Printer Friendly

Diagnosing Crop Water Stress of Rice using Infra-red Thermal Imager under Water Deficit Condition.

Byline: Junzeng Xu, Yuping Lv, Xiaoyin Liu, Twecan Dalson, Shihong Yang and Jia Wu

Abstract

A thermal imager was used for measuring the canopy temperature to calculate crop water stress index (CWSI) of rice under water deficit condition. The CWSI varied diurnally with peaks appeared at noon, and soil water deficit led to higher CWSI values during noon. Transpiration rate (Tr), stomatal conductance (gs) and net photosynthetic rate (Pn) were high at low CWSI, and reduced with increasing CWSI. The relationship between CWSI and Pn, Tr or gs at noon was described by quadratic polynomial equations. At critical noon, CWSI values for the decline trend in Pn (0.303, 0.385 and 0.446 at tillering, panicle initiation to booting, milk to soft dough stage) were higher than for decline in Tr and gs.

Assuming a 5% reduction in Pn from maximum is moderate water deficit, the critical CWSI values were 0.420, 0.472 and 0.536 at tillering, panicle initiation to booting and milk to soft dough stages. CWSI at 14:00 decreased significantly with increasing relative soil moisture contents. There was a slight difference between the linear relations under different vapor pressure deficit (VPD) conditions. The critical relative soil moisture contents for a 5% reduction in Pn were 1.57%, 1.18% and 1.27% higher under high VPD than low VPD conditions. It implied that rice water status was determined in conjunction with field soil moisture content and air aridity. The water deficit diagnosis based on canopy temperature tracked by thermal infrared imager is a promising method in reflecting the conjuncted function of soil moisture deficit and air aridity on crop water status.

Keywords: Water deficit diagnosis; Crop water stress index; Vapor pressure deficit; Net photosynthesis rate; Transpiration rate

Introduction

With increasing water scarcity, water deficit becomes one of the main abiotic stresses on crop production. Many researchers addressed the impact of water stress on crop physiological activity and growth. Moderate water stress improves crop yield and water use efficiency, while severe water deficit affects crop growth and eventually leads to loss in crop production (Turner, 1986). Crop water deficit diagnosis or water status monitoring is the base to proper irrigation scheduling. Thus, crop water deficit diagnosis methods based on soil water status, crop water potential, and leaf physiological parameters are of great concern (Yatapanage and So, 2001; Narasimhan and Srinivasan, 2005; Silva et al., 2007).

Methods based on crop physiological response to water stress, such as leaf water potential, leaf water content and stomatal conductance are considered as the most reliable one in qualifying crop water deficit (Jones, 2004). But these methods are always time consuming, sometimes destructive, and only provide points information.

Stomatal closure induced by water deficit reduces leaf transpiration rate, and consequently results in reduced evaporative cooling and increased leaf temperature (Berni et al., 2009). Indices based on leaf or canopy temperature are widely used in crop water deficit diagnosis since 1970's with the advent of hand-held thermometers (Idso et al., 1977, 1981; Jackson et al., 1981; Jones, 2004; Gontia and Tiwari, 2008; Peng et al., 2011), such as stress degree days (SDD) (Jackson et al., 1977; Patil et al., 2014), canopy temperature variability (CTV) (Clawson and Blad, 1982; Gonzalez-Dugo et al., 2006) and crop water stress index (CWSI).

CWSI has been applied in many different crops, such as wheat (Yuan et al., 2004; Gontia and Tiwari, 2008; Li et al., 2010), cotton (Silva and Rao, 2005; O'shaughnessy et al., 2011), maize (Anda, 2009; Li et al., 2010; Romano et al., 2011;Taghvaeian et al., 2012), bean (Erdem et al., 2006b), and some vegetables (Cremona et al., 2004; Simsek et al., 2005; Erdem et al., 2010; Aladenola and Madramootoo, 2014; Rud et al., 2014) or fruits (Erdem et al., 2006a; Paltineanu et al., 2009). Early researchers mostly scanned several pots by hand-held infrared thermometer under field to detect the crop water status. Recently, a portable thermal imagers, as non-invasive, non-destructive and versatile imaging tool for monitoring crop canopy temperature also has been used for crop water deficit diagnosis. Cohen et al. (2005) used thermal images taken with a radiometric infrared video camera to estimate the crop water status of irrigated cotton.

