Lily (Lilium spp.) is an economically important cut flower and bulb crop whose expansion into coastal production zones is increasingly constrained by progressive soil and water salinization driven by sea-level rise, tidal ingress, and erratic rainfall patterns. Salinity stress alters osmotic balance, disturbs ion homeostasis, and suppresses photosynthetic efficiency, leading to substantial yield and quality losses across many crop species [1-4]. In ornamentals, particularly bulbous crops, elevated electrical conductivity (EC) of soil and irrigation water has been shown to reduce sprouting, bud initiation, stem length, and flower longevity, thereby lowering marketable returns [5, 6]. Coastal belts exhibit high spatial heterogeneity of salinity due to micro-topography, tidal channels, drainage patterns, and land-use history, which complicates the selection of suitable sites and experimental plots. In recent decades, geo-spatial tools such as GIS, remote sensing, and geostatistical interpolation have enabled the mapping of soil salinity, pH, and related edaphic variables at fine spatial resolution, supporting more precise land suitability evaluations in constrained environments [7-10]. However, these spatial datasets are often underutilized when designing agronomic and horticultural experiments, resulting in layouts that fail to adequately account for underlying spatial structure.
Precision-agriculture literature emphasizes that spatially optimized experimental designs can substantially improve the power and reliability of treatment comparisons by reducing confounding effects and incorporating spatial covariance into plot allocation [11, 12]. This is particularly relevant for lily cultivation under coastal salinity stress, where small changes in micro-elevation or drainage can produce sharp gradients in EC and ion composition within short distances. Recent research by Das proposed a geo-referenced design for experimental lily plantations in the Sundarbans, demonstrating how spatial information can guide plot layout and treatment placement under tidally influenced saline conditions [13]. Beyond such pioneering examples, broader integration of geo-spatial mapping, salinity zoning, and statistical design optimization remains limited in floriculture research. Case studies from other salt-affected cropping systems show that layering thematic maps (soil salinity, texture, landform) enables identification of relatively safe or moderately saline pockets where targeted experimentation and cultivation can be undertaken [14, 15]. Advances in digital soil mapping and spatial prediction have further improved the capacity to model continuous variation in soil properties, while plant physiological research continues to refine our understanding of salinity-induced growth constraints [16, 17].
Against this backdrop, the present article focuses on geo-spatial mapping and experimental design optimization for lily cultivation in a coastal salinity context. The overarching objective is to develop a spatially explicit framework that combines high-resolution salinity mapping with robust field-experiment layout to enhance the accuracy of cultivar evaluation and management trials. The working hypothesis is that a geo-spatially optimized design, guided by mapped salinity gradients and spatial statistics, will significantly improve the precision of treatment comparisons and the identification of salinity-tolerant lily genotypes, relative to conventional, non-spatial layouts. By linking geospatial analysis, horticultural experimentation, and salinity physiology, the research aims to support climate-resilient lily production strategies in vulnerable coastal landscapes.
International Journal of Multidisciplinary Trends
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