Crop Water Stress Index Research Papers (original) (raw)

Studies were conducted to determine the crop water stress index (CWSI) for irrigation scheduling of cotton crop under irrigated semiarid environment. The investigations were carried out at the Post Graduate Agriculture Research Station... more

Studies were conducted to determine the crop water stress index (CWSI) for irrigation scheduling of cotton crop under irrigated semiarid environment. The investigations were carried out at the Post Graduate Agriculture Research Station (PARS), University of Agriculture, Faisalabad (UAF), Pakistan
(latitude 31°25´N, longitude 73°09´E and altitude 184.4 m from sea level), during summer season 2006. Five treatments with different irrigation conditions were managed under randomized complete block design (RCBD). These treatments included T0 no irrigation except rainfall (NI); T1
irrigation at vegetation stage (VS-1); T2 irrigation at vegetation and flowering stage (VS-1 + FS-1); T3 irrigation at vegetation, flowering and boll formation stage (VS-1 + FS-1 + BF-1) and T4 irrigation at vegetation, flowering, boll formation and at late stage (VS-1 + FS-1 + BF-1 + LS-1). Lower and upper baselines were developed for the cotton crop. For developing upper baseline (fully water-stressed), cotton was grown as a separate
treatment where irrigation application was restricted. Lower baseline was developed by using canopy and air temperatures attained on clear sky days with 5-8 days of irrigation and rainfall application to field. The seasonal CWSI values for each irrigation treatment were calculated as the average for the entire season. The mean value of CWSI for treatment To, T1, T2, T3 and T4 were 0.76, 0.60, 0.42, 0.28 and 0.24, respectively. The trends in CWSI were consistent with moisture content in the soil. The relationship between yield and seasonal mean CWSI values was primarily linear: Y = -2320.3CWSI + 3048.5 (r2 = 0.95, r = 0.97, SE = 0.05, P<0.01). This relation can be used to predict the yield of cotton, and data thus generated will be beneficial for further research.

In this study, the relationship between water deficit index (WDI) and a number of parameters related to soil water status, crop monitoring and yield were investigated with regard to drip irrigated dwarf green beans (Phaseolus vulgaris,... more

In this study, the relationship between water deficit index (WDI) and a number of parameters related to soil water status, crop monitoring and yield were investigated with regard to drip irrigated dwarf green beans (Phaseolus vulgaris, humilis) in Ankara, Turkey during the 2004 and 2005 growing seasons. Three different WDIs were calculated based on three different spectral indexes and oblique viewed surface temperature. Soil water status was quantified by soil water content (SWC) and soil water deficit index (SWDI). Crop evapotranspiration (ETc), leaf water potential (LWP), spectral indexes and crop water stress index (CWSI) were determined. Although the WDIs have statistically significant relationships with the parameters, it is hard to use WDIs based on oblique viewed surface temperature for irrigation scheduling purposes. However, total yield estimation and monitoring of seasonal crop water use status could be achieved through this kind of WDI.

The crop water stress index (CWSI) is a valuable tool for monitoring and quantifying water stress as well as for irrigation scheduling. This study was conducted during the 1990 and 1991 growing seasons at the Colorado State University... more

The crop water stress index (CWSI) is a valuable tool for monitoring and quantifying water stress as well as for irrigation scheduling. This study was conducted during the 1990 and 1991 growing seasons at the Colorado State University Horticulture Farm near Fort Collins, CO, USA ...

Summary. In this paper we outline the ways in which thermal and spectral remote sensing can be used to diagnose and monitor effects of environmental stresses on plants. Following an introduction to the theory and practice of using thermal... more

Summary. In this paper we outline the ways in which thermal and spectral remote sensing can be used to diagnose and monitor effects of environmental stresses on plants. Following an introduction to the theory and practice of using thermal sensing to study plant water relations and stresses involving stomatal closure, the discussion is widened to cover a range of imaging

We model the impact of agricultural droughts with a new multi-parameter index (using both climatic and non-climatic parameters) and propose a new risk transfer solution for crop insurance, called Climate Cost of Cultivation (CCC). We used... more

We model the impact of agricultural droughts with a new multi-parameter index (using both climatic and non-climatic parameters) and propose a new risk transfer solution for crop insurance, called Climate Cost of Cultivation (CCC). We used 1979/80 to 2012/13 data relevant for wheat in Bihar, India to test the variation in the CCC values. The variance (risk to farmer) increased significantly in the second half of the period (two-tailed F-test, p=0.00045). We examine the efficiency of CCC by comparing it to typical index insurance (TII), and both indices to wheat yield data (2000/01 to 2012/13). The correlation of CCC index payouts with actual yield losses is improved by a factor of ~3.9 over TII results (76.0 per cent, compared with 19.6 per cent). The pure risk premium of the CCC index is lower by around 90 per cent than the premium of the TII. We also elaborate a method to quantify the premium's climate change cost component.