Jones et al. (2009) captured grapevine thermal images by using a Therma CAM P25 (FLIR Systems, Sweden) to investigate and quantify the plant response to water stress through remote diagnosis, and then validated on soybean and cotton by O'shaughnessy et al. (2011) using a Therma CAM SC2000 thermal infrared camera.

Paddy rice, one of the most widespread cereal crops in the Asian monsoon region, is traditionally flooded and did not suffer from water deficit. Thus, water deficit diagnosis method based on canopy thermal image is seldom reported in paddy rice. Cao et al. (2013) reported the use of infrared thermal imaging technology (Fluke Ti-125 infrared camera) in reflecting the rice water status.

With increasing water scarcity, water saving irrigation (WSI) techniques are widely used in rice paddies (Belder et al., 2004; Uphoff et al., 2010; Abbasi and Sepaskhah, 2011; Kato et al., 2011) exposing rice plants to a certain degree of water deficit.

Non-flooded controlled irrigation (CI), uses the ratio of soil moisture content to the saturated one for water deficit diagnosis, is widely used WSI technique in China (Mao, 2002; Peng et al., 2011). Under CI irrigation, rice is cultivated under non-flooding condition in about half of the rice season. The performance of water deficit diagnosis on WSI irrigated rice based on thermal imaging method is not clear.

Meanwhile, crop water status is determined conjunctively by the soil moisture content and air aridity (Jones et al., 1985; Wang et al., 2010; Belko, et al., 2013; Conaty, et al., 2014). The impact of different atmospheric vapor pressure deficit (VPD) on the relations between CWSI and soil moisture contents is still unknown.

Thus, infrared thermal images were taken from rice grown under water deficit conditions in East China. The CWSI was calculated based on the canopy temperature derived from the thermal images. The relations between CWSI and rice physiological activities such as leaf net photosynthesis rate (Pn), stomatal conductance (gs) and transpiration rate (Tr) were discussed to reveal if it is possible to diagnose the rice water status using the thermal image technique. Furthermore, we attempted to investigate whether the relationships between CWSI and soil moisture contents differed as changing atmospheric VPD.

Materials and Methods

Site Description and Experimental Design

The experiment was conducted in 2012 at the Kunshan irrigation and drainage experiment station (3115'15"N, 12057'43"E), Jiangsu, China. The study area has a humid subtropical monsoon climate (with average annual air temperature of 15.5C, mean annual precipitation of 1,097.1 mm).

The soil in the experimental field is dark-yellow hydromorphic paddy soil. The soil texture in the plowed layer is clay, with organic matter of 21.9 g kg-1, total nitrogen of 1.03 g kg-1, and total phosphorus of 1.35 g kg-1. The soil was collected from a rice field, then air-dried, ground, and passed through a 4 mm sieve to remove coarse fragments, and homogenized manually by using the shovels and rakes. Then the soil was packed into the bottom-sealed pots (55 cm 55 cm 65 cm) to the depth of 60 cm at the bulk density of 1.28, 1.33 and 1.35 g m-3 for soil depths of 0-10, 10-20 and 20-60 cm, respectively. The saturated soil water contents (v/v) for the layers of 0-20, 0-30, and 0-40 cm are 52.4, 49.7, and 47.8%, respectively.

The rice variety, Nanjing 46, was transplanted in the density of 9 hills (3 plants per hill) per pot on June 28 in 2012.

There were four water deficit treatments, W1, W2, W3, and W4 treatments. The lower soil moisture thresholds for irrigation at different stages from tillering to soft dough stage are listed in Table 1. The thresholds for W1 treatment are used to practice CI irrigation in China (Peng et al., 2013). These treatments replicated two times in 8 pots buried in the soil with 10 cm above the ground, and were located under a movable rainout shelter. At each side outside the pot, there were three rows of rice to avoid the edge effect.

When the soil moisture of any treatment approached the lower thresholds (measured daily at 8:00), the same amount of irrigation water, determined based on the soil moisture deficit to saturation in W1 treatment, was applied to each pot.

Field Measurements

Soil moisture contents in each pot were measured daily at 8:00 using a time domain reflectometer (TDR, soil moisture, USA) and with 20 cm waveguides installed at 0-20, 20-40, and 40-60 cm depths. Daily meteorological data including precipitation volume, wind speed, temperature, solar radiation, and relative humidity, were recorded by an automatic weather station (ICT, Australia) every 30 minutes.

Irrigation water volume was measured by a 500 mL plastic graduated cylinder (accuracy, 5 mL). After rice harvesting, yield was determined for each pot.

