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Scarce freshwater resources in arid and semiarid regions means that recreational landscapes should use recycled or low quality waters for irrigation, increasing the risk of salinity and infiltration problems. We map salinity distribution within turf fields using electromagnetic sensing, evaluate need for leaching and evaluate post leaching results for subsequent management decisions. Electromagnetic measurements were made with two EM38 instruments positioned vertically and horizontally in order to determine salinity distribution. Sensor readings were coupled to GPS data to create spatial salinity maps. Next, optimal calibration point coordinates were determined via ESAP software. Soil samples were taken from 0-60 cm at 5 depths for each calibration point. Laboratory soil saturation percentage, moisture content, ECe and pHe of saturation extracts were determined for calibration to convert resistivity measurements to ECe. Next, ECe maps were created using ESAP software. Leaching for reclamation was performed by means of sprinkling. Treated municipal wastewater was utilized both for irrigation and for reclamation leaching. Low water content and high spatial variability of soil texture adversely affected the accuracy of the readings. Pre and post leaching surveys indicate that there was only a 30% decrease in salinity, very low relative to expected results considering the amount of water applied. This relatively low reduction in salinity and the lack of runoff during irrigation combined with infiltration measurements suggests that aeration techniques for healthier grasses led to water bypassing small pores thus limiting leaching efficiency. In this instance practices to improve infiltration lead paradoxically to less salinity reclamation than expected.

Directed soil sampling based on geospatial measurements of apparent soil electrical conductivity (EC a ) is a potential means of characterizing the spatial variability of any soil property that influences EC a including soil salinity, water content, texture, bulk density, organic matter, and cation exchange capacity. Multi-field EC a survey data often exhibit abrupt changes in magnitude across field boundaries that complicate the calibration of EC a to soil salinity (i.e., EC e , electrical conductivity of the saturation extract) over large spatial extents. The primary objective of this study is to evaluate three regression techniques for calibrating EC a to EC e over spatial scales ranging from a few thousand to a hundred thousand hectares, where EC a was measured using electromagnetic induction equipment. The regression techniques include analysis of covariance (ANOCOVA), field specific regression (FSR), and common coefficient regression (CCR). An evaluation was made by comparing jack-knifed mean square prediction errors (MSPE) of EC e for two case studies: 2400 ha of the Broadview Water District in California's San Joaquin Valley and roughly 100,000 ha of the west side of Kittson County in the Red River Valley of Minnesota. The ANOCOVA model outperformed the FSR and CCR regression models on a prediction accuracy basis with the smallest MSPE estimates for depth predictions of soil salinity. The implication of this evaluation is that once ANOCOVA models for each depth are established for a representative set of fields within a regional-scale study area, then the slope coefficients can be used at all future fields, thereby significantly reducing the need for ground-truth soil samples at future fields, which substantially reduces labor and cost. Land resource managers, producers, agriculture consultants, extension specialists, and Natural Resource Conservation Service field staff are the beneficiaries of regional-scale maps of soil salinity.

This article presents a new goodness-of-fit (GOF) test statistic for multiply Type II censored Exponential data. The new test also applies to ordinary Type II censored samples and complete samples, since those cases are special cases of multiply Type II censoring. This test statistic is based on a ratio of linear functions of order statistics. Empirical power studies confirm that this ratio test compares favorably to currently available GOF tests for ordinary Type II censored data. Three data analysis examples are provided that demonstrate the usefulness of this new test statistic.

Soil salinity is a spatially complex and dynamic property of soil that influences crop yields when the threshold salinity level is exceeded. The mapping and monitoring of soil salinity is necessary for reclamation, crop selection, and site-specific irrigation management of salt-affected soils in the arid and semi-arid agricultural regions of the world. Because of its spatial and temporal heterogeneity, soil salinity is difficult to map and monitor at field scales. There are various methods for characterizing soil salinity variability, but none of these approaches has been as extensively investigated and is as reliable and cost effective as apparent soil electrical conductivity (ECa) directed soil sampling. Geospatial measurements of ECa are well-suited for characterizing soil salinity spatial distribution because they are reliable, quick, and easy to take with GPS-based mobilized ECa measurement equipment. However, ECa is influenced by a variety of soil properties, which makes the measurement of soil salinity at field scale problematic. It is the goal of this review and analysis paper to provide an overview of the field-scale characterization of soil salinity distribution using ECa-directed soil sampling. Guidelines, special considerations, protocols, and strengths and limitations are presented for characterizing spatial and temporal variation in soil salinity using ECa-directed soil sampling. Original data is presented showing the critical importance of conducting ECa surveys at or near field capacity (≥70% of field capacity). A case study of a 32.4-ha field in San Jacinto, California, is provided as an example to demonstrate the effectiveness of generating soil salinity maps. Land resource managers, farmers, extension specialists, and Natural Resource Conservation Service field staff are the beneficiaries of field-scale maps of soil salinity.

