SPATIAL RELATIONSHIPS BETWEEN SOIL SEED BANK AND SEEDLING POPULATIONS OF HORDEUM SPONTANEUM AND CARDARIA DRABA IN SAFFRON FIELDS

Geostatistical techniques were used to characterize the spatial relationship between Hordeum spontaneum and Cardaria draba seedling and soil seed bank over the entire growing season of 2004-2005 in three saffron (Crocus sativus L.) fields, located in Southern Khorasan (33° North latitude, 57° East longitude), Iran. The maps of H. spontaneum seed bank density corresponded moderately to those seedling density in a and strongly to those in b and c fields. The emergence percentage of Cardaria draba was higher than for H. spontaneum in all fields. Semivariograms showed spatial autocorrelation in seed bank and seedling populations of H. spontaneum and C. draba in all fields. Grey-scale field maps of C. draba seed banks corresponded visually to maps of seedling populations and could have been used to target control efforts, but visual correspondence between H. spontaneum seed bank and seedling maps was poor. INTRODUCTION Understanding the spatial and temporal dynamics of weed populations and the rate of their spread within fields is increasingly important as methods are being developed for the site-specific management of weeds (Zhang and Wang, 2002). Several studies have shown that weeds are not randomly distributed but are aggregated at one or more spatial scales (Cardina et al., 1995; Mortensen et al., 1998; González-Andújar and Saavedra, 2003; Jurado-Expósito et al., 2004). The cause of the patchy nature of weed distribution has been attributed to interacting influences of innate weed biology, local microenvironmental conditions, and agricultural practices (Bigwood and Inouye, 1988; Chavel et al., 1989). A weed patch is considered stable if it is consistent with density and location over time (Wyse, 1996; Gerhards et al., 1997). Stability is important from the perspective of patch management, so that a patch map from one year can be used to direct weed control in subsequent years (Mortensen et al., 1998). Knowledge of spatial variability helps to improve weed management efficiency (Moloney, 1988; Wiles et al., 1992a; Colbach et al., 2000). Currently, yield loss is usually overestimated (Brain and Cousens, 1990) as patchiness of weeds may result in incorrect evaluation of control measures (Wiles et al., 1992a). An approach to the analysis of weed spatial distribution has been described for emerged populations of common lambsquarters (Chenopodium album L.) in soybean (Glycine max (L.) Merr) fields (Cardina et al., 1996). The process involves the use of semivariance statistic to characterize the spatial structure of the weed populations. Maps showing the distribution of weed density were drawn using kriging techniques to interpolate values at locations from which samples were not taken (Cardina et al., 1995; Colbach et al., 2000; Mohammadi, 2002). Bioeconomic models have been developed to estimate seedling emergence time and density for the purpose of making early-season weed management decisions (Lybecker et al., 1991; Swinton & King, 1994). Previous approaches to determine the relationship between weed seed bank populations in soil and future seedling populations have generally relied on correlation coefficients and regression techniques to generate single values describing relationships between global means and variances (Wilson et al., 1985; Ball and Miller, 1989; Forcella, 1992). If seed bank data are to be valuable in characterizing and predicting weed populations for weed management, they should describe the spatial distribution of weeds in a field. Weed threshold densities are generally low, and densities of weeds within a patch commonly exceed threshold levels (Cardina et al., 1996). Therefore, seed bank data may be more valuable if they describe the pattern of the weed population than if they simply predict a single density value for a field. Information about the spatial pattern of weeds in a field could be used to target control efforts as well as to develop efficient scouting protocols. Because real-time weed detection and precision application are not currently possible in Iran, seed bank or seedling maps are the most practical approach for targeting management efforts. The cross-semivariogram analysis of spatial statistics is one way to describe the variation between seed bank and emerged populations in a field. Cross-semivariograms describe the joint spatial dependence, or continuity, between two variables such as the seed bank and emerged weeds. This approach has been used to examine the spatial relationship between two soil properties (Vauclin et al., 1983), two carabid species, summer and fall collections of the same insect (Rossi et al., 1992), and other pairs of variables that are influenced by the same local conditions or spatial processes (Trangmar et al., 1985). The objective of this study was to describe the spatial relationship between fall populations of seeds in the soil seed bank and winter populations of seedlings in three saffron fields. MATERIALS AND METHODS Field experiments were conducted during the growing season of 2004-2005 in three fields (a, b, and c, separated about 150 m from each other), 32 m wide and 60 m long, located at Boshrooyeh (33° North latitude, 57° East longitude) region in southern Khorasan, Iran. Cropping condition and management practices were similar across the whole three fields. The soil was sandy loam. The fields were fertilized with 250 kg ha ammonium phosphate according to soil tests recommendations every year. Saffron had been sown in late August of 2002. Galant super TM [haloxyfop-Rmethyl] was applied to the three fields at a rate of 162 g.a. i ha during the first week of every year, to control winter annual grasses. Weeds were also counted after each herbicide application. Hordeum spontaneum (L.) HUDS. subsp. Gussoneanum (Mouse barley as a winter annual grass) and Cardaria draba (L.) Desv. subsp. Chalepensis (Hoary cress) as a perennial weed are the most important weeds monitored in saffron fields in Iran (Rashed, 1992). An area measuring 32 m wide by 60 m long was selected for the intensity surveys on each field. H. spontaneum and C. draba seedling and seed bank density assessments were performed following a 4 m X 4 m grid pattern on each area, a total of 144 sampling units on each field. At each node of grid pattern, the numbers of weed seedlings were counted in the three fields within a permanent 50 by 30 cm quadrat, perpendicular to crop rows. During the fourth week of January 2005, the edge of the quadrat was placed about 1cm from the edge of the hole left by the soil probe so that the seed bank extraction and the seedling counts were as close as possible. In early November 2004, soil samples were taken with an auger 5 cm in diameter and 7.5 cm deep. A total of 144 cores were collected from each node of grid pattern. Each core was analyzed individually. Seeds from the soils were extracted by a modified sieving/floatation technique (Shaukat and Siddiqui, 2004). Soil from each core was mixed with 250 ml 0.5 % sodium hexametaphosphate (Calgon solution). The suspension was passed through two sieves. The upper one with 0.5 mm mesh size and the lower one with 0.2 mm mesh size. The hollow seeds floating on the surface of the suspension were discarded. Subsequently, the material retained on the lower sieve was collected on a piece of filter paper and air-dried. Seeds were counted and identified by species under a stereomicroscope (x 10). Viability test was not performed and thus the viability can not be assured. Ball and Miller (1989), demonstrated that sieving floatation method or germination technique provide closely similar estimates of the relative density of different species in the seed bank. Geostatistical Analysis The seed bank and seedling data at each sampling site were converted to ground area unit. Percentage emergence was calculated as the density of emerged seedlings (from quadrat counts in the field) divided by the density of seeds in the seed bank (from corresponding soil core samples). Before subjecting data to spatial statistical analysis, they were log-transformed and detrended by a median polishing procedure as described previously (Cardina et al., 1995). The semivariance statistic was calculated as half the average squared difference between data values at pairs of points a given distance apart (Vauclin et al., 1983; Rossi et al., 1992). The semivariance is a measure of spatial dependence, and it can be interpreted as an index of univariate spatial continuity (Isaaks and Srivastava, 1989). To describe the spatial correlation between data points the semivariance statistic was calculated as: ( ) ( ) ( ) ( ) [ ] ( ) 2