By using an LC-pro+ photosynthetic system (ADC, UK), the Pn, Tr and gs of the last one or second full expanded rice leaf was measured simultaneously at regular interval of 4-5 days on sunny day. The measurement included diurnal variation measured at 8:00, 10:00, 12:00, 14:00, 16:00, 18:00 and routine measurement at 14:00. To avoid errors caused by indoor-outdoor air temperature difference, the LC-pro+ photosynthetic system was put in an outdoor environment for 3-5 min in 0

The crop water stress index (CWSI) developed by Idso et al. (1981), was defined as:

(Equation)

Where TL is the canopy leaf temperature (C), it was calculated by averaging temperatures derived from six different sun-facing rectangular leaf areas (1 cm 1 cm) through analyzing thermal imager in Therma CAM Researcher Pro 2.8 software (FLIR system, USA); Twet is the average temperature of the wet reference that act as the substitute of the well-watered base line temperature; and Tdry is the upper boundary for canopy temperature, which equates to the temperature of a non-transpiring leaf with stomata completely closed, estimated by adding 5C to the air, Tdry = Tair + 5C (Irmak et al., 2000).

Atmospheric Vapor Pressure Deficit (VPD)

The atmospheric VPD (kPa) (Banerjee et al., 2012) was calculated with the temperature (T, C) and relative humidity (RH, %) measured by the automatic weather station at the same time of the image capturing.

(Equation)

Results

Rice Yields under Different Water Treatments

With the decrease in soil moisture thresholds in different water treatments, the total panicle numbers and rice yield decreased. The rice yields in W3 treatment was significantly lower than in W1 treatment, and yield in W4 treatment was significantly lower than in W1 and W2 treatment. But the number of kernels per panicle and thousand kernel weight were not affected by water status. The soil moisture thresholds for W1 treatment were adopted from the CI irrigation in China (Peng et al., 2013). It indicated that thresholds lower than these used in CI irrigation resulted in rice yield loss, and the yield reduction was mostly attributed in the decrease in panicle numbers.

Diurnal Pattern of CWSI under Different Soil Water

Deficit Conditions

The CWSI of rice from different treatments varied diurnally for clear weather in the same pattern (Fig. 1). It reached the maximum at 12:00 or 14:00 and got the minimum in the morning or evening. Crop water deficit was defined as the gap between the root absorption and crop transpiration. The diurnal pattern of CWSI was likely dominated by the air evaporative demand. In the morning or evening, the air temperature and air evaporation ability were weak, the water absorbed by plants from soil was sufficient for plant physiological activities, rice did not suffer water deficit and the CWSI was low. At noon, crop transpiration rate was higher than root water absorption rate due to the high solar radiation and air temperature. As a result, crop suffered from water deficit to a certain degree, and the CWSI was high. Generally, low soil water contents led to high CWSI values, especially during noon.

The peak CWSI values in W1, W2, W3 and W4 treatments increased in sequence with increasing soil water deficit degree. It can be concluded that CWSI value increased with increase in water stress degree and the daily most severe water stress occurred at noon (12:00 to 14:00).

Crop Water Stress Index (CWSI) in Relation with Soil

Moisture Depletion

CWSI at 14:00 decreased in the range of 0.29-0.58 (Fig. 2) and increased with the reduction in th/ths under different soil moisture deficit treatments. It got a periodic peak just prior to irrigation when the soil moisture approaching the lower thresholds in different treatments, and dropped in a short time after irrigation before it increased gradually again as soil moisture depleted. Comparison between different treatments indicated the lower th/ths always accompanied with higher CWSI value.

Effect of CWSI on Physiological Indexes

Crop water deficit observed at noon (Fig. 1), and the relationships between CWSI and leaf physiological indexes at noon (12:00 and 14:00) were plotted in Fig. 3. When the CWSI was less than 0.4, water stress was light, Pn, Tr and gs varied at a high level. At CWSI higher than 0.5, Pn, Tr and gs decreased gradually (Fig. 3).

It indicated that Pn, Tr and gs were generally high when CWSI was low, and reduced with increase in CWSI. When CWSI was small, the water absorbed by plants from the soil was sufficient for plant transpiration, and rice leaf maintained a high Tr and gs, as a result the Pn was high. When CWSI was high, the water absorbed by plants from the soil was not enough for plant transpiration and water deficit led to partial closure of stomata to restrain transpiration water loss.

Tr and gs reduced, and consequently resulted in reduction of Pn. The relationships between CWSI and Pn, Tr or gs could be described by quadratic polynomial equations, which were significant at pless than 0.05 confidence level.

