Comparative Performance of Spectral Reflectance Indices and Multivariate Modeling for Assessing Agronomic Parameters in Advanced Spring Wheat Lines Under Two Contrasting Irrigation Regimes

Abstract

The incorporation of nondestructive and cost-effective tools in genetic drought studies in combination with reliable indirect screening criteria that exhibit high heritability and genetic correlations will be critical for addressing the water deficit challenges of the agricultural sector under arid conditions and ensuring the success of genotype development. In this study, the proximal spectral reflectance data were exploited to assess three destructive agronomic parameters [dry weight (DW) and water content (WC) of the aboveground biomass and grain yield (GY)] in 30 recombinant F7 and F8 inbred lines (RILs) growing under full (FL) and limited (LM) irrigation regimes. The utility of different groups of spectral reflectance indices (SRIs) as an indirect assessment tool was tested based on heritability and genetic correlations. The performance of the SRIs and different models of partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) in estimating the destructive parameters was considered. Generally, all groups of SRIs, as well as different models of PLSR and SMLR, generated better estimations for destructive parameters under LM and combined FL+LM than under FL. Even though most of the SRIs exhibited a low association with destructive parameters under FL, they exhibited moderate to high genetic correlations and also had high heritability. The SRIs based on near-infrared (NIR)/visible (VIS) and NIR/NIR, especially those developed in this study, spectral band intervals extracted within VIS, red edge, and NIR spectral range, or individual effective wavelengths relevant to green, red, red edge, and middle NIR spectral region, were found to be more effective in estimating the destructive parameters under all conditions. Five models of SMLR and PLSR for each condition explained most of the variation in the three destructive parameters among genotypes. These models explained 42% to 46%, 19% to 30%, and 39% to 46% of the variation in DW, WC, and GY among genotypes under FL, 69% to 72%, 59% to 61%, and 77% to 81% under LM, and 71% to 75%, 61% to 71%, and 74% to 78% under FL+LM, respectively. Overall, these results confirmed that application of hyperspectral reflectance sensing in breeding programs is not only important for evaluating a sufficient number of genotypes in an expeditious and cost-effective manner but also could be exploited to develop indirect breeding traits that aid in accelerating the development of genotypes for application under adverse environmental conditions.

Potential of Multivariate Statistical Technique Based on the Effective Spectra Bands to Estimate the Plant Water Content of Wheat Under Different Irrigation Regimes

“…Furthermore, plant water content affects chlorophyll content and canopy size. For this reason, previous findings indicated that bands in the visible region can be used indirectly to assess plant water status (Das et al, 2017;El-Hendawy et al, 2019a).…”

Potential of Hyperspectral and Thermal Proximal Sensing for Estimating Growth Performance and Yield of Soybean Exposed to Different Drip Irrigation Regimes Under Arid Conditions

“…However, moisture stress is one of the widespread abiotic factors limiting plant growth and productivity of major crops under irrigated agriculture. Additionally, abrupt climate change, high temperature and evaporation rates, and sparse and highly variable rainfall further exacerbate the drought-stress problems in irrigated agriculture, which poses a serious threat to future global food security [ 2 , 3 , 4 ]. Consequently, maximizing production per unit of irrigation water applied by shifting the irrigation strategies of crops from the paradigm of full irrigation to deficit irrigation practices is one plausible solution for addressing water shortage in irrigated agriculture [ 5 , 6 ].…”

“…Generally, moisture stress directly affects plant water status, which can interfere with normal plant functions and can cause substantial changes in morphological and physiological traits. These effects subsequently translate into substantial changes in crop growth and development with significantly decreased leaf area, biomass accumulation, photosynthesis efficiency, stomatal conductance, transpiration rate, and chlorophyll content and ultimately significant losses in SY [ 4 , 7 , 14 , 15 , 16 ]. These reports indicate that the close association between plant water status and available soil water can be exploited to effectively manage irrigation scheduling that involves deciding how much and when irrigation water should be applied and how to simultaneously maximize yield and water use efficiency through accurate estimation and monitoring of the various morpho-physiological indicators [ 17 , 18 , 19 , 20 , 21 ].…”

“…The absorption features of leaf water content are primarily located in the NIR and SWIR regions of the spectrum, with the peaks of water absorption bands located in the SWIR regions near 1450, 1940, 2100, 2250, and 2500 nm, while the weak absorption bands are identified in the NIR region near 970 and 1200 nm. The absorption peak in the SWIR region can be attributed to the combinations and overtones of the fundamental vibrational excitation within the water molecule (O–H) [ 4 , 42 , 43 ]. However, weak absorption bands, especially at 970 nm, can be attributed to the ability of NIR wavebands to penetrate more deeply into the canopy than SWIR wavebands and, therefore, provide the ability to estimate water content in the entire canopy more accurately than SWIR [ 17 , 44 ].…”

Use of Hyperspectral Reflectance Sensing for Assessing Growth and Chlorophyll Content of Spring Wheat Grown under Simulated Saline Field Conditions

“…Until now, several types of SRI that include 2-3 wavelengths have been developed to assess the Chl content and biomass of plants. Most of these SRIs are based on specific wavelengths within the green (550 nm), red (660-670 nm), red-edge (680-750 nm), and NIR (750-870 nm) regions [24,26,[37][38][39][40][41]. These specific wavelengths have been incorporated in simple mathematical formulas to eliminate the sensitivity of these wavelengths to other factors that also influence the spectral reflectance but not the plant variables of interest [42].…”