Soil spatial variability has a profound influence on most agronomic and environmental processes at field and landscape scales, including site-specific management, vadose zone hydrology and transport, and soil quality. Mobile sensors are a practical means of mapping spatial variability because their measurements serve as a proxy for many soil properties, provided a sensor-soil calibration is conducted. A viable means of calibrating sensor measurements over soil properties is through linear regression modeling of sensor and target property data. In the present study, two sensor-directed, model-based, sampling scheme delineation methods were compared to validate recent applications of soil apparent electrical conductivity (EC)-directed spatial simulated annealing against the more established EC-directed response surface sampling design (RSSD) approach. A 6.8-ha study area near San Jacinto, CA, was surveyed for EC, and 30 soil sampling locations per sampling strategy were selected. Spatial simulated annealing and RSSD were compared for sensor calibration to a target soil property (i.e., salinity) and for evenness of spatial coverage of the study area, which is beneficial for mapping nontarget soil properties (i.e., those not correlated with EC). The results indicate that the linear modeling EC-salinity calibrations obtained from the two sampling schemes provided salinity maps characterized by similar errors. The maps of nontarget soil properties show similar errors across sampling strategies. The Spatial Simulated Annealing methodology is, therefore, validated, and its use in agronomic and environmental soil science applications is justified.

Several studies have reported on the detection of perchlorate (ClO(4)(-)) in edible leafy vegetables irrigated with Colorado River water. However, there is no information on spinach as related to ClO(4)(-) in irrigation water nor on the effect of other anions on ClO(4)(-) uptake. A greenhouse ClO(4)(-) uptake experiment using spinach was conducted to investigate the impact of presence of chloride (Cl(-)) and nitrate (NO(3)(-)) on ClO(4)(-) uptake under controlled conditions. We examined three concentrations of ClO(4)(-), 40, 220, and 400 nmol(c)/L (nanomoles of charge per liter of solution), three concentrations of Cl(-), 2.5, 13.75, and 25 mmol(c)/L, and NO(3)(-) at 2, 11, and 20 mmol(c)/L. The results revealed that ClO(4)(-) was taken up the most when NO(3)(-) and Cl(-) were lowest in concentration in irrigation water. More ClO(4)(-) was detected in spinach leaves than that in the root tissue. Relative to lettuces, spinach accumulated more ClO(4)(-) in the plant tissue. Perchlorate was accumulated in spinach leaves more than reported for outer leaves of lettuce at 40 nmol(c)/L of ClO(4)(-) in irrigation water. The results also provided evidence that spinach selectively took up ClO(4)(-) relative to Cl(-). We developed a predictive model to describe the ClO(4)(-) concentration in spinach as related to the Cl(-), NO(3)(-), and ClO(4)(-) concentration in irrigation water.

Subsurface measures have been adapted to identify manure accumulation on feedlot surfaces. Understanding where manure accumulates can be useful to develop management practices that mitigate air emissions from manure, such as odor or greenhouse gases. Objectives were to determine if electromagnetic induction could be used to predict differences in volatile fatty acids (VFA) and other volatiles produced in vitro from feedlot surface material following a simulated rain event. Twenty soil samples per pen were collected from eight pens with cattle fed two different diets using a predictive sampling approach. These samples were incubated at room temperature for 3 d to determine fermentation products formed. Fermentation products were categorized into acetate, straight-, branched-chained, and total VFAs. These data were used to develop calibration prediction models on the basis of properties measured by electromagnetic induction (EMI). Diet had no significant effect on mean volatile solids (VS) concentration of accumulated manure. However, manure from cattle fed a corn (Zea mays L.)-based diet had significantly ( P ≤ 0.1) greater mean straight-chained and total VFA generation than pens where wet distillers grain with solubles (WDGS) were fed. Alternately, pens with cattle fed a WDGS-based diet had significantly (P ≤ 0.05) greater branched-chained VFAs than pens with cattle fed a corn-based diet. Many branched-chain VFAs have a lower odor threshold than straight-chained VFAs; therefore, emissions from WDGS-based diet manure would probably have a lower odor threshold. We concluded that diets can affect the types and quantities of VFAs produced following a rain event. Understanding odorant accumulation patterns and the ability to predict generation can be used to develop precision management practices to mitigate odor emissions.

Spatial variability has a profound influence on a variety of landscape-scale agricultural issues including solute transport in the vadose zone, soil quality assessment, and site-specific crop management. Directed soil sampling based on geospatial measurements of apparent soil electrical conductivity [Formula: see text] is a potential means of characterizing the spatial variability of any soil property that influences [Formula: see text] including soil salinity, water content, texture, bulk density, organic matter, and cation exchange capacity. Arguably the most significant step in the protocols for characterizing spatial variability with [Formula: see text]-directed soil sampling is the statistical sampling design, which consists of two potential approaches: model- and design-based sampling strategies such as response surface sampling design (RSSD) and stratified random sampling design (SRSD), respectively. The primary objective of this study was to compare model- and design-based sampling strategies to evaluate if one sampling strategy outperformed the other or if both strategies were equal in performance. Using three different model validation tests, the regression equation estimated from the RSSD data produced accurate and unbiased predictions of the natural log salinity levels at the independently chosen SRSD sites. Design optimality scores (i.e., D-, V-, and G-optimality criteria) indicate that the use of the RSSD design should facilitate the estimation of a more accurate regression model, i.e., the RSSD approach should allow for better model discrimination, more precise parameter estimates, and smaller prediction variances. Even though a model-based sampling design, such as RSSD, has been less prevalent in the literature, it is concluded from the comparison that there is no reason to refrain from its use and in fact warrants equal consideration.

