Jeq50204 1060..1071

In arid irrigated regions, the proportion of crop production under deficit irrigation with poorer quality water is increasing as demand for fresh water soars and efforts to prevent saline water table development occur. Remote sensing technology to quantify salinity and water stress effects on forage yield can be an important tool to address yield loss potential when deficit irrigating with poor water quality. Two important forages, alfalfa (Medicago sativa L.) and tall wheatgrass (Agropyron elongatum L.), were grown in a volumetric lysimeter facility where rootzone salinity and water content were varied and monitored. Ground-based hyperspectral canopy reflectance in the visible and near infrared (NIR) were related to forage yields from a broad range of salinity and water stress conditions. Canopy reflectance spectra were obtained in the 350to 1000-nm region from two viewing angles (nadir view, 45 from nadir). Nadir view vegetation indices (VI) were not as strongly correlated with leaf area index changes attributed to water and salinity stress treatments for both alfalfa and wheatgrass. From a list of 71 VIs, two were selected for a multiple linearregression model that estimated yield under varying salinity and water stress conditions. With data obtained during the second harvest of a three-harvest 100-d growing period, regression coefficients for each crop were developed and then used with the model to estimate fresh weights for preceding and succeeding harvests during the same 100-d interval. The model accounted for 72% of the variation in yields in wheatgrass and 94% in yields of alfalfa within the same salinity and water stress treatment period. The model successfully predicted yield in three out of four cases when applied to the first and third harvest yields. Correlations between indices and yield increased as canopy development progressed. Growth reductions attributed to simultaneous salinity and water stress were well characterized, but the corrections for effects of varying tissue nitrogen (N) and very low leaf area index (LAI) are necessary. SALINITY and drought are major inhibitors to agronomic production. The ability of growers to quickly adopt management practices that address productivity hinge on timely and accurate assessments of factors that impede crop growth. Increased efforts to remotely detect the effects of salinity hazards and water stress for irrigation management are needed since few studies have quantitatively assessed the ability of remote sensing technology to characterize simultaneous water and salinity stress on plant yields. Current remote methods for assessment and monitoring salinity hazards in field situations have included optical sensing and soil electromagnetic induction (EM) techniques, both capable of timely assessments. When coupled with geographical information systems and sophisticated soil sampling strategies, EM has been established as a reliable and useful tool to estimate soil salinity patterns of fields between cropping seasons (Corwin et al., 1999). Salinity measurements based on EM are usually taken on bare soil during periods between crops or during seedling establishment. Electromagnetic induction correlates similarly to high water content and soil textures whose particle size is related to water holding capacity. Thus, a high EM reading does not always correspond to saline conditions, unless the bulk conductivity is above some value unapproachable without salinity effects. Despite these interactions, the relationship between bulk soil conductivity (ECa) based on EM and sugarbeet growth and yield has been successfully demonstrated (Kaffka et al., 2000). Similarly, crop reflectance spectra can often be confounded by other factors. Recently, field-derived spectra of salinized soils and vegetation were found to be good indicators of irrigation-induced soil salinization (Dehaan and Taylor, 2002) where spectra were indirectly related to salinity by delineating the presence or absence of various salinity indicator species. They noted, however, that absence of these species did not necessarily indicate that non-saline conditions existed. Bare soil spectral evaluations delineate soil salinity by recognizing evaporiteminerals such as gypsum, halite, bassanite, polyhalite, and bloedite when compared to non-saline soils (Dehaan and Taylor, 2002). In forage production, however, the ability to detect the effects of simultaneous salinity and water stress on yield is needed for a mono-cropped, fully developed canopy. Significant effort has been invested in evaluating relations between plant leaf and canopy reflectance measurements and nutrition, stress agents, and pigment composition. Recent research has related remote spectral indices with nitrogen fertility (Bausch and Duke, 1996; Read et al., 2002; Jacobsen et al., 1998), plant pigments (Sims and Gamon, 2002; Tan et al., 2000; Blackburn, 1998, 1999), and stress induced by a host of factors including water (Peñuelas and Inoue, 1999; Peñuelas et al., 1997; Datt et al., 2003) and pathological agents (Steddom et al., 2003; Rinehart et al., 2002). Irrigation water management may address salinity stress detected indirectly by remote sensing of biomass reductions (Pinter et al., 2003), provided that a priori knowledge of the relationship between crop salinity USDA-ARS George E. Brown, Jr. Salinity Laboratory, 450 West Big Springs Road, Riverside, CA 92507. Use of a company or product name is for the convenience of the reader and does not imply endorsement of the product by the USDA to the exclusion of others that may also be suitable. Received 18 May 2006. *Corresponding author ([email protected]). Published in J. Environ. Qual. 35:1060–1071 (2006). Technical Reports: Plant and Environment Interactions doi:10.2134/jeq2005.0204 a ASA, CSSA, SSSA 677 S. Segoe Rd., Madison, WI 53711 USA Abbreviations: EC, electrical conductivity; ET, evapotranspiration; ETR, evapotranspiration ratio; LAI, leaf area index; NDVI, normalized difference vegetation index; NIR, near-infrared spectral region; R, canopy reflectance; VI, vegetation index; VLS, volumetric lysimeter system. R e p ro d u c e d fr o m J o u rn a l o f E n v ir o n m e n ta l Q u a lit y . P u b lis h e d b y A S A , C S S A , a n d S S S A . A ll c o p y ri g h ts re s e rv e d . 1060 Published online May 31, 2006