Rainfed crop production is the main source of food and income in Burkina Faso. Environmental conditions are characterized by low and erratic rainfall, compounded by high temperatures and radiation loads. These factors make inadequate... more

Rainfed crop production is the main source of food and income in Burkina Faso. Environmental conditions are characterized by low and erratic rainfall, compounded by high temperatures and radiation loads. These factors make inadequate water availability the major cause for low crop yields and frequent crop failures. This paper outlines the procedure used to investigate the water-limited growth environment of an improved millet cultivar in Burkina Faso. A daily time-step cropping system simulation model (CropSyst) was used to simulate the soil water budget components and millet production potential, both spatially and temporally, by coupling the model with databases of soil type, long-term weather, and crop management using a geographic information system (GIS). From the cropping model outputs, two agroclimatic indices (Aridity Index and Crop Water Stress Index) that show the water-limited growth environment of the millet crop throughout the country were quantified and mapped with the help of the GIS. This allowed the identification of agroclimatic zones, as determined by the crop water needs. Millet productivity decreased from the south to the north of the country in relation with rainfall isolines and soil types. Locations with less than 500 mm of annual rainfall are marginal for millet, particularly on planosols and arenosols. In regions with rainfall above 700 mm, moisture availability is not a major limiting factor for the 90-day millet production, especially on regosols, cambisols, acrisols and nitosols. Overall, the approach followed in this study appeared promising for quantifying the growth environment of millet as affected by soil type and weather. It could also help to provide guidelines for crop water management and analysis of the suitability of improved crop cultivars.

In this study the suitability of thermal imaging for phenotyping was investigated as part of a breeding experiment carried out by the International Maize and Wheat Improvement Centre (CIMMYT) at Tlaltizapán experimental station in Mexico.... more

In this study the suitability of thermal imaging for phenotyping was investigated as part of a breeding experiment carried out by the International Maize and Wheat Improvement Centre (CIMMYT) at Tlaltizapán experimental station in Mexico. Different subtropical maize genotypes with two replications were screened with respect to their tolerance to water stress. Thermal images of the canopy of 92 different maize genotypes were acquired on two different days in the time interval between anthesis and blister stages (grain filling 1), whereby each picture contained five plots of different genotypes and canopy temperatures calculated for each plot. Significantly, lower canopy temperatures were found in well-watered genotypes compared with water-stressed genotypes. Furthermore significant differences (p < 0.001) between genotypes under water stress were detected using thermal images. A close correlation (p < 0.01–0.001) between canopy temperature or modified Crop water stress index with NDVI and SPAD values was obtained. It may be concluded that genotypes better adapted to drought conditions exhibited lower temperatures.Thermography is a potentially promising method to accelerate the screening process and thereby enhance phenotyping for drought adaptation in maize.► Lower canopy temperatures were found in well-watered compared with water-stressed genotypes. ► Temperature differences within genotypes under water stress were detected using thermal images. ► Genotypes which better adapted to drought exhibiting lower temperatures. ► Significant correlation were obtained between canopy temperature with NDVI, SPAD and yield data.

Accurate irrigation scheduling is important to ensure maximum yield and optimal water use in irrigated cotton. This study hypothesizes that cotton water stress in relatively humid areas can be detected from crop stress indices derived... more

Accurate irrigation scheduling is important to ensure maximum yield and optimal water use in irrigated cotton. This study hypothesizes that cotton water stress in relatively humid areas can be detected from crop stress indices derived from canopy reflectance or temperature. Field experiments were conducted in the 2003 and 2004 crop seasons with three irrigation treatments and multiple cultivars to study

Cotton (Gossypium hirsutum L.) is an important cash crop of Pakistan. The study regarding determination of lower and upper base lines for crop water stress index (CWSI) for cotton was conducted during summer, 2006 at Post Agricultural... more

Cotton (Gossypium hirsutum L.) is an important cash crop of Pakistan. The study regarding determination of lower and upper base lines for crop water stress index (CWSI) for cotton was conducted during summer, 2006 at Post Agricultural Research Station, University of Agriculture, Faisalabad situated at latitude 31°25΄N, longitude 73°09΄E and altitude 184.4 m from sea level. Experiment comprised of five treatments of irrigation levels replicated thrice under randomized
complete block design. Vapor pressure deficit (VPD) was also measured for this purpose. Upper baseline was established by growing a separate treatment T5 without any irrigation or excessive rainfall. While lower baseline was established by using air and canopy temperature attained on clear sunny days within 5-8 days of irrigation and rainfall application. Effect of treatments on leaf area index (LAI) was observed and a relationship was established between yield and LAI. The
relationship between yield and seasonal mean CWSI was found to be linear. No significant differences were found in yield of treatments T3 and T4, suggesting that an extra irrigation may be saved in treatment T4 without any significant loss in yield. Water use efficiency (WUE) was found non significant among different irrigation treatments with overall mean value of 0.54 kg m-3.

