measurements obtained fromthe fully irrigated (T 100) and rainfed (T 0) treatments, some CWSIvalues related to the T 100 and T 0 treatments were lower than zero

andhigher than 1.0, respectively. TheCWSIvalues were affected not




Fig. 7. The relationships between seed yield (Y ), crop evapotranspiration (ETc), protein yield (PY), oil yield (OY), water use efficiency (WUE) and seasonal mean crop waterstress index (CWSI). R2: determination coefficient; ** Significant at the1% probability level (P <0.01).

only by T c −T a , depending on the transpiration of crop but also by

the VPD ofthe air,and thus, CWSI was verysensitive to waterstressinthesoybeancrops.BasedontheseresultsamaximumCWSIvalueof nearly 0.36 could be offered as a threshold number for excellentirrigation timing and CWSI over that values could be caused waterstress and thus yield reduction.

3.5. Comparisons between yield, ET c , quality parameters,WUE

and CWSI

Seed yield, quality components, WUE, seasonal mean CWSI andmean CWSI values related to data measured before irrigations(CWSIbi) of each treatment for both experiment years were cal-culated and are presented in Table 3. The seasonal mean CWSI andCWSIbi values of the T 100 treatment were 0.17 and 0.22 (2-year

averages), respectively. The results indicated that if the seasonal

mean CWSI exceeds 0.17, then the soybean seed, protein and oil

yields would decrease. Nielsen (1990) used four threshold valuesof CWSI (i.e., CWSI= 0.2, 0.3, 0.4 and 0.5) to initiate irrigations anddetermined that the threshold CWSI value of 0.2 resulted in thehighest seed yield of drip-irrigated soybean in Akron, CO, USA. Thisvalue was close to the mean CWSI of approximately 0.22 beforeirrigation that was obtained in our study.

Fig. 7 shows a summary of the comparison statistics betweenthe mean CWSI values and soybean seed yield, seasonal ETc, pro-tein yield, oil yield and WUE. All relationships were significantat the 0.01 probability level. To define the highest coefficient of determination, linear, logarithmic, polynomial, power and expo-nential regression analyses were conducted. As a result, thehighest determination coefficients were calculated for the expo-nential relationship between seed yield and mean CWSI (R2 =0.92,

RMSE = 0.19), the power relationship between ETc and mean CWSI



http://www.kimberly.uidaho.edu/water/asceewri/




Jackson, R.D., Idso, S.B., Reginato,R.J., Pinter, P.J., 1981.Canopy temperatureas a cropwater stress indicator. Water Resources Research 17, 1133–1138.

Jensen,M.E., 1980. Designand Operationof Farm Irrigation Systems.An ASAE mono-graph Number 3. American Society of Agricultural Engineers, 2950 Niles Road,Michigan 49085, USA, p. 829.

Jensen,M.E., Burman,R.D., Allen, R.G., 1990.Evapotranspiration and IrrigationWaterRequirements.Manuals andReportson EngineeringPractices No.70., ASCE, NewYork.

Kar, G., Kumar, A., 2007. Surface energy fluxes and crop water stress index ingroundnut under irrigatedecosystem. Agricultural and Forest Meteorology146,94–106.

Karam, F., Masaad, R., Mounzer, T.O., Rouphael, Y., 2005. Evapotranspiration andseedyieldof fieldgrownsoybean under deficit irrigationconditions.AgriculturalWater Management 75, 226–244.

Kayam, Y., Beyazgül, M., 2001. Infrared termometre teknigininpamuk sulamasındakullanılma olanakları. Toprak ve Su Kaynakları Aras tırma Yıllıgı 2000. KöyHizmetleri Genel Müdürlügü, Toprak ve Su Kaynakları Sube Müdürlügü, yayın117, 312–326, Ankara.

Kırnak, H.,Dogan, E.,2009. Effectof seasonalwaterstress imposed on drip irrigatedsecond crop watermelon grown in semi-arid climatic conditions. IrrigationSci-ence 27, 155–164.

Köksal, E.S., 2006. Sulama suyu düzeylerinin sekerpancarının verim, kalite vefizyolojik özellikleri üzerindeki etkisinin, infrared termometre ve spektro-radyometre ile belirlenmesi. Doktora Tezi. Ankara Üniversitesi, Fen BilimleriEnstitüsü, Tarımsal Yapılar ve Sulama Anabilim Dalı, Ankara, 101sayfa.

Köksal, E.S., 2008.Irrigation water management with water deficit indexcalculatedbased on oblique viewed surface temperature. Irrigation Science 27, 41–56.

Lebourgeois, V., Chopart, J.L., Bégué, A., Le Mézo, L., 2010. Towards using a thermalinfrared index combined with water balance modelling to monitor sugarcaneirrigationin a tropicalenvironment.AgriculturalWater Management 97,75–82.

Maestri, D.M., Labuckas, D.O., Meriles, J.M., Lamarques, A.L., Zygadlo, J.A., Guzman,C.A., 1998. Seed composition of soybean cultivars evaluated in different envi-ronmental regions. Journal of theScience of Food and Agriculture 77, 494–498.

Nielsen,D.C.,Gardner,B.R.,1987.Scheduling irrigations forcorn withthe cropwaterstress index (CWSI). Applied Agricultural Research 2, 295–300.

Nielsen, D.C., Anderson, R.L., 1989. Infrared thermometry to measure single leaf temperaturesfor quantificationof water stress in sunflowers. AgronomyJournal81, 840–842.

