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a USDA-ARS, George E. Brown, Jr., Salinity Lab., 450 West Big Springs Rd., Riverside, CA 92507-4617
b USDA-ARS, Water Manage. Res. Lab., 9611 S. Riverbend Ave., Parlier, CA 92648
* Corresponding author (dcorwin{at}ussl.ars.usda.gov)
Received for publication March 22, 2002.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODS AND MATERIALSRESULTS AND DISCUSSIONSUMMARY AND CONCLUSIONREFERENCES |
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Abbreviations: ECa, apparent soil electrical conductivity • ECe, electrical conductivity of the saturation extract • GIS, geographical information systems • GPS, global positioning systems • LF, leaching fraction • OLS, ordinary least squares • SP, saturation percentage • 
g, gravimetric water content • 
b, bulk density
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODS AND MATERIALSRESULTS AND DISCUSSIONSUMMARY AND CONCLUSIONREFERENCES |
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Site-specific crop management is the management of soils, pests, and crops based on spatial variations within a field (Larson and Robert, 1991). Site-specific management utilizes rapidly evolving electronic information technologies to modify land management in a site-specific manner as conditions change spatially and temporally (van Schilfgaarde, 1999). The aim of precision agriculture is to improve management to increase profitability, increase crop productivity, sustain the soil–plant–water environment, and/or reduce detrimental environmental impacts (Atherton et al., 1999).
Precision agriculture is a technologically driven system (van Schilfgaarde, 1999). First conceived in the mid 1980s, the technological pieces needed to bring precision agriculture into its own began to fall into place in the mid 1990s with the maturation of global positioning systems (GPS) and geographical information systems (GIS). These and other new technologies potentially provide the ability to (i) quantify yield variability in small areas of the field; (ii) quantify the spatial variability of soil properties influencing yield; and (iii) adjust inputs such as fertilizer, pesticide, and seeding rates based on knowledge of soil and yield variability (Atherton et al., 1999). The measurement of ECa is among the technologies that are helping to bring precision agriculture from a concept to a tool for addressing the issue of agricultural sustainability.
Bullock and Bullock (2000) point out that efficient methods for accurately measuring within-field variations in soil physical and chemical properties are important for precision agriculture. Soil ECa has become one of the most reliable and frequently used measurements to characterize field variability for application to precision agriculture due to its ease of measurement and reliability (Rhoades et al., 1999a, 1999b; Corwin and Lesch, 2003). For instance, it has been previously shown by Kitchen et al. (1999) using boundary-line analysis that soil ECa provides a measure of the within-field soil differences associated with topsoil thickness, which for claypan soils, is a measure of root zone suitability for crop growth and yield. The potential of the spatial measurement of profile ECa for predicting crop yield due to soil differences has been reported by Jaynes et al. (1995) and Sudduth et al. (1995). The rapid spatial measurement of soil ECa has been demonstrated using both mobile electromagnetic (EM) induction (McNeil, 1992; Rhoades, 1992a, 1992b; Carter et al., 1993; Jaynes et al., 1993; Kitchen et al., 1996) and mobile electrical resistivity equipment (Rhoades, 1992a, 1992b; Carter et al., 1993).
Precision agriculture studies relating crop yield directly to ECa have met with inconsistent results due to the complex interaction of soil properties that influence the ECa measurement, thereby confounding results (Corwin and Lesch, 2003). These soil properties include soil salinity, clay content and cation exchange capacity, clay mineralogy, soil pore size and distribution, soil moisture content, organic matter, bulk density (b), and soil temperature (McNeil, 1992; Rhoades et al., 1999a, 1999b; Corwin and Lesch, 2003).
In instances where yield correlates with ECa, spatial measurements of ECa can be used in a precision agriculture context (Corwin and Lesch, 2003). More specifically, spatial ECa information can be used to develop a directed soil sampling plan that identifies sites adequately reflecting the range and variability of various soil properties thought to influence crop yield. This is advantageous because the cost of obtaining soil samples to characterize field spatial variability is a key problem in precision agriculture.