Critical CWSI values for the decline trend in Pn, Tr and gs were determined by analyzing the vertex point of the polynomial equations. The critical CWSI values for decline in Tr were 0.273, 0.319 and 0.241 at tillering, panicle initiation to booting and milk to soft dough stages. These values were very close to the critical CWSI values for decline in gs (0.269, 0.286 and 0.302), but lower than the critical CWSI values for decline in Pn (0.303, 0.385 and 0.446). Maintaining CWSI larger than the critical points for Tr decline, but lower than the critical points for Pn decline was an ideal range for high water use efficiency at leaf scale.

Assuming a 5% reduction in Pn from maximum was the critical point of a moderate water stress, the critical CWSI values were estimated as 0.420, 0.472 and 0.536 at tillering, panicle initiation to booting and milk to soft dough stages respectively.

Table 1: Lower soil moisture thresholds at different stages for different treatments

Treatment###Tillering###Panicle initiation to booting###Heading to anthesis###Milk to soft dough stage

###Early###Middle###Late###Early###Late

Period duration###7.7~7.14###7.15~7.28###7.29~8.4###8.5~8.15###8.16~9.1###9.2~9.13###9.14-9.29

W1###70%s1###65%s1###60%s1###70%s2###75%s2###80%s3###70%s3

W2###70%s1###60%s1###55%s1###65%s2###70%s2###75%s3###70%s3

W3###70%s1###55%s1###50%s1###60%s2###65%s2###70%s3###70%s3

W4###70%s1###50%s1###45%s1###55%s2###60%s2###65%s3###70%s3

Critical Relative Soil Moisture Contents

There were significant negative correlations between CWSI at 14:00 and relative soil moisture contents (th/ths) at different stages (Fig. 4). At tillering, panicle initiation to booting and milk to soft dough stages, data were divided into two subsets with high and low VPD conditions. Slight differences were found between the correlations based on data measured under different VPD conditions. The linear slopes at higher VPD condition were 0.8372, 0.7353 and 1.0633 at tillering, panicle initiation to booting and milk to soft dough stages, higher than the corresponding slopes (0.7417, 0.6860 and 0.9211) at lower VPD conditions.

Thresholds of relative soil moisture content were determined based on the linear relation between CWSI and th/ths, and the critical CWSI values either for decline in Pn or a 5% reduction in Pn from maximum. The critical values of th/ths for decline in Pn were determined as 66.85%, 77.57% and 74.30% under high VPD condition at tillering, panicle initiation to booting and milk to soft dough stages, almost the same as the critical th/ths values under low VPD condition. But for 5% reduction in Pn from maximum, the critical values of th/ths were determined as 52.88%, 65.74% and 65.83% under high VPD condition, values were higher than critical th/ths values under low VPD condition (Table 3).

The thresholds of th/ths used in the practice of CI irrigation were 60%, 70% and 70% at tillering, panicle initiation to booting and milk to soft dough stages in China (Peng et al., 2013), these values almost equaled to the mean values of the critical th/ths values for decline in Pn and for a 5% reduction in Pn from maximum. This indicated the soil moisture condition in traditional CI paddies did not always suffered from water deficit, while thresholds lower than these used in CI irrigation led to higher CWSI (Fig. 1 and 2) and resulted in rice yield loss (Table 2).

Discussion

The diurnal variation pattern of CWSI indicated that the daily most severe water stress occurred at noon (12:00 to 14:00) in different water deficit treatments. This was consistent with some previous studies (Zia et al., 2012; Agam et al., 2013; Li et al., 2014). Thus, water deficit diagnosis should be conducted at noon. The relationships between CWSI and leaf physiological indexes (Pn, Tr or gs) at noon could be described by quadratic polynomial equations. The quadratic polynomial equation between CWSI and gs was the same with the results got by Aladenol and Madramootoo (2014) on bell pepper, but was different from the negatively linear relationship between CWSI and gs reported by Moller et al. (2007) or Zia et al. (2011). It might be because the data were collected on different sampling data, that might result in the difference in relations between CWSI and gs among sampling date as reported by Rud et al. (2014) on potato.