The ability to inventory and map soil salinity at regional scales remains a significant challenge to scientists concerned with the salinization of agricultural soils throughout the world. Previous attempts to use satellite or aerial imagery to assess soil salinity have found limited success in part because of the inability of methods to isolate the effects of soil salinity on vegetative growth from other factors. This study evaluated the use of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery in conjunction with directed soil sampling to assess and map soil salinity at a regional scale (i.e., 10-10(5) km(2)) in a parsimonious manner. Correlations with three soil salinity ground truth datasets differing in scale were made in Kittson County within the Red River Valley (RRV) of North Dakota and Minnesota, an area where soil salinity assessment is a top priority for the Natural Resource Conservation Service (NRCS). Multi-year MODIS imagery was used to mitigate the influence of temporally dynamic factors such as weather, pests, disease, and management influences. The average of the MODIS enhanced vegetation index (EVI) for a 7-yr period exhibited a strong relationship with soil salinity in all three datasets, and outperformed the normalized difference vegetation index (NDVI). One-third to one-half of the spatial variability in soil salinity could be captured by measuring average MODIS EVI and whether the land qualified for the Conservation Reserve Program (a USDA program that sets aside marginally productive land based on conservation principles). The approach has the practical simplicity to allow broad application in areas where limited resources are available for salinity assessment.

A study was initiated to test the validity of using electromagnetic induction (EMI) survey data, a prediction-based sampling strategy, and ordinary linear regression modeling to predict spatially variable feedlot surface manure accumulation. A 30- by 60-m feedlot pen with a central mound was selected for this study. A Dualem-1S EMI meter (Dualem Inc., Milton, ON, Canada) pulled on 2-m spacing was used to collect feedlot surface apparent electrical conductivity (EC a ) data. Meter data were combined with global positioning system coordinates at a rate of five readings per second. Two 20-site sampling approaches were used to determine the validity of using EMI data for prediction-based sampling. Soil samples were analyzed for volatile solids (VS), total N (TN), total P (TP), and Cl ― . A stratified random sampling (SRS) approach (n = 20) was used as an independent set to test models estimated from the prediction-based (n = 20) response surface sample design (RSSD). The RSSD sampling plan demonstrated better design optimality criteria than the SRS approach. Excellent correlations between the EMI data and the In(Cl ― ), TN, TP, and VS soil properties suggest that it can be used to map spatially variable manure accumulations. Each model was capable of explaining >90% of the constituent sample variations. Fitted models were used to estimate average manure accumulation and predict spatial variations. The corresponding prediction maps show a pronounced pen design effect on manure accumulation. This technique enables researchers to develop precision practices to mitigate environmental contamination from beef feedlots.

Disposal of saline drainage water is a significant problem for irrigated agriculture. One proposal is to recycle drainage water to irrigate salt tolerant crops until the volume has been reduced sufficiently to enable final disposal by evaporation. Part of this concept requires in situ crop water reuse from shallow groundwater; and data is needed to quantify the potential use of groundwater by alternative crops. A column lysimeter study was initiated to determine the potential crop water use from shallow groundwater by alfalfa as a function of groundwater quality and depth to groundwater. The results demonstrated that up to 50% of the crop water use could be met from shallow groundwater ( 4 dS/m) in the shallow groundwater experienced increased salinity in the soil profile with time, which resulted in reduced crop water use from shallow groundwater. Yields decreased with time as the groundwater salinity increased and periodic leaching will be required for in situ use to be a sustainable practice. Statistical analysis of crop yield demonstrated that there was significant use of groundwater with an EC of 6 dS/m for a few years.

In this article, we propose a simple technique that can be used to calculate accurate approximations for the covariances of Weibull and/or Gamma order statistics, given knowledge of the corresponding means and variances of these order statistics. Ratio formulas for relating the covariances and variances of the order statistics for these two probability distribution functions (pdf's) are intuitively derived from a first-order Mosteller approximation, and then refined using the Blom adjustment technique. The necessary Blom coefficients are in turn found by optimizing the well-known recursive formula for mixed moments using standard nonlinear estimation techniques. Analytical and simulation results confirm that very good approximations to the covariance matrices of Gamma and Weibull order statistics can be obtained for a wide range of shape parameters using this methodology. Four examples are provided which demonstrate how this approach can be used to derive accurate o-BLU weights, precise variance approxima...

In this article we review two historical approximations to the Poisson and binomial cumulative distribution functions (CDFs); that is, the Wilson–Hilferty and Camp–Paulson approximations. Both of these approximations reduce to standard normal formulas that produce very accurate estimates of the Poisson and binomial CDFs, and are thus quite simple to implement. Additionally, in an upper-division undergraduate or master’s level probability and inference course, the derivation of these approximations presents a nice opportunity to introduce and study the distributional relationships between the gamma and Poisson CDFs, and the binomial, beta, and F CDFs. This article presents the basic theorems and lemmas needed to derive each approximation, along with some relevant examples that compare and contrast the precision of these approximations with their large-sample, limiting normal counterparts.