Cotton yield in California's San Joaquin Valley is thought to be influenced by a variety of soil physical and chemical properties including, but not limited to, salinity [electrical conductivity of the saturation extract (ECe)], texture, b, LF, B, NO3–N, and plant-available water. Similarly, the ECa measurement of high clay content soils of the arid San Joaquin Valley is influenced by the properties of ECe, texture, water content, and b and often correlates with soil B levels and LF (Rhoades et al., 1999a, 1999b; Corwin and Lesch, 2003; Corwin et al., 2003). It is hypothesized that in instances where cotton yield correlates with ECa, the spatial distribution of ECa could provide a means of determining the effects of soil variability on yield by guiding an optimized sample design of soil properties influencing yield.
The objectives of this study were to (i) determine the correlation between cotton yield and ECa for a site in the San Joaquin Valley; (ii) utilize an intensive spatial survey of ECa to devise a soil sampling scheme that will identify soil properties influencing cotton yield; (iii) correlate cotton yield with spatially associated soil physicochemical properties, including gravimetric water content (g), ECe, B, pH, percentage clay, b, NO3–N, Cl-, LF, and saturation percentage (SP); and (iv) develop a site-specific cotton yield response model.
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METHODS AND MATERIALS |
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TOPABSTRACTINTRODUCTIONMETHODS AND MATERIALSRESULTS AND DISCUSSIONSUMMARY AND CONCLUSIONREFERENCES |
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Intensive Fixed-Array Apparent Soil Electrical Conductivity Survey
The methods and materials that were used in this study for conducting an ECa survey followed the suggested guidelines of Corwin and Lesch (2003). Mobile fixed-array electrical resistivity equipment developed by Rhoades and colleagues (Rhoades, 1992a, 1992b; Carter et al., 1993) was used in an intensive ECa survey. The intensive ECa survey was conducted in March 2000 following a preplant irrigation to bring the study site to field capacity. The fixed-array electrodes were set to measure ECa to a depth of roughly 1.2 to 1.5 m. Approximately 4000+ ECa measurements were taken across the 32.4-ha study area. Each ECa measurement was georeferenced using GPS.
Soil Sampling Design
Once the intensive ECa survey was conducted, the ESAP-95 version 2.01 software package developed by Lesch et al. (1995a)(1995b, 2000) was used to establish the locations where soil cores were taken based on the ECa survey data. Using a model-based sampling strategy, 60 sites were selected that reflected the observed spatial variability in ECa while simultaneously maximizing the spatial uniformity of the sampling design across the study area. The number of sites is user-defined in ESAP. In this particular instance, 60 sites (six depths at each site) were selected as the maximum number of samples that could be analyzed with the available resources to characterize any encountered spatial autocorrelation. A detailed discussion of the application of a model-based sampling strategy using ECa survey data can be found in Lesch et al. (1995b). Figure 2
is a map of the 4000+ ECa measurements and the 60 locations selected with the ESAP software.
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b determination (Blake and Hartge, 1986), and another set was taken for soil chemical and physical property analysis. All samples were bagged in zip-lock bags and stored in an ice chest until they could be refrigerated. At 17 locations, soil cores could not be taken at the deepest depth increment (i.e., 1.5–1.8 m) because the water table had been reached and the sample was saturated, causing it to run out of the core tube before reaching the soil surface. This resulted in two sets of 343 soil samples.
Soil Chemical and Physical Analyses
In the field, a subsample (200–400 g) of each core from the set of samples designated for chemical and physical property analysis was taken for soil moisture content determination. The subsamples were weighed in the field to minimize any error due to moisture loss. These subsamples were later oven-dried at 110°C for 24 h and weighed again to determine g.