Table 2: Rice yields and yield components for different treatments

Treatment###Total panicle numbers (104 ha-1)###kernel numbers (per panicle)###Thousand kernel weight (g###Yield (kg ha-1)

W1###302.40 a###86.91 a###27.04 a###6439.01 a

W2###290.77 a###86.32 a###27.68a###6338.47 ab

W3###267.51 a###85.13 a###27.57 a###5815.34 bc

W4###255.88 a###87.94 a###26.83 a###5534.89 c

Based on the quadratic polynomial relationships between CWSI and leaf physiological indexes, the critical CWSI values for decline in Pn were higher than those for decline in Tr or gs. It also implied that stomatal limitation caused by stomata closure exerted larger reduction in Tr than in Pn (Mullet and Whitsitt, 1997; Ierna and Mauromicale, 2006). Keeping the CWSI between the critical points for Tr decline and for Pn decline was an ideal for higher leaf water use efficiency.

The significant negative correlations between CWSI at 14:00 and relative soil moisture contents (th/ths) were same with the results reported in previous studies (Wang et al., 2005; Paltineanu et al., 2009; Paltineanu et al., 2012; Cao et al., 2013). In present study, we also found slight differences between the correlations based on data measured under different VPD conditions. The critical th/ths values for a 5% reduction in Pn, the differences between high and low VPD conditions were 1.57%, 1.18% and 1.27%. These differences were very small, and ascribed to the relative narrow VPD ranges (2.20-2.96 kPa, 1.03-1.40 kPa, 1.30- 1.38 kPa at tillering, panicle initiation to booting and milk to soft dough stages) in present study, due to the high air humidity in humid region of East China. If the VPD ranges were larger, the difference in critical th/ths values between high and low VPD conditions might be more obvious.

It implied that crop water status was determined conjunctively by field soil moisture content and atmospheric conditions, and the plant might suffer a higher stress under the same soil moisture condition when the VPD is higher (El-Sharkawy, 2006; Padhi, et al., 2012; Schoppach and Sadok, 2012; Belko et al., 2013). Naithani et al. (2012) argued that a combination of atmospheric and surface soil drought controlled leaf transpiration rate, whereas stomatal conductance was mainly driven by atmospheric drought.

Table 3: Regressions between th/ths and CWSI and the critical values of th/ths at different stages under different VPD conditions

Growth period###VPD###Linear fitting equation###R2###CWSICI*###Q/Qs %###CWSICII*###Q/Qs %

Tillering###less than2.5kPa CWSI=-0.7417/s+0.8006###0.941###0.303###67.07###0.420###51.31

###>=2.5kPa###CWSI=-0.8372/s+0.8627###0.945###66.85###52.88

Panicle initiation to booting less than1.3kPa CWSI=-0.6860/s+0.9149###0.792###0.385###77.24###0.472###64.56

###>=1.3kPa###CWSI=-0.7353/s+0.9554###0.659###77.57###65.74

Milk to soft dough stage###less than1.3kPa CSWI=-0.9211/s+1.1307###0.815###0.446###74.34###0.536###64.56

###>=1.3kPa###CSWI=-1.0633/s+1.2360###0.962###74.30###65.83

Conaty et al. (2014) showed adjusting the critical canopy temperature by utilizing its strong associations to leaf water potential and VPD could improve the precision of irrigation in canopy temperature based irrigation scheduling protocols. Thus, when the rice WSI irrigation techniques were applied in arid regions with high VPD values, the ideal water deficit diagnosis should be done by incorporating the soil moisture condition with air VPD condition, and the critical relative soil moisture thresholds determined in humid region might not perform well in arid region. From this point of view, the water deficit diagnosis based on canopy temperature tracked by high precision thermal infrared imagers is a promising method in reflecting the conjunction function of soil moisture deficit and air aridity on crop water status.

Conclusion

The CWSI of rice under different water treatments, calculated based on the canopy temperature derived from infrared thermal images, varied in the same diurnal pattern with peak values at noon. Soil water deficit led to high CWSI values, especially at noon. Pn, Tr and gs reduced generally with increase in CWSI. Critical noon CWSI values for the decline trend in Pn were higher than those for decline in Tr and gs. Slight differences were found between the linear relations of CWSI at 14:00 and relative soil moisture contents under high or low VPD conditions, and the critical th/ths for a 5% reduction in Pn that was assuming as moderate water stress in Pn were slightly higher under high VPD than low VPD conditions.

It implied that rice water status was determined conjunctively by field soil moisture content and air aridity, the water deficit diagnosis based on canopy temperature tracked by thermal infrared imager is a promising method in reflecting the conjunction function of soil moisture deficit and air aridity on crop water status.