Geospatial measurements of ancillary sensor data, such as bulk soil electrical conductivity or remotely sensed imagery data, are commonly used to characterize spatial variation in soil or crop properties. Geostatistical techniques like kriging with external drift or regression kriging are often used to calibrate geospatial sensor data to specific soil or crop properties. More traditional statistical methods such as ordinary linear regression models are also commonly used. Unfortunately, some soil scientists see these as competing and unrelated modeling approaches and are unaware of their relationship. In this article we review the connection between the ordinary linear regression model and the more comprehensive geostatistical mixed linear model and describe when and under what conditions ordinary linear regression models represent valid spatial prediction models. The formulas for the ordinary linear regression model parameter estimates and best linear unbiased predictions are derived from the geostatistical mixed linear model under two different residual error assumptions; i.e., strictly uncorrelated (SU) residuals and effectively uncorrelated (EU) residuals. The theoretically optimal (best linear unbiased) and computable (linear unbiased) predictions and variance estimates derived under the EU error assumption are examined in detail. Statistical tests for detecting spatial correlation in LR model residuals are also reviewed, in addition to three LR model validation tests derived from classical linear modeling theory. Two case studies are presented that highlight and demonstrate the various parameter estimation, response variable prediction and model validation techniques discussed in this article.

The constant capacitance model, a chemical surface complexation model, was applied to selenite, Se(IV), adsorption on 36 soils selected for variation in soil chemical properties. The constant capacitance model was able to fit Se(IV) adsorption by optimizing one monodentate Se(IV) surface complexation constant and the surface protonation constant. A general regression model was developed for predicting these surface complexation constants for Se(IV) from easily measured soil chemical characteristics. These chemical properties were inorganic carbon content, organic carbon content, iron oxide content, aluminum oxide content, and surface area. The prediction equations were used to obtain values for the surface complexation constants for four additional soils, thereby providing a completely independent evaluation of the ability of the constant capacitance model to describe Se(IV) adsorption. The model's ability to predict Se(IV) adsorption was quantitative on one soil and semi-quantitative on three soils. Incorporation of these prediction equations into chemical speciation-transport models will allow simulation of soil solution Se(IV) concentrations under diverse non-calcareous agricultural and environmental conditions without the requirement of soil specific adsorption data and subsequent parameter optimization.

Existing irrigation water quality criteria related to sodium and salinity are based primarily on short-term laboratory column studies. These earlier studies measured infiltration or hydraulic conductivity of disturbed soil under continuously saturated conditions. Application of these standards to field conditions is uncertain, as it does not account for wetting and drying conditions, formation of crusts and impact of rain events, etc. In this study we examine water infiltration into loam and clay soils irrigated at EC = 1.0 and 2.0 dS m−1 at SAR of 2, 4, 6, 8, and 10 in a management system with alternating (simulated) rain and irrigation and drying between irrigations. For the loam soil the adverse impacts of sodium on infiltration were evident above SAR 2, while for the clay soil adverse impacts occurred above SAR 4. In both soils the SAR behavior was similar for both EC values, 1.0 and 2.0 dS m−1, indicating that in this range, EC did not affect infiltration. Reductions in infiltration were evident during both the irrigation and rain events, with lower infiltration, as expected during the rain simulations. These results show a greater sensitivity to SAR than indicated in laboratory column studies and existing water quality criteria.

Remotely sensed imagery provides contiguous spatial coverage of a field and can be used as a surrogate to measure crop and soil attributes. Empirical regression models are often used to convert imagery to attribute maps, when an a priori linear relationship can be assumed to exist between the imagery and ground attributes. In this study, we used the response surface approach incorporated in the ECe Sampling, Assessment, and Prediction (ESAP) software to create ground sampling designs from input imagery in order to develop regression equations for predicting crop height and width attributes in a 3.4-ha cotton field. We examined both the reliability of this model-based sampling approach as well as the validity of the assumed linear models using multiple-date imagery and sample data collected from a 3-year remote sensing experiment. Predictions of height and width from regressions between the imagery and ground sampling at the calibration locations gave coefficients of determination for height ranging from 0.34 to 0.90 and for width, 0.30 to 0.94. All regression models but one were statistically significant at the α = 0.01 level. To test the reliability of the sampling approach, the regression models developed during the first year were used to predict additional crop height and width attributes at a randomly chosen set of validation sites. Multiple statistical tests indicated that these predictions were both unbiased and within the specified precision of the estimated regression equations. This regression-based directed sampling and estimation method requires fewer points than co-kriging to develop reliable imagery-crop attribute relationships, and thus is potentially less expensive. We hypothesize that other variables such as crop nitrogen might also be accurately predicted using this approach as long as the crop attribute and spectral index meet the model assumptions. Maps of crop attributes and/or soil properties could be used by farmer consultants to schedule variable-rate applications of chemicals or as inputs to crop simulation models providing a spatial extension to their time-series nature. Published by Elsevier B.V.