The 343 soil samples designated for analysis of soil chemical and physical properties were analyzed for pH, B, NO3–N, Cl-, EC, and percentage clay. Solution extracts were taken from 1:1 soil/water mixtures. The 1:1 extracts were analyzed for pH, NO3–N, Cl-, EC, and B following procedures outlined in Agronomy Monograph No. 9 (Page et al., 1982). From the electrical conductivity from the 1:1 extracts and from the SP, ECe was calculated because ECe is the most common representation of soil salinity. The second set of 343 soil samples was used to determine b. The LF was estimated by dividing the average Cl- concentration of the irrigation water by the Cl- concentration of the saturation extract at the 1.2- to 1.5-m depth increment. The Cl- concentration of the saturation extract at the 1.2- to 1.5-m depth increment was used because 0 to 1.5 m was selected as the root zone of cotton at the study site. The rationale for using the 0 to 1.5 m as the root zone is discussed in the following section. Particle size distribution was measured using the hydrometer method (Gee and Bauder, 1986).
Simple Statistical Correlations and Cotton Yield Modeling
For all statistical correlation and regression analyses, the average over the root zone (0–1.5 m) at each site was used. Simple correlations were determined between yield and the physicochemical properties of g, ECe, B, pH, percentage clay, b, NO3–N, Cl-, LF, and SP. Correlations between the physicochemical properties and ECa and between cotton yield and ECa were also determined.
The cotton yield data collected during the study did not exactly overlap with the ECa survey data; consequently, ordinary kriging was used to determine the expected cotton yield at the 60 soil-core sites. The spatial correlation structure of yield was modeled with an isotropic variogram (Fig. 3)
. As shown in Fig. 3, the overall structure revealed a sizable nugget term, suggesting the existence of considerable localized yield variation most likely due to large measurement error caused by yield-monitoring dynamics. The following exponential variogram model was used to describe this spatial structure:
 | [1] |
2 represents the nugget variance, 
2 the spatial variance component (partial sill), D the lag distance, and 
the range parameter. A nonlinear least-squares fitting algorithm was used to estimate the three variogram model parameters (standard errors are in parentheses): 2 = 0.76 (0.02), 2 = 1.08 (0.02), and = 109.3 (5.97). This fitted variogram model was then used in an ordinary kriging procedure to estimate the expected yield at the 60 sample sites. The mean estimated yield for the sample sites was 5.95 Mg ha-1, with a range from 3.40 to 7.41 Mg ha-1, and associated kriging standard errors ranged from 0.93 to 0.96 Mg ha-1.
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g instead of volumetric water content because of the collinearity between b and volumetric water content. Based on this exploratory correlation analysis and preliminary multiple linear regression analysis, six primary soil properties were selected for the initial yield response model structure: ECe, LF, pH, percentage clay, g, and b. The above-described data analysis and regression modeling were performed on the individual depth increment sample data (i.e., 0–0.3, 0.3–0.6, 0.6–0.9, 0.9–1.2, 1.2–1.5, and 1.5–1.8 m) and a variety of composite depth increments. No single soil layer or composite of layers rendered a better yield model than the 0- to 1.5-m depth increment; consequently, this was taken to represent the root zone of cotton at the study site. The preliminary regression modeling of cotton yield also showed that 1 of the 60 sample sites was consistently an outlier. The outlier was found to occur at a site intersecting the temporary east–west canal that was excavated to flood-irrigate the northern half of the field. This site was removed from all subsequent statistical analyses involving cotton yield. Figure 2 shows the east–west irrigation canal intersecting the eliminated sample site.
Compensating for Spatial Autocorrelation
The development of functional relationships between crop productivity and yield-influencing factors with classical statistics such as OLS is only valid if the regression model residual errors can be shown to be spatially independent. However, when the residual errors are spatially correlated, OLS estimation techniques generally produce biased parameter estimates and test results (Cressie, 1993). Long (1998) demonstrated that autoregressive response modeling can be used to "diminish the adverse effects of autocorrelation on variance estimates and, thus, permit valid inferences concerning the dependence of Y (crop yield) on the X variables (factors influencing yield)." Spatial autocorrelation can also be adjusted with a maximum-likelihood approach (Littell et al., 1996).