Acknowledgement

The research was financially supported by the Fundamental Research Funds for the Central Universities (No. 2012B07514), the Innovative Young Scholar Project of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (No. 20145027912), the Priority Academic Program Development of Jiangsu Higher Education Institutions, and Advanced Science and Technology Innovation Team in Colleges and Universities in Jiangsu Province.

References

Abbasi, M. and A. Sepaskhah, 2011. Response of different rice cultivars (Oryza sativa L.) to water-saving irrigation in greenhouse conditions. Int. J. Plant Prod., 5: 37-48

Agam, N., Y. Cohen, J.A.J. Berni, V. Alchanatis, D. Kool, A. Dag, U. Yermiyahu and A. Ben-Gal, 2013. An insight to the performance of crop water stress index for olive trees. Agric. Water Manage., 118: 79-86

Aladenola, O. and C. Madramootoo, 2014. Response of greenhouse-grown bell pepper (Capsicum annuum L.) to variable irrigation. Can. J. Plant Sci., 94: 303-310

Anda, A., 2009. Irrigation timing in maize by using the crop water stress index (CWSI). Cereal Res. Commun., 37: 603-610

Banerjee, A., A. de Fortier Smit and J.A. Prozzi, 2012. Modeling the effect of environmental factors on evaporative water loss in asphalt emulsions for chip seal applications. Constr. Build. Mater., 27: 158-164

Silva, B. and T.V. R. Rao, 2005. The CWSI variations of a cotton crop in a semi-arid region of Northeast Brazil. J. Arid Environ., 62: 649-659

Belder, P., B.A.M. Bouman, R. Cabangon, L. Guoan, E.J.P. Quilang, L. Yuanhua, J.H.J. Spiertz and T.P. Tuong, 2004. Effect of water-saving irrigation on rice yield and water use in typical lowland conditions in Asia. Agric. Water Manage., 65: 193-210

Belko, N., M. Zaman-Allah, N. Diop, N. Cisse, G. Zombre, J. Ehlers and V. Vadez, 2013. Restriction of transpiration rate under high vapour pressure deficit and non-limiting water conditions is important for terminal drought tolerance in cowpea. Plant Biol., 15: 304-316

Berni, J.A.J., P.J. Zarco-Tejada, G. Sepulcre-Canto, E. Fereres and F. Villalobos, 2009. Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sens. Environ., 113: 2380-2388

Cao, M.C., W.Z. Zhang, Y.D. Han, C. Yao, Y.T. Wang and G.H. Ding, 2013. A theoretical model research of rice water stress index based on automated infrared thermal imaging. Adv. Mater. Res., 712: 433-438

Conaty, W. C., J.R. Mahan, J.E. Neilsen and G.A. Constable, 2014. Vapour pressure deficit aids the interpretation of cotton canopy temperature response to water deficit. Funct. Plant Biol., 41: 535-546

Clawson, K.L. and B.L. Blad, 1982. Infrared thermometry for scheduling irrigation of corn. Agron. J., 74: 311-316

Cohen, Y., V. Alchanatis, M. Meron, Y. Saranga and J. Tsipris, 2005. Estimation of leaf water potential by thermal imagery and spatial analysis. J. Exp. Bot., 56: 1843-1852

Cremona, M.V., H. Stutzel and H. Kage, 2004. Irrigation scheduling of kohlrabi (Brassica oleracea var. gongylodes) using crop water stress index. Hortic. Sci., 39: 276-279

El-Sharkawy, M.A., 2006. International research on cassava photosynthesis, productivity, eco-physiology, and responses to environmental stresses in the tropics. Photosynthetica, 44: 481-512

Erdem, Y., T. Erdem, A.H. ORTA and H. Okursoy, 2006a. Irrigation scheduling for watermelon with crop water stress index (CWSI). J. Cent. Eur. Agric., 6: 449-460

Erdem, Y., S. Sehirali, T. Erdem and D. Kenar, 2006b. Determination of crop water stress index for irrigation scheduling of bean (Phaseolus vulgaris L.). Turk. J. Agric. For., 30: 195-202

Erdem, Y., L. Arin, T. Erdem, S. Polat, M. Deveci, H. Okursoy and H.T. Gultas, 2010. Crop water stress index for assessing irrigation scheduling of drip irrigated broccoli (Brassica oleracea L. var. italica). Agric. Water Manage., 98: 148-156

Gontia, N. and K. Tiwari, 2008. Development of crop water stress index of wheat crop for scheduling irrigation using infrared thermometry. Agric. Water Manage., 95: 1144-1152