In an effort to elucidate the physiological processes involved in cowpea differential growth response of four major USA cowpea cultivars (CB5, CB27, 8517 and 7964) to increasing salinity, we investigated the effect of salinity on leaf gas exchange of net photosynthetic rate per unit leafmass(Pnm)andperunitleafarea(Pna),andstomatalconductance(gs)ofthefourcowpeacultivars.Theexperimentwassetupasastandard split-plot design in which cowpea plants were grown in greenhouse sand tanks irrigated with nutrient solutions. Seven salinities ranging from 2.6 to 20.5 dS m � 1 were constructed based on Colorado River water salt composition with NaCl, CaCl2 and MgSO4 as the salinization salts. Light-saturated Pnm, Pna and gs of fully expanded trifoliage were examined at the vegetative growth and flowering stages, and the data were analyzed using a split-plot analysis of variance (ANOVA) model. We found a highly significant (P � 0.0001) reduction of Pnm, Pna and gs due to salinity. The responses of Pnm, Pna and gs to salinity could be further described by a general model of log(y )= a1 + a2x + a3x 2 ,w herey represents either Pnm, Pna, or gs; a1, a2 and a3, empirical constants; x, salinity. We found that Pnm was more sensitive to salinity than Pna. Additionally, we found that increasing stomatal closure with increasing salinity might limit Pnm or Pna. While we did not find any significant difference (P > 0.05) of Pnm and Pna among the four cultivars, we did find a significant difference (P � 0.05) in gs. No significant

Characterizing spatial and temporal variability of soil properties at field and landscape scales is tremendously important for a variety of agronomic and environmental concerns including solute transport modeling of non-point source pollutants in the vadose zone, site-specific crop management, and soil quality assessment, to mention a few. Currently, there is a global need to develop tools that evaluate the overall quality of soil to determine the effectiveness and sustainability of farm-management practices. The knowledge now exists for characterizing the spatial variability of soil quality with non-invasive geophysical measurements of apparent soil electrical conductivity (ECa) using mobile GPS-based systems. The objective of this research was to evaluate ECa-directed soil sampling as a means for monitoring management-induced spatio–temporal changes in soil quality. Appraisal was made of a specific management practice, the reuse of irrigation drainage water applied to a saline–sodic soil in central California. A soil quality assessment study was conducted on a 32.4-ha saline–sodic field comprised of 8 rectangular paddocks in California’s San Joaquin Valley from August 1999 to April 2002. The study evaluated the spatio–temporal changes that had occurred as a result of irrigation with drainage water over that time period. Using geospatial electromagnetic induction (EMI) measurements of ECa and a spatial response surface sampling design, 40 sites were selected that reflected the spatial variability of the ECa measurements. Duplicate samples were taken at eight selected sites (one randomly selected site from each paddock) to study local-scale variability. At each site soil-core samples were taken at 0.3-m intervals to a depth of 1.2 m and analyzed for

Over the last several years, there has been increasing interest in amending the soil using cover crops, especially in desert agriculture. One cover crop of interest in the desert Coachella Valley of California is cowpea [Vigna unguiculata (L.) Walp.]. Cowpea is particularly useful in that as an excellent cover crop, fixing abundant amounts of nitrogen which can reduce fertilizer costs. However, soil salinity problems are of increasing concern in the Coachella Valley of California where the Colorado River water is a major source of irrigation water. Unfortunately, little information is available on the response of cowpea growth to salt stress. Thus, we investigated the growth response of 12 major cowpea cultivars (`CB5', `CB27', `CB46', `IT89KD-288', `IT93K-503-1', `Iron Clay', `Speckled Purple Hall', `UCR 134', `UCR 671', `UCR 730', `8517', and `7964') to increasing salinity levels. The experiment was set up as a standard Split Plot design. Seven salinity levels ranging from 2.6 to 20.1 dS·m–1 were constructed, based on Colorado River water salt composition, to have NaCl, CaCl2 and MgSO4 as the salinization salts. The osmotic potential ranged from –0.075 to –0.82 MPa. Salt stress began 7 days after planting by adding the salts into irrigating nutrient solution and ended after 5 consecutive days. The plants were harvested during flowering period for biomass measurement (53 days after planting). Data analysis using SAS analysis of variance indicated that the salinity in the range between 2.6 and 20.1 dS·m–1 significantly reduced leaf area, leaf dry weight, stem dry weight and root dry weight (P ≤ 0.05). We applied the data to a salt-tolerance model, log(Y) = a1 + a2X + a3X2, where Y represents biomass, a1, a2 and a3 are empirical constants, and X represents salinity, and found that the model accounted for 99%, 97%, 96%, 99%, and 96% of salt effect for cowpea shoot, leaf area, leaf dry weight, stem dry weight and root dry weight, respectively. We also found significant differences (P ≤ 0.05) of each biomass parameter among the 12 cultivars and obtained different sets of the empirical constants to quantitatively describe the response of each biomass parameter to salinity for individual cowpea cultivars. Since a significant salt × cultivar interaction effect (P ≤ 0.05) was found on leaf area and leaf dry weight, we concluded that salt tolerance differences exist among the tested cultivars.

Soybean is an important crop worldwide. Soybean cultivars differ in their sensitivity to soil salinity and herbicide damage. In these experiments, we examined the impact of salinization an...

