Characterization of water stress and prediction of yield of wheat using spectral indices under varied water and nitrogen management practices

Bandyopadhyay, K. K.; Pradhan, Sanatan; Sahoo, Rabi Narayan; Singh, Ravender; Gupta, Vinod Kumar; Joshi, Deeksha; Sutradhar, Apurba K.

doi:10.1016/j.agwat.2014.07.017

Cited by 47 publications

(24 citation statements)

References 25 publications

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“…The passive sensor includes spectral information on the whole plant, which could, due to increasing senescence, negatively influence the relationship with RLWC. Although the study of Bandyopadhyay et al (2014) showed low correlations between RLWC and NWI-1 to 4, the five NIR-based water indices showed significant relationships with RLWC, which were on the same level as those observed for the active sensors. Moreover, the water indices exhibited highly significant relationships with LT and leaf and grain CID.…”

Section: Discussionmentioning

confidence: 64%

Evaluation of Yield and Drought Using Active and Passive Spectral Sensing Systems at the Reproductive Stage in Wheat

Becker

Schmidhalter

2017

Front. Plant Sci.

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Active and passive sensors are available for ground-based, high-throughput phenotyping in the field. However, these sensor systems have seldom been compared with respect to their determination of plant water status and water use efficiency related parameters under drought conditions. In this study, five passive and active reflectance sensors, including a hyperspectral passive sensor, an active flash sensor (AFS), the Crop Circle, and the GreenSeeker, were evaluated to assess drought-related destructive and non-destructive morphophysiological parameters (ground cover, relative leaf water content, leaf temperature, and carbon isotope discrimination of leaves and grain) and grain yield of twenty wheat (Triticum aestivum L.) cultivars. Measurements were conducted in a 2-year study, including a drought stress and a control environment under field conditions. A comparison of the active sensors at the heading, anthesis and grain-filling stages indicated that the Crop Circle provided the most significant and robust relationships with drought-related parameters (relative leaf water content and leaf and grain carbon isotope discrimination). In comparison with the passive sensor, the five water and normalized water indices (WI and NWI—1 to 4), which are only provided by the passive sensor, showed the strongest relationships with the drought stress-related parameters (r = −0.49 to −0.86) and grain yield (r = −0.88) at anthesis. This paper indicates that precision phenotyping allows the integration of water indices in breeding programs to rapidly and cost-effectively identify drought-tolerant genotypes. This is supported by the fact that grain yield and the water indices showed the same heritability under drought conditions.

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Section: Discussionmentioning

confidence: 64%

Evaluation of Yield and Drought Using Active and Passive Spectral Sensing Systems at the Reproductive Stage in Wheat

Becker

Schmidhalter

2017

Front. Plant Sci.

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“…(2011) explained that changes in spectral features are caused by an increase in magnitude with wavelength. However, there has been evidence of changes in overall spectral reflectance due to water stress (Carter, 1991; Aldakheel & Danson, 1997; Pradhan et al ., 2013; Bandopadhyay et al ., 2014). Lately, Panigrahi & Das (2018) have also studied water stress in rice using different water indices in the NIR and SWIR regions.…”

Section: Introductionmentioning

confidence: 99%

Canopy spectral reflectance for crop water stress assessment in wheat (Triticum aestivum, L.)*

Chandel

Rajwade

Golhani

et al. 2020

Irrigation and Drainage

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Spectral reflectance in the near‐infrared (NIR) and short‐wave–infrared (SWIR) regions shows decreasing water absorption troughs upon crop water stress. In this study, decreasing water absorption troughs were identified from reflectance of irrigated wheat crop. The crop was grown under two different irrigation methods, sprinkler and flood irrigation. Crop reflectance was measured using an ASD FieldSpec 3 (350–2,500 nm) hand‐held spectroradiometer at 1 week before the critical growth stages. Among different growth stages, wheat crop was found to be under stress in crown root initiation, jointing, and flowering. Results based on crop yield, and raw and first derivative spectral analysis indicated that flood‐irrigated wheat had more stress compared with sprinkler‐irrigated wheat. Five water stress‐sensitive wavelengths (974; 1,195; 1,455; 1,791; and 1,935 nm) were identified from flood‐irrigated wheat. Among them, two wavelengths (974 and 1,195 nm) were found to be highly water sensitive and good indicators for water stress. Detecting crop water stress prior to the critical growth stages of wheat helps in proper irrigation scheduling.

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“…Earlier, Hatfield et al (1987) found that cotton germplasm could be screened using canopy temperatures as a method of quantifying water conservation among lines. Bandyopadhyay et al (2014) found water and N stresses in wheat could be quantified using remotely sensed spectral indices and proposed a normalized water stress index. The advantage of this method was that grain yield could be accurately predicted at the milk stage of wheat, providing a forecast of the potential drought effects (Bandyopadhyay et al, 2014).…”

Section: Closing the Yield Gap: Genetics × Environment × Managementmentioning

confidence: 99%

“…Bandyopadhyay et al (2014) found water and N stresses in wheat could be quantified using remotely sensed spectral indices and proposed a normalized water stress index. The advantage of this method was that grain yield could be accurately predicted at the milk stage of wheat, providing a forecast of the potential drought effects (Bandyopadhyay et al, 2014). Further refinement of reflectance and thermal indices may advance our ability to screen germplasm for their response to water stresses.…”

Section: Closing the Yield Gap: Genetics × Environment × Managementmentioning

confidence: 99%

Meeting Global Food Needs: Realizing the Potential via Genetics × Environment × Management Interactions

2015

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Global food needs are projected to double by 2050 to feed the 9 billion people and the challenge presented to agriculture is whether this is feasible. Th ese goals will be faced with an increasing variability in climate and more extremes in temperature and precipitation in all parts of the world and a decreasing land resource base in extent and quality. Th ere are many challenges to be faced; however, focusing on the interactions of genetics × environment × management (G × E × M) off ers the potential to feed the 9 billion. Understanding and quantifying yield gaps off er a framework to assess the progress, and the challenge will be to determine the most eff ective and effi cient way of closing the yield gap by using water and nutrients more effi ciently. Th e more feasible approach of increasing potential will be to increase the actual yields rather than increasing potential yield. Actual yield increases and overall productivity can come from management systems focused on increasing land productivity because our ability to expand the available land resources are not a viable option. Development of methods of screening genotypes for a variety of responses to combinations of environmental and management scenarios off ers the potential pathway to developing a robust structure for G × E × M. We can meet this challenge; however, the paradigm of how we currently conduct research will not be rapid enough and we need to develop the transdisciplinary teams to represent each component of the G × E × M interaction.

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Characterization of water stress and prediction of yield of wheat using spectral indices under varied water and nitrogen management practices

Cited by 47 publications

References 25 publications

Evaluation of Yield and Drought Using Active and Passive Spectral Sensing Systems at the Reproductive Stage in Wheat

Evaluation of Yield and Drought Using Active and Passive Spectral Sensing Systems at the Reproductive Stage in Wheat

Canopy spectral reflectance for crop water stress assessment in wheat (Triticum aestivum, L.)*

Meeting Global Food Needs: Realizing the Potential via Genetics × Environment × Management Interactions

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