Gonzalez-Dugo, M., M. Moran, L. Mateos and R. Bryant, 2006. Canopy temperature variability as an indicator of crop water stress severity. Irrig. Sci., 24: 233-240

Idso, S.B., R.D. Jackson and R.J. Reginato, 1977. Remote sensing for agricultural water management and crop yield prediction. Agric. Water Manage., 1: 299-310

Idso, S., R. Jackson, P. Pinter Jr, R. Reginato and J. Hatfield, 1981. Normalizing the stress-degree-day parameter for environmental variability. Agric. Meteorol., 24: 45-55

Ierna, A. and G. Mauromicale, 2006. Physiological and growth response to moderate water deficit of off-season potatoes in a Mediterranean environment. Agric. Water Manage., 82: 193-209

Irmak, S., D.Z. Haman and R. Bastug, 2000. Determination of crop water stress index for irrigation timing and yield estimation of corn. Agron. J., 92: 1221-1227

Jackson, R., R. Reginato and S. Idso, 1977. Wheat canopy temperature: a practical tool for evaluating water requirements. Water Res., 13: 651-656

Jackson, R.D., S. Idso, R. Reginato and P. Pinter, 1981. Canopy temperature as a crop water stress indicator. Water Res., 17: 1133-1138

Jones, H.G., A.N. Lakso and J. Syvertsen, 1985. Physiological control of water status in temperate and subtropical fruit trees. Hortic. Rev., 7: 301-344

Jones, H.G., 2004. Irrigation scheduling: advantages and pitfalls of plant-based methods. J. Exp. Bot., 55: 2427-2436

Jones, H.G., R. Serraj, B.R. Loveys, L. Xiong, A. Wheaton and A.H. Price, 2009. Thermal infrared imaging of crop canopies for the remote diagnosis and quantification of plant responses to water stress in the field. Func. Plant Biol., 36: 978-989

Kato, Y., A. Henry, D. Fujita, K. Katsura, N. Kobayashi and R. Serraj, 2011. Physiological characterization of introgression lines derived from an indica rice cultivar, IR64, adapted to drought and water-saving irrigation. Field Crops Res., 123: 130-138

Li, B., T. Wang and J. Sun, 2014. Crop water stress index for off-season greenhouse green peppers in Liaoning, China. Int. J. Agric. Biol. Eng., 7: 28-35

Li, L., D. Nielsen, Q. Yu, L. Ma and L. Ahuja, 2010. Evaluating the crop water stress index and its correlation with latent heat and CO2 fluxes over winter wheat and maize in the North China plain. Agric. Water Manag., 97: 1146-1155

Mao, Z., 2002. Water saving irrigation for rice and its effect on environment. Eng. Sci., 4: 8-16

Moller, M., V. Alchanatis, Y. Cohen, M. Meron, J. Tsipris, A. Naor, V. Ostrovsky, M. Sprintsin and S. Cohen, 2007. Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. J. Exp. Bot., 58: 827-838

Mullet, J.E. and M.S. Whitsitt, 1997. Plant Cellular Responses to Water Deficit, Drought Tolerance in Higher Plants: Genetical, Physiological and Molecular Biological Analysis, pp: 41-46. Springer, The Netherlands

Naithani, K.J., B.E. Ewers and E. Pendall, 2012. Sap flux-scaled transpiration and stomatal conductance response to soil and atmospheric drought in a semi-arid sagebrush ecosystem. J. Hydrol., 464/465: 176-185

Narasimhan, B. and R. Srinivasan, 2005. Development and evaluation of soil moisture deficit index (SMDI) and evapotranspiration deficit index (ETDI) for agricultural drought monitoring. Agric. For. Meteorol., 133: 69-88

O'shaughnessy, S., S. Evett, P. Colaizzi and T. Howell, 2011. Using radiation thermography and thermometry to evaluate crop water stress in soybean and cotton. Agric. Water Manage., 98: 1523-1535

Padhi, J., R.K. Misra and J.O. Payero, 2012. Estimation of soil water deficit in an irrigated cotton field with infrared thermography. Field Crops Res., 126: 45-55

Paltineanu, C., E. Chitu and N. Tanasescu, 2009. Correlation between the crop water stress index and soil moisture content for apple in a loamy soil: A case study in southern Romania. VI Int. Symp. Irri. Hort. Crops 889, pp: 257-264

Paltineanu, C., L. Septar and C. Moale, 2012. Crop water stress in peach orchards and relationships with soil moisture content in a Chernozem of Dobrogea. J. Irrig. Drain. Eng., 139: 20-25