An adjustment of the OLS cotton yield model for spatial autocorrelation was made using the maximum-likelihood approach implemented in the SAS (SAS Inst., 1999) PROC MIXED model-fitting procedure (Littell et al., 1996). Both the model parameter estimates and spatial error parameters were simultaneously estimated using this approach. In addition, the approximate statistical significance of the estimated spatial error parameters was determined using the likelihood test (Littell et al., 1996).
Geographic Information System and Map Preparation
All spatial data were entered into a GIS using the commercial GIS software ArcView 3.1. Interpolated maps of the soil physicochemical properties were prepared by kriging the measurements. Previous studies comparing interpolation methods for mapping soil properties have found kriging better (Laslett et al., 1987; Warrick et al., 1988; Leenaers et al., 1990; Kravchenko and Bullock, 1999) while others have shown inverse distance weighting to be superior (Weber and Englund, 1992; Wollenhaupt et al., 1994; Gotway et al., 1996). Kriging was selected as the preferred method of interpolation because it was more accurate than inverse distance weighting based on the use of the mean squared error as the main criterion for comparison. Interpolated maps of the factors influencing cotton yield were prepared, and a map of the cotton yield was prepared by interpolation of the 7706 cotton yield sites.
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODS AND MATERIALSRESULTS AND DISCUSSIONSUMMARY AND CONCLUSIONREFERENCES |
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b values consistently showed the lowest coefficients of variation. Leaching through the root zone (0–1.5 m), as quantified by the LF, was the highest in a broad band that extended west to east through the middle of the field. Another pocket of high leaching occurred in the southwest corner. Overall, the LF for the entire field was 0.28 with considerable variability across the field (CV = 64.6).
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The correlation coefficient (r) for ECa and yield was r = 0.51 (P < 0.01). Because ECa is a measure of several soil properties, the moderate positive correlation of ECa and yield suggests that either the interaction of these properties was significant in influencing cotton yield or that one or two properties influenced yield and the others were collinear. Even though the correlation between ECa and yield was significant, there were obviously other factors influencing yield beyond those measured by ECa. Nevertheless, at this particular study site, ECa was a useful indicator of cotton yield.
The results from preliminary correlation analysis between ECa and soil properties are shown in Table 3. Electrical conductivity of the saturation extract, B, g, percentage clay, and SP are highly correlated with the ECa, with correlation coefficients of 0.87, 0.88, 0.79, 0.76, and 0.77, respectively (Table 3). However, B is not a property measured by ECa. The high correlation of B to ECa is an artifact due to its close correspondence to salinity (i.e., ECe) as a result of the process of leaching. The correlation coefficient between B and ECe was 0.96. The high correlation of ECa to both percentage clay and SP is expected because it reflects the influence of texture on the ECa reading and because percentage clay and SP are highly correlated (r = 0.99). In this particular field, ECa is highly correlated with salinity, g, and texture.
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g, and b appear to be linearly related with the yield data to various degrees (Fig. 4c, 4e, and 4f). Even though there is no correlation between yield and pH (r = -0.01; see Fig. 4d), pH became statistically significant in the presence of the other variables in the final yield response model. Although the absolute soil water content is clearly time dependent, the relative spatial patterns of water content variation (i.e., hydrologic signatures) remain fairly constant over the growing season (Engman, 1999). This suggests that the g data can serve as a surrogate variable indicating the relative level of plant-available water. The high correlation between percentage clay and g (r = 0.90) and between percentage clay and calculated water content at field capacity (r = 0.79 where field capacity = SP/2; Rhoades et al., 1999a) suggests that hydrologic signatures are reasonably stable at this study site.
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Crop Yield Response Model
Based on initial exploratory correlation and multiple linear regression analysis, the following regression model structure was proposed for describing the soil property effects on cotton yield:
 | [2] |
g, and b are assumed to be linear; the relationship between yield and ECe is assumed to be quadratic; the relationship between yield and LF is assumed to be curvilinear; ß0, ß1, ß2, ... , ß7 are the regression model parameters; and 
represents the random error component, initially assumed to be normally distributed and spatially independent.