Collection Research HIGHLIGHTS  Artificial neural network modeling was used to predict crop water stress index lower reference canopy temperature.  Root mean square error of predicted lower reference temperatures was <1.1°C for... more

Collection Research HIGHLIGHTS  Artificial neural network modeling was used to predict crop water stress index lower reference canopy temperature.  Root mean square error of predicted lower reference temperatures was <1.1°C for sugarbeet and Pinot noir wine grape.  Energy balance model was used to dynamically predict crop water stress index upper reference canopy temperature.  Crop water stress index for sugarbeet was well correlated with irrigation and soil water status.  Crop water stress index was well correlated with midday leaf water potential of wine grape. ABSTRACT. Normalized crop canopy temperature, termed crop water stress index (CWSI), was proposed over 40 years ago as an irrigation management tool but has experienced limited adoption in production agriculture. Development of generalized crop-specific upper and lower reference temperatures is critical for implementation of CWSI-based irrigation scheduling. The objective of this study was to develop and evaluate data-driven models for predicting the reference canopy temperatures needed to compute CWSI for sugarbeet and wine grape. Reference canopy temperatures for sugarbeet and wine grape were predicted using machine learning and regression models developed from measured canopy temperatures of sugarbeet, grown in Idaho and Wyoming, and wine grape, grown in Idaho and Oregon, over five years under full and severe deficit irrigation. Lower reference temperatures (T LL) were estimated using neural network models with Nash-Sutcliffe model efficiencies exceeding 0.88 and root mean square error less than 1.1°C. The relationship between T LL minus ambient air temperature and vapor pressure deficit was represented with a linear model that maximized the regression coefficient rather than minimized the sum of squared error. The linear models were used to estimate upper reference temperatures that were nearly double the values reported in previous studies. A daily CWSI, calculated as the average of 15 min CWSI values between 13:00 and 16:00 MDT for sugarbeet and between 13:00 and 15:00 local time for wine grape, were well correlated with irrigation events and amounts. There was a significant (p < 0.001) linear relationship between the daily CWSI and midday leaf water potential of Malbec and Syrah wine grapes, with an R 2 of 0.53. The data-driven models developed in this study to estimate reference temperatures enable automated calculation of the CWSI for effective assessment of crop water stress. However, measurements taken under conditions of wet canopy or low solar radiation should be disregarded as they can result in irrational values of the CWSI.

Nitrogen (N) is the largest agricultural input in many Australian cropping systems and applying the right amount of N in the right place at the right physiological stage is a significant challenge for wheat growers. Optimizing N uptake... more

Nitrogen (N) is the largest agricultural input in many Australian cropping systems and applying the right amount of N in the right place at the right physiological stage is a significant challenge for wheat growers. Optimizing N uptake could reduce input costs and minimize potential off-site movement. Since N uptake is dependent on soil and plant water status, ideally, N should be applied only to areas within paddocks with sufficient plant available water. To quantify N and water stress, spectral and thermal crop stress detection methods were explored using hyperspectral, multispectral and thermal remote sensing data collected at a research field site in Victoria, Australia. Wheat was grown over two seasons with two levels of water inputs (rainfall/irrigation) and either four levels (in 2004; 0, 17, 39 and 163 kg/ha) or two levels (in 2005; 0 and 39 kg/ha N) of nitrogen. The Canopy Chlorophyll Content Index (CCCI) and modified Spectral Ratio planar index (mSRpi), two indices designed to measure canopy-level N, were calculated from canopy-level hyperspectral data in 2005. They accounted for 76% and 74% of the variability of crop N status, respectively, just prior to stem elongation (Zadoks 24). The Normalised Difference Red Edge (NDRE) index and CCCI, calculated from airborne multispectral imagery, accounted for 41% and 37% of variability in crop N status, respectively. Greater scatter in the airborne data was attributable to the difference in scale of the ground and aerial measurements (i.e., small area plant samples against whole-plot means from imagery). Nevertheless, the analysis demonstrated that canopy-level theory can be transferred to airborne data, which could ultimately be of more use to growers. Thermal imagery showed that mean plot temperatures of rainfed treatments were 2.7 °C warmer than irrigated treatments (P < 0.001) at full cover. For partially vegetated fields, the two-Dimensional Crop Water Stress Index (2D CWSI) was calculated using the Vegetation Index-Temperature (VIT) trapezoid method to reduce the contribution of soil background to image temperature. Results showed rainfed plots were consistently more stressed than irrigated plots. Future work is needed to improve the ability of the CCCI and VIT methods to detect N and water stress and apply both indices simultaneously at the paddock scale to test whether N can be targeted based on water status. Use of these technologies has significant potential for maximising the spatial and temporal efficiency of N applications for wheat growers.