An assessment of field-scale variation and the characterization of correlated crop response are the first steps in evaluating the potential for variable-rate technologies and other techniques used in site-specific management (SSM). The responses of sugar beets to salinity and to residual and applied soil N were studied at sites in California's Imperial and San Joaquin Valleys to evaluate the potential of electromagnetic induction (EMI) techniques for SSM. Electromagnetic induction was used to create geo-referenced assessments of apparent soil electrical conductivity (EC"a) and correlated soil properties in salt-affected fields in the Imperial (IV) and San Joaquin (SJV) Valleys. Soils at the two sites were primarily fine, smectitic (calcareous) thermic, vertic Torifluvents and Fluvequents, respectively. Two crops were grown in the IV and one in the SJV. Root and gross sugar yields were evaluated at field scale using a yield monitor and in subplots centered on soil sampling locations chosen using ESAP-95 (v. 2.01) software that were harvested by hand. Average subplot root yields differed from field-scale averages derived from the yield monitor by less than 4% for all three crops. In the IV field, average salinity increased with depth, indicating leaching of salts. The natural logs of electrical conductivity of the saturation paste extract (EC"e) and saturation percentage (SP) were strongly correlated with measured EC"a (r=0.97 and r=0.86, respectively) and nitrate was moderately correlated (r=0.55). Root and sugar yields declined at the higher salinity levels from 18.0 to 8.0mgha^-^1. The SJV site was poorly drained and leaching was not apparent. EC"e was strongly correlated with EC"a (r=0.94), but SP was not (r=0.20). Gross sugar yields apparently were influenced by SP rather than by EC"e and varied from 2.9 to 14.1mgha^-^1. Where crop growth and yield are influenced by salinity, EMI is useful for estimating optimum fertilizer N application rates and for identifying areas of the field that will have unprofitable yields. Irrigation might be withheld from these areas and the water saved for other beneficial uses.

Effective management of the soil resource requires basic information about the spatial distribution of various attributes. A method widely used for providing spatial information is a combination of sampling strategies and geostatistics. However, geostatistical methods demand intensive sampling that is expensive and time-consuming. Geophysical methods, such as electromagnetic (EM) induction, offer an alternative, more robust, and less expensive approach to gather soil information. In this study, a methodology is outlined for mapping spatial distribution of bulk soil average clay content to a depth of 7m using EM measurements. The study was conducted southeast of Trangie in the lower Macquarie valley of New South Wales, Australia. Two EM sensors were employed. To provide deep bulk soil EM measurements, an EM34 was used in the horizontal dipole mode at coil configurations of 10, 20, and 40m (respectively, designated EM34-10, EM34-20, and EM34-40). For shallower bulk soil EM measurements, an EM38 was used in vertical and horizontal modes (EM38-v and EM38-h, respectively). A total of 755 locations were measured on a grid of approximately 0.5km. In order to classify the EM34 data into broad physiographic and hydrogeological units, fuzzy k-means (FKM) classification was applied. By iterating fuzziness exponents (@f), input parameters, and evaluating various clustering performance indices, we found optimal classification when @f=1.5 and number of classes (c)=4. Fuzzy k-means with extragrade (FKMe) classification was subsequently undertaken to account for Extragrades (i.e., outliers in the data). A spatial response surface sampling (SRSS) design was invoked to select sampling sites within each of the four regular and one Extragrade class. From 40 calibration holes (i.e., 8 from each class), soil samples were taken at 1m intervals from the soil surface to a depth of 7m. Each sample was analyzed for clay content then averaged for a 0-7m clay content (%clay) for each hole. In order to predict the %clay across the landscape, a hierarchical spatial regression model (HSR) was developed using a composite signal variable [i.e., ln(EM34-10)+ln(EM34-40)+ln(EM38-h)] and first-order trend surface components (i.e., Easting and Northing). The final map of %clay generally reflects the known surface clay content and provides information about the spatial distribution of subsurface %clay variability. We conclude that although the FKMe analysis did not result in an improved calibration within each class, the approach delineated similar clusters of signal readings that were useful in providing a framework to determine a soil sampling design that accounted for variations in physiography and hydrogeology.

The field-scale application of apparent soil electrical conductivity (EC"a) to agriculture has its origin in the measurement of soil salinity, which is an arid-zone problem associated with irrigated agricultural land and with areas having shallow water tables. Apparent soil electrical conductivity is influenced by a combination of physico-chemical properties including soluble salts, clay content and mineralogy, soil water content, bulk density, organic matter, and soil temperature; consequently, measurements of EC"a have been used at field scales to map the spatial variation of several edaphic properties: soil salinity, clay content or depth to clay-rich layers, soil water content, the depth of flood deposited sands, and organic matter. In addition, EC"a has been used at field scales to determine a variety of anthropogenic properties: leaching fraction, irrigation and drainage patterns, and compaction patterns due to farm machinery. Since its early agricultural use as a means of measuring soil salinity, the agricultural application of EC"a has evolved into a widely accepted means of establishing the spatial variability of several soil physico-chemical properties that influence the EC"a measurement. Apparent soil electrical conductivity is a quick, reliable, easy-to-take soil measurement that often, but not always, relates to crop yield. For these reasons, the measurement of EC"a is among the most frequently used tools in precision agriculture research for the spatio-temporal characterization of edaphic and anthropogenic properties that influence crop yield. It is the objective of this paper to provide a review of the development and use of EC"a measurements for agricultural purposes, particularly from a perspective of precision agriculture applications. Background information is presented to provide the reader with (i) an understanding of the basic theories and principles of the EC"a measurement, (ii) an overview of various EC"a measurement techniques, (iii) applications of EC"a measurements in agriculture, particularly site-specific crop management, (iv) guidelines for conducting an EC"a survey, and (v) current trends and future developments in the application of EC"a to precision agriculture. Unquestionably, EC"a is an invaluable agricultural tool that provides spatial information for soil quality assessment and precision agriculture applications including the delineation of site-specific management units. Technologies such as geo-referenced EC"a measurement techniques have brought precision agriculture from a 1980's concept to a promising tool for achieving sustainable agriculture.