Table 4 shows the OLS regression modeling summary results for Eq. [2]. The R2 value of 0.61 suggests that Eq. [2] successfully described slightly more than 60% of the estimated spatial yield variation. The nonsignificant t tests associated with the percentage clay and b parameter estimates suggest that these soil properties do not contribute to the yield predictions in a statistically meaningful manner. However, all of the other parameters appear to be significant near or below the 0.05 level.
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b can be removed from the regression model. The likelihood ratio test statistic for the spatial covariance structure yielded a Chi-square value of 8.8 (see Table 5; 111.3 - 102.5 = 8.8) with 1 degree of freedom (p = 0.003), indicating that the errors are spatially correlated.
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b from Eq. [2] resulted in the most robust and parsimonious yield response model for cotton. Table 6 shows the final maximum likelihood parameter estimates after removing percentage clay and b, which results in the following yield response model:
 | [3] |
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g parameter estimates are significant at or near the 0.05 level. The LF2 and pH parameters are both negative, implying that the yield decreased as either the LF or soil pH increased. The g term is positive, implying that the yield increased as the plant-available water content increased. The positive linear and negative quadratic ECe terms imply that the yield increased under low salinity but decreased under higher salinity levels. The point of maximum yield with respect to salinity was calculated by setting the first partial derivative of the fitted regression to zero with respect to ECe, which resulted in a value of 7.17 dS m-1. This is very similar to the salinity threshold for cotton of 7.7 dS m-1 reported by Maas and Hoffman (1977). Table 6 also shows the estimated sill (mean square error) and range parameters for the assumed exponential spatial covariance structure. The maximum likelihood estimate of the mean square error was 0.39, indicating that the root mean square error is about 0.63 Mg ha-1 for this model. The range parameter estimate was about 66.2 m, indicating that the range of residual spatial correlation was less than the corresponding raw yield spatial correlation range of 109.3 m but still statistically significant.
Figure 6 displays the final observed vs. predicted cotton yield estimates while Fig. 7 compares maps of measured cotton yields and predicted yields based on Eq. [3]. Figure 6 suggests that the estimated regression relationship has been reasonably successful at reproducing the predicted yield estimates. Figure 7 shows a reasonably close spatial association between interpolated measured and predicted maps.
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With respect to the northern half of the field, high leaching in the vicinity of the temporary irrigation canal (i.e., the east–west midfield portion of the canal) and progressively lower leaching from south to north are the consequence of uncontrolled leaching from the unlined canal and flood irrigation down the north–south furrows from the midfield canal, respectively. The uneven water distribution from south to north is a consequence of the time for water to travel down the furrows and reach the end of the field; consequently, greater applied water, infiltration, and leaching occurred near midfield closest to the irrigation source (i.e., the east–west canal) with diminished applied water from south to north.
The observed spatial patterns of LF in the southern half of the field shows greater leaching on the west side than on the east side with no noticeable north–south trends. The east–west spatial patterns of LF are similar to the general east–west patterns of percentage clay with decreasing clay content from west to east. The fact that greater leaching occurred where fine-textured soil is present is unexpected and more likely reflects nonuniformity of water application from west to east due to flood irrigation. The nonuniform water application is the consequence of higher volumes of water being applied on the west side of the field closest to the source of irrigation water and nearest to the unlined north–south canal.
A potential cause-and-effect explanation for the inverse relationship of yield to LF may also relate to overleaching. Intuitively, areas receiving larger amounts of water would most likely produce higher yields unless the areas of lowest leaching were receiving sufficient water to meet water needs and areas of high leaching were being overleached. Overleaching would be a consequence of applying more water than needed for the particular soil texture. Coarse-textured soils require less water to reach field capacity than fine-textured soils. Overleaching would remove important mobile nutrients. Evidence of overleaching is reflected by the field's negative correlation between LF and NO3–N (r = -0.52).