Nielsen, D.C., 1990. Scheduling irrigations for soybeans with the crop water stressindex (CWSI). Field Crops Research 23, 103–116.

Nielsen, D.C., Halvorson, A.D., 1991. Nitrogen fertility influence on water stress andyield of winter wheat. Agronomy Journal 83, 1065–1070.

Nielsen,D.C., 1994.Non-water-stressedbaselines for sunflowers.Agricultural WaterManagement 26, 265–276.

Nielsen, D.C., 1997. Water use and yield of canola under dryland conditions in thecentral Great Plains. Journal of Production Agriculture 10, 307–313.

Orta, A.H., Erdem, Y., Erdem, T., 2003. Crop water stress index for watermelon.Scientia Horticulturae 98, 121–130.

O’Shaughnessy, S.A., Evett, S.R., Colaizzi, P.D., Howell, T.A., 2011. Using radiationthermography and thermometry to evaluate crop water stress in soybean andcotton. Agricultural Water Management 98, 1523–1535.

Pala, M., Ryan, J., Zhang, H., Singh, M., Harris, H.C., 2007. Water-use efficiency of wheat-based rotation systems in a Mediterranean environment. AgriculturalWater Management 93, 136–144.

Piper, E.L., Boote, K.J., 1999. Temperature and cultivar effects on soybean seed oiland protein concentrations. Journal of the American Oil Chemists Society 76,1233–1242.

Pomeranz, Y., Clifton, E., 1994. Food Analysis Theory and Practice, 3rd ed. KluwerAcademic Publisher, San Diego.

Qiu, G.Y., 1996. A new method for estimation of evapotranspiration. Doctoral dis-sertation, the United Graduate School of Agriculture Science, Tottori University, Japan.

Qiu, G.Y., Momii, K., Yano, T., 1996a. Estimation of plant transpiration by imitationleaftemperature, I. Theoretical considerationand fieldverification.Transactionsof the JSIDRE 64, 401–410.

Qiu, G.Y., Yano, T., Momii, K., 1996b. Estimation of plant transpiration by imitationleaf temperature. II. Application of imitation leaf temperature for detection of crop water stress. Transactionsof the JSIDRE 64, 767–773.

Rosadi, R.A.B., Afandi, M.S., Senge, M., Ito, K., Adomako, J.T., 2005. Critical watercontent and water stress coefficient of soybean (Glycine max [L.] Merr.) underdeficit irrigation. Paddy and Water Environment 3, 219–223.

Schoner, C.S., Fehr, W.R., 1979. Utilization of plantintroductionin soybean breedingpopulation. Crop Science 11, 185–188.

Scott, H.D., Ferguson, J.A., Sojka,R.E., Batchelor,J.T.,1986.Responseof Lee74 Soybeanto Irrigationin Arkansas.In: ArkansasAgric. Exp.Stn. Bull. 886, Univ. of ArkansasAgric. Exp. Stn., Fayetteville, AR.

Specht, J.E., Elmore, R.W., Eisenhauer, D.E., Klocke, N.W., 1989. Growth stagescheduling criteria for sprinkler-irrigated soybeans. Irrigation Science 10,99–111.

Specht, J.E., Chase, K., Macrander, M., Graef, G.L., Chung, J., Markwell, J.P., Orf, H.H.,Lark,K.G., 2001. Soybeanresponse to water: a QTLanalysisof droughttolerance.Crop Science 41, 493–509.

Steel, R.G.D., Torrie, J.H., 1980. Principles and Procedures of Statistics, 2nd ed.McGraw-Hill, New York.

Sweeney, D.W., Long, J.H., Kirkham, M.B., 2003. A singleirrigation to improve earlymaturing soybean yield and quality. Soil Science Society of America Journal 67,235–240.

S ims ek, M., Tonkaz, T., Kac ıra, M., Cömlekc ioglu, N., Dogan, Z., 2005. The effects of different irrigation regimes on cucumber (Cucumbis sativus L.) yield and yieldcharacteristicsunderopen fieldconditions.AgriculturalWater Management 73,173–191.

Testi, L., Goldhamer, D.A., Iniesta, F., Salinas, M., 2008. Crop water stress indexis a sensitive water stress indicator in pistachio trees. Irrigation Science 26,395–405.

Xiaobing, L., Herbert, S.J., Jin, J., Zhang, Q., Wang, G., 2004. Responses of photosyn-thetic ratesand yield/quality of maincrops to irrigationand manure applicationin theblack soil area of Northeast China. Plant Soil 261, 55–60.

Xu, X.B., Zhang,X.T., 1995. Density and fertilizer doses in relation to soybean yield.Tillage Culture 2, 18–19.

Yazar, A., Howell, T.A., Dusek, D.A., Copeland, K.S., 1999. Evaluation of crop water stres index for LEPA irri gated corn. Irrigation Science 18,171–180.

Yuan, G., Luo Yi Sun, X., Tang, D., 2004. Evaluation of a crop water stress index fordetecting water stress in winter wheat in the North China Plain. AgriculturalWater Management 64, 29–40.

Zhang, M., Kang, M.S., Reese, P.F., Bhardwaj, H.L., 2005. Soybean cultivar evaluationvia GGE biplotanalysis. Journal of NewSeeds7, 37–50.

Documents

CWSI_Deficit irrigation.pdf