Geospatial measurements of apparent soil electrical conductivity (EC a) are recognized as a means of characterizing soil spatial variability at field and landscape scales. However, inconsistencies in the measurement and interpretation of field- and landscape-scale geospatial EC a measurements have resulted in data sets that are unreliable and/or incompatible. These inconsistencies are, in part, a consequence of the lack of ECa-survey protocols that provide standardized guidelines to assure reliability, consistency, and compatibility. It is the objective of this paper to apply EC a-survey protocols to a soil quality assessment to demonstrate their utility in characterizing spatial variability. The soil quality assessment was conducted on a 32.4-ha field on the westside of central California’s San Joaquin Valley where a mobile electromagnetic induction (EM) survey was performed following outlined protocols. The EM survey consisted of ECa measurements taken at 22,177 locations in April 2002. A responsesurface sampling design was used to identify 40 sites where soil-core samples were taken at 0.3-m increments to a depth of 1.2 m. Duplicate samples were taken at eight sites to evaluate the local-scale variability. Soil samples were analyzed for a variety of physico-chemical properties associated with soil quality for an arid zone soil. Analysis characterized the soil as montmorillonitic, saline, and sodic Abbreviations: ANOVA, analysis of variance; CEC, cation exchange capacity; CV, coefficient of variation; ECa, apparent soil electrical conductivity; EC e, electrical conductivity of the saturation extract; EM, electromagnetic induction; EMh, electromagnetic induction measurement in the horizontal coil-mode configuration; EM v, electromagnetic induction measurement in the vertical coil-mode configuration; ESAP, EC e sampling, assessment and prediction software; ESP, exchangeable sodium percentage; GIS, geographic information system; GPS, global positioning system; IDW, inverse distance weighting; Ks, saturated hydraulic conductivity; SAR, sodium adsorption ratio; SP, saturation percentage; WSJV, western San Joaquin Valley

Sustaining irrigated agricultural production systems in semi-arid and arid regions requires consideration of with saline and sodic soil conditions. The spatial variability of these conditions makes soil reclamation an ideal practice in which to apply site-specific management (SSM). We discuss an application of SSM in which preliminary measurements of apparent soil electrical conductivity (EC"a), supplemented by EC"a-directed soil sampling, are used to construct a GIS map of salinity zones on which site-specific amendment application can be based. Our primary objective was to develop a field-implementable methodology for site-specific soil amendment application. The focus of the program was on cotton production, although the method should be applicable to other crops. A second objective was to establish experiments to test the effectiveness of this program. Evaluating the effectiveness of a sprinkler application for the first irrigation was incorporated as a part of this program in hopes of reducing variability due to poor germination. This study consisted of two commercial fields located in the San Joaquin Valley of California. Based on the consideration that EC"a is directly related to electrical conductivity from soil saturation paste extract (EC"e) for known saline-sodic conditions, we developed a four-step method for site-specific salinity management in commercial fields. The steps included the following: (1) generation of an EC"a map; (2) directed soil sampling for EC"e; (3) determination of the estimated amendment requirement as a function of location in the field; and (4) integration of the individual amendment requirements into a practical spatial pattern for amendment application. Cotton yield monitors were utilized to indicate spatial yield variation throughout the replicated plots instead of aggregated yield values. Because of the high levels of variability in these commercial fields and the time required for amendments to have a substantial effect, we can provide only short-term results of these experiments. In both of the experiments no significant differences existed between the irrigation and amendment treatments (p>0.05), but increased yield trends supported the benefit of sprinkler application of the first irrigation. A Scheffe test of the cotton yield monitor data indicated significant yield differences between in-plot treatment zones.

In many applied precision farming applications, remotely sensed survey data are collected specifically because these data correlate well with some soil property of interest. Additionally, a general model for the functional relationship between the soil property and the sensor data is often known a priori, but the exact parameter estimates associated with the model must still be determined via some type of site-specific sampling strategy. The main objective of this paper is to present an objective sampling and simplified modeling strategy for predicting soil property information from such spatially referenced sensor data. Some common types of spatial linear prediction models and linear geostatistical models are reviewed, and the assumptions needed to reduce these more complicated models to a spatially referenced, ordinary linear regression model (LR) are discussed. Next, a model-based sampling strategy for estimating an ordinary linear regression model in the spatial setting is described. This sampling strategy incorporates a traditional response surface design into an iterative, space-filling type algorithm for purposes of selecting sample site locations that are (i) nearly optimal with respect to matching the selected response surface design levels and (ii) physically separated far apart as possible to ensure the best chance that the independent error assumption is adequately met. This strategy can in principal be used to select a minimal number of optimal sample site locations that satisfy the residual independence assumptions in the ordinary model. A detailed case study of a salinity survey using electromagnetic induction (EM) and four-electrode sensor data is then presented. These case study results confirm that the sampling strategy was highly effective at ensuring efficient regression model parameter estimates and a reliable salinity prediction map. An additional simulation study confirmed the effectiveness of this model-based strategy over a more traditional simple random sampling strategy with respect to four regression model design criteria. Under the right conditions, this methodology should be applicable to many types of precision farming survey applications where soil property/sensor data prediction models need to be fitted using only a limited number of soil samples.