An interpretation of how and why the remaining two soil properties, pH and g, influence yield provides further insight into the crop response dynamics. Soil pH has a negative parameter estimate, indicating an inverse relationship with yield, while g has a positive relationship (Table 6). The relationship between yield and g reflects the positive response of the plant to higher available water. Over the range of encountered root-zone pHs (7.21–8.12), an increase in the pH slightly decreased the yield. Potential reasons for the slight decrease in yield with increasing pH are (i) the solubility of cationic trace elements decreases with increasing pH, so plant deficiencies of Cu, Fe, Mn, and Zn commonly exist at higher pH in saline soils (Page et al., 1990) and (ii) high pHs can cause soil infiltration problems (Suarez et al., 1984), thereby reducing water availability.
Because of the close positive correlation between B and salinity (r = 0.96), it is difficult to separate out the yield reduction effect of salinity and B. It could be argued that B rather than salinity was influencing cotton yield or that both in combination were influencing yield. The root-zone threshold levels of salinity and B are 7.7 dS m-1 (Maas and Hoffman, 1977) and 10 mg L-1 (Maas, 1984), respectively, for cotton. The average root-zone salinity in 20 locations was >7.7 dS m-1. The average root-zone B concentration exceeded 10 mg L-1 at 14 locations. However, exploratory correlation analysis showed no influence of B on cotton yield. Furthermore, the fact that B was eliminated by backward elimination procedures from exploratory model variable selection suggests that there is no statistical evidence for the reduction of cotton yield by B.
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SUMMARY AND CONCLUSION |
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TOPABSTRACTINTRODUCTIONMETHODS AND MATERIALSRESULTS AND DISCUSSIONSUMMARY AND CONCLUSIONREFERENCES |
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b, and clay content. Hypothetically, when crop yield correlates with ECa, spatial distributions of ECa provide a potential means of determining soil properties that influence yield. This hypothesis was evaluated. A yield map would provide this same capability, but because yield monitoring has not been developed for all crops, ECa maps provide a viable alternative when yield-monitoring data are not available. From a practical perspective, the key to determining the soil properties that influence crop yield is a sample design that minimizes the number of samples but spatially characterizes the soil properties influencing yield. Rapid and easily obtained spatial measurements of ECa provide a means of determining the soil properties that influence cotton yield by serving as covariate spatial information for directing soil sample design. This approach minimizes an otherwise intensive grid sampling of multiple soil parameters with a sample design optimized for the characterization of crop yield and soil heterogeneity. The presented approach provides a means of identifying within a field the predominant soil properties that influence cotton yield through the correlation of various soil properties and cotton yield at points that characterize the field's full range of yield and properties influencing that yield. However, this is just one piece to the puzzle because yield is influenced by a complex interaction of meteorological (humidity, temperature, etc.), biological (e.g., pests), anthropogenic (management related), and edaphic (soil related) factors. Nevertheless, knowing the site-specific edaphic influences on crop yield provides a layer of information useful in its site-specific management. By knowing the soil properties that influence a crop's yield, recommendations can be made to improve productivity. Based on Eq. [3], cotton yield at the Broadview Water District study site can hypothetically be improved by
All four recommendations can be accomplished by improved water application timing and distribution and precision application of soil amendments. By improving the water application distribution and timing, (i) areas with high LF (i.e., LF > 0.5) can receive less water; (ii) areas with average root-zone salinity > 7.17 dS m-1 can be leached more heavily; and (iii) coarse-textured soils with low plant-available water (i.e., g < 0.3 g g-1) can be irrigated more frequently. Areas of high pH (i.e., pH > 8) can be lowered with the addition of a soil amendment (e.g., organic matter, acid, sulfur). Clearly, the delineation of site-specific management units based on this information can be accomplished with the overlay capability in a GIS.