Patil, S., M. Jadhav and J. Jadhav, 2014. Impact of sowing windows and varieties on canopy temperature (CT), stress degree days (SDD) in soybean. Int. J. Plant Sci., 9: 342-348

Peng, S., S. Yang, J. Xu and H. Gao, 2011. Field experiments on greenhouse gas emissions and nitrogen and phosphorus losses from rice paddy with efficient irrigation and drainage management. Sci. China Technol. Sci., 54: 1581-1587

Peng, S., H. Hou, J. Xu, S. Yang and Z. Mao, 2013. Lasting effects of controlled irrigation during rice-growing season on nitrous oxide emissions from winter wheat croplands in Southeast China. Paddy Water Environ., 11: 583-591

Romano, G., S. Zia, W. Spreer, C. Sanchez, J. Cairns, J.L. Araus and J. Muller, 2011. Use of thermography for high throughput phenotyping of tropical maize adaptation in water stress. Comput. Electron. Agric., 79: 67-74

Rud, R., Y. Cohen, V. Alchanatis, A. Levi, R. Brikman, C. Shenderey, B. Heuer, T. Markovitch, Z. Dar, C. Rosen, D. Mulla and T. Nigon, 2014. Crop water stress index derived from multi-year ground and aerial thermal images as an indicator of potato water status. Precis. Agric., 15: 273-289

Schoppach, R. and W. Sadok, 2012. Differential sensitivities of transpiration to evaporative demand and soil water deficit among wheat elite cultivars indicate different strategies for drought tolerance. Environ. Exp. Bot., 84: 1-10

Silva, M.d.A., J.L. Jifon, J.A. Da Silva and V. Sharma, 2007. Use of physiological parameters as fast tools to screen for drought tolerance in sugarcane. Braz. J. Plant Physiol., 19: 193-201

Simsek, M., T. Tonkaz, M. Kacira, N. Comlekcioziu and Z. Dozan, 2005. The effects of different irrigation regimes on cucumber (Cucumbis sativus L.) yield and yield characteristics under open field conditions. Agric. Water Manage., 73: 173-191

Taghvaeian, S., J.L. Chavez and N.C. Hansen, 2012. Infrared thermometry to estimate crop water stress index and water use of irrigated maize in Northeastern Colorado. Remote Sens., 4: 3619-3637

Turner, N.C., 1986. Crop water deficits: a decade of progress. Adv. Agron., 39: 1-51

Uphoff, N., A. Kassam and R. Harwood, 2010. SRI as a methodology for raising crop and water productivity: productive adaptations in rice agronomy and irrigation water management. Paddy Water Environ., 9: 3-11

Wang, L., G.Y. Qiu, X. Zhang and S. Chen, 2005. Application of a new method to evaluate crop water stress index. Irrig. Sci., 24: 49-54

Wang, X., W. Yang, A. Wheaton, N. Cooley and B. Moran, 2010. Automated canopy temperature estimation via infrared thermography: A first step towards automated plant water stress monitoring. Comput. Electron. Agric., 73: 74-83

Yatapanage, K.G. and H.B. So, 2001. The relationship between leaf water potential and stem diameter in sorghum. Agron. J., 93: 1341-1343

Yuan, G., Y. Luo, X. Sun and D. Tang, 2004. Evaluation of a crop water stress index for detecting water stress in winter wheat in the North China Plain. Agric. Water Manage., 64: 29-40

Zia, S., K. Spohrer, W.Y. Du, W. Spreer, G. Romano, X.K He and J. Muller, 2011. Monitoring physiological responses to water stress in two maize varieties by infrared thermography. Int. J. Agric. Biol. Eng., 4: 7-15

Zia, S., W. Du, W. Spreer, K. Spohrer, X. He and J. Muller, 2012. Assessing crop water stress of winter wheat by thermography under different irrigation regimes in North ChinaPlain. Int. J. Agric. Biol. Eng., 5: 24-34
COPYRIGHT 2016 Asianet-Pakistan
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2016 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Publication:International Journal of Agriculture and Biology
Article Type:Report
Date:Jun 30, 2016
Words:6142
Previous Article:Biocontrol and Salinity Tolerance Potential of Azospirillum lipoferum and its Inoculation Effect in Wheat Crop.
Next Article:Cross Adaptation Tolerance in Rice Seedlings Exposed to PEG Induced Salinity and Drought Stress.
Topics:

Terms of use | Privacy policy | Copyright © 2020 Farlex, Inc. | Feedback | For webmasters