Pecan is a large deciduous tree that is cultivated primarily for its nuts. With an annual production of 139 million kilograms, the United States is the world’s largest producer of pecan. The total area of pecan orchards in the United States is ≈236,000 ha; New Mexico pecan cultivation accounts for ≈7% of that area. In 2012, New Mexico produced 31.3 million kilograms (in-shell basis) of pecan, ≈23% of total U.S. production (U.S. Department of Agriculture, 2012).
New Mexico has an arid to semiarid climate. Much of the pecan cultivation occurs in riparian areas, especially along the Rio Grande River, where water can be diverted for irrigation. However, this supply of surface water often is limited. This means that farmers also must pump groundwater to supplement irrigation, which makes pecan vulnerable to water deficits. Low soil moisture negatively affects several physiological processes in pecan trees, such as photosynthesis (A) and gas exchange (Othman et al., 2014a). Water deficit reduced pecan yield 5% to 24% when the applied water was reduced from 5% to 52% relative to control (Garrot et al., 1993).
For the pecan farmer, irrigation must be scheduled to maximize pecan growth and nut production while minimizing costs associated with water appropriation and application. Effective irrigation schedules rely on irrigation application only when an indicator variable reaches a threshold value (Cifre et al., 2005). This indicator variable must be sufficiently sensitive to water status so that the threshold at which irrigation starts can be determined with some precision (Jones, 2004). Midday stem water potential has been proposed for detecting moisture status and monitoring irrigation in commercial orchards, including pecan (Jones, 2004; Othman et al., 2014a). However, using ψsmd for irrigation scheduling, especially, on a large scale is labor intensive (therefore, expensive), slow, and unsuitable for automation (Jones, 2004).
Remote sensing applications hold potential for predicting plant water status, growth, and development (Othman et al., 2014a; Rossi et al., 2010). Hyperspectral sensors measure reflectance in a narrow wavelength range (usually 10 nm or less) and contain hundreds of contiguous bands over the electromagnetic spectrum that can be used to estimate the biochemical properties of vegetation (Huber et al., 2008). There has been considerable success in relating hyperspectral reflectance indices to plant physiological properties. For example, the water band index has been shown to be related to surface-atmosphere fluxes of CO2 and H2O (Claudio et al., 2006). Hyperspectral reflectance within the 705- to 750-nm spectral range successfully detects water deficit in apple (Malus domestica) trees (Kim et al., 2011), and holds promise for doing so in pecan. In grape (Vitis vinifera), the reflectance-based water index effectively tracked variation in leaf stomatal conductance (R2 = 0.81) at a predawn leaf water potential of −0.42 MPa (Serrano et al., 2010). Moisture stress index and vegetation moisture index which incorporate the 850- and 1928-nm spectral bands showed significant strong correlations with equivalent water thickness in 21 Eucalyptus sp. subjected to deficit irrigation (Datt, 1999). Sims and Gamon (2003) concluded that the 1150- to 1260-nm and 1520- to 1540-nm wavelength regions can penetrate more deeply into canopies and may be used to accurately detect tree water status. Although the 1944-nm band yielded the best correlation with available soil water, this band is not recommended for practical use because its location in a strong water vapor absorption area makes measurements from space difficult (Weidong et al., 2003). In olive trees (Olea europaea), PRI derived from airborne hyperspectral scanner sensor was sensitive to water stress indicators, such as stomatal conductance and ψsmd (Suárez et al., 2008). However, leaf orientation and soil background significantly affected PRI derived from airborne sensor data leading Suárez et al. (2008) to conclude that canopy structure must be considered when PRI is used.
In a previous study, we screened several leaf-level physiological measurements to determine which of these leaf-level parameters best represented changes in plant moisture status (Othman et al., 2014a). We concluded that ψsmd was the best performing physiological indicator for detecting moisture status in pecan trees (Othman et al., 2014a). We also found that ψsmd of −0.9 to −1.5 MPa was the critical water status range to prevent significant reduction in A and gas exchange (>50%) in pecan (Othman et al., 2014b). It is not known whether vegetation indices derived from advanced sensing technologies can precisely predict water status within this range of ψsmd (−0.9 to −1.5). The objective of this study was to investigate whether hyperspectral remotely sensed data derived from a handheld spectroradiometer could detect pecan low water status as estimated using ψsmd.
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