The presented approach provides spatial information for use in soil and crop management. The aforementioned recommendations reflect the importance of irrigation management and efficiency to cotton yield on arid-zone soils of the San Joaquin Valley. As indicated, spatial distribution and frequency of applied irrigation water are important factors in cotton yield by controlling leaching of NO3–N, salt accumulation in the root zone, and available water to the plant. The results suggest that irrigation water needs to be applied at a higher frequency in areas of a field that have lower plant-available water (i.e., generally more sandy-textured soils), whereas soils high in clay with higher plant-available water need less frequently applied water. Furthermore, an efficient spatial distribution of water application is needed that optimizes leaching. Optimal leaching provides sufficient water at a location to optimize crop yield while minimizing environmental impacts and unnecessary depletion of water resources. This is achieved by (i) providing sufficient water to meet evapotranspiration and leaching needs; (ii) minimizing overleaching of nutrients, which reduces yield and detrimentally impacts ground water and drainage water; and (iii) adequately leaching areas that are above the salinity threshold for the crop. All of these require accurate spatial information on crop water use.
Adjustments to the application of irrigation water within a field can be based on the analysis and interpretation of spatial soil data as presented in this study. However, the ability to control the application of irrigation water to delineated management units within a field is limited. Commercially available irrigation systems capable of within-field application control at a cost-effective price are currently unavailable. Nevertheless, conventional sprinkler and flood irrigation systems can still benefit from this spatial soil information provided simple modifications are made to the irrigation system. Stationary sprinkler systems can be outfitted with valves at each sprinkler head to control frequency and distribution of application. Maps of plant-available water and salinity distribution can then be used to adjust by hand the volume and frequency of irrigation water application. In areas where flood irrigation occurs, more uniform application of irrigation water can be achieved through shorter runs that minimize over- and underleaching. The locations of shorter or longer runs and the volume of water applied within each furrow can be established from the maps of soil properties influencing cotton yield. The presented GIS-based approach provides the maps that make this level of irrigation management and efficiency possible from a strictly informational standpoint.
As a cautionary note, there are definite limitations to the site-specific information that can be derived from Eq. [3]. First, it is apparent from the moderate R2 value for Fig. 6, which compares observed and predicted yields as calculated from Eq. [3], that there are factors (biological, meteorologic, and anthropogenic) influencing yield other than those identified in the equation. The full range of influence of these factors on the identified edaphic factors is unknown. For instance, increases in humidity or CO2 could drastically alter the influence of the edaphic factors and their interrelationship as quantified in Eq. [3]. Second, the robustness of Eq. [3] is limited because of the noise in the yield data, which is a function of the yield monitor and combine dynamics. Furthermore, because Eq. [3] was developed from one observational field study without any experimental control over the state variables, it is not sufficiently robust to use in an explicit calculation of ECe, LF, v, and pH values that will optimize cotton yield at point locations within the field. Rather, the yield model serves as an implicit indicator of those factors that can be adjusted, over the range of their measured occurrence within the field, to improve yield.
It is well known that the exclusive use of ECa maps to explain yield variability is ineffective. Even though a crop yield map is the best indicator of all factors influencing yield because it encompasses edaphic, anthropogenic, biological, and meteorological factors, the interactions of these factors are too complex to derive site-specific management recommendations. Crop yield maps by themselves are of limited utility because of the difficulty in isolating the influence of each factor. However, a map of ECa can be used in soil sample design to help isolate soil-related factors and specific anthropogenic factors (i.e., leaching efficiency), which influence yield heterogeneity, thereby providing an initial level of understanding for making site-specific management recommendations. Furthermore, crop yield maps are not always obtainable because yield-monitoring equipment has not been developed for all crops, but a map of ECa can always be obtained. In general, exclusive use of a yield or ECa map by itself is not sufficient to understand the reason(s) for yield variability in a field. Each is a piece to the puzzle of understanding the cause-and-effect factors influencing the spatial variation of a crop's yield.
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ACKNOWLEDGMENTS |
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMETHODS AND MATERIALSRESULTS AND DISCUSSIONSUMMARY AND CONCLUSIONREFERENCES |
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| The SCI Journals | Crop Science | Vadose Zone Journal | |||
| Journal of Natural Resources and Life Sciences Education |
Soil Science Society of America Journal | ||||
| Journal of Plant Registrations | Journal of Environmental Quality |
The Plant Genome | |||