Soil spatial variability refers to the variation in soil physical, chemical, mineralogical, and biological properties across the landscape and through the soil profile. The significance of soil spatial variability lies in its direct or indirect influence on any landscape-scale soil-related issue ranging from solute transport of non-point source pollution to precision agriculture. A general discussion of the origin, need for characterization, and measurement methods is presented with greatest emphasis placed on the use of apparent soil electrical conductivity (ECa) to characterize spatial variability. The characterization of spatial variability with geospatial ECa measurements is based on the correlation of ECa with various physicochemical properties and the use of ECa-directed soil sampling designs. Edaphic factors influencing ECa measurements, mobile ECa measurement equipment, ECa-directed sampling designs, guidelines for conducting an ECa survey, and limitations and precautions are discussed. The discussion is intended to provide a basic understanding of spatial variability and its characterization with geospatial ECa measurements to a general scientific audience. Keywords: spatial variability; apparent soil electrical conductivity; electromagnetic induction; electrical resistivity; spatial heterogeneity; landscape-scale processes; soil quality; solute transport modeling; precision agriculture; salinity; water content; texture

Soil fumigation is useful for controlling soil-borne pests and diseases in high-cash-value crops. Fumigants are highly volatile, and approaches to reduce atmospheric emissions are required to protect human and environmental health. Application of fumigants through drip irrigation has been proposed as a means to decrease fumigant emissions, improve fumigant distribution in soil, and minimize worker exposure. These experiments were conducted to investigate the effect of the configuration of the drip system on the volatilization and distribution of the fumigants 1,3-dichloropropene (1,3-D), propargyl bromide (PrBr), and methyl isothiocyanate (MITC) in bedded systems. Results indicated that changing the drip emitter spacing and using multiple drip lines in each bed had little effect on the emissions and distribution of any fumigant. Increasing the depth of application from 15 to 30 cm reduced volatilization of MITC by approximately 20 to >90%; emissions were reduced due to a decrease in the flux from the bed top, and deeper injection did not change the amount of fumigant volatilized from the bed side slope and furrow. Increasing the application depth resulted in a slight decrease in the rate of fumigant dissipation in soil, indicating the potential for some improvement in pest-control efficacy with deeper application.

Measurement of soil dielectric properties, e are widely used to estimate water content in soils from remote sensing and from in situ soil sensors such as time domain reflectometry (TDR). The mineral permittivity also plays an important role in geochemical dissolution and precipitation. Models used to estimate water content from soils often assume a value of 5 for the mineral permittivity e s . However, calcite (CaCO 3 ), a major constituent of some arid and semi-arid soils, has a permittivity of 8 to 9, nearly twice the permittivity of quartz (e s = 4.6). We studied four soils, with micaceous mineralogy, but with two soils having approximately 40% calcite. We also measured the permittivity of Iceland Spar calcite (e s = 9.1) and a microcrystalline calcite (e s = 8.3), and use atomistic modeling to account for differences in permittivity based on the crystal density. We found permittivities for our soils to be in the range of 5.8 to 6.6, higher calcite contents resulting in increased permittivity. The estimated permittivity of the calcite in the soils was 7.4 to 7.9, lower than the highly crystalline samples. We estimate, for a soil with a porosity of 0.5 that assuming a permittivity of 5 instead of 6.6 will result in an overestimation of water content of about 1% at saturation. This demonstrates that a large quantity of pedogenic calcite (40%) in soil is unlikely to cause substantial error in the determination of water content using standard calibration equations. However, the lower dielectric permittivity predicted for pedogenic calcite may have consequences for the interpretation and understanding of geochemical processes.

Prediction of B adsorption and transport has required detailed studies of B adsorption and subsequent determination of model parameters. In this study we tested a general regression model previously developed for predicting soil B surface complexation constants from easily measured soil chemical characteristics. The constant capacitance model, a chemical surface complexation model, was applied to B adsorption isotherms on 22 soils from the A and B horizons of 16 soil series from Oklahoma and Iowa, The measured chemical properties were surface area, organic C (OC) content, inorganic C (IOC) content, and Al oxide content. The prediction equations of Goldberg et al. (2000) were used to obtain constant capacitance model values for B surface complexation constants thereby providing a completely independent evaluation of the ability of the constant capacitance model to describe B adsorption. The model was well able to predict B adsorption isotherms on the majority of the soils. The regression model was used to obtain the parameters for the constant capacitance model. Then the constant capacitance model was used to predict the soil specific B adsorption. This is in contrast to regression models that fit adsorption of a series of soils. The distinction is that using the combined regression equations and the constant capacitance model only soil properties and not adsorption are needed to predict soil specific B adsorption data. The prediction equations developed from a set of soils primarily from California, were able to predict B adsorption on a set of soils from different parts of the country. This result suggests wide applicability of the model prediction equations developed previously, for describing B adsorption both as a function of solution B concentration and solution pH.

The EM-38 is a noninvasive instrument, commonly used for monitoring salinity, mapping bulk soil properties, and evaluating soil nutrient status. Users in the Southwest USA have observed as much as 20% “drift” in the measurement of bulk soil electrical conductivity (ECₐ) with this instrument. This drift has usually been ignored or compensated for by statistical procedures. We performed laboratory and field experiments to determine if the drift is due to calibration instability of the instrument or to heating of the instrument by the sun. In laboratory experiments, after a warm-up period, the instrument provided constant readings in the range 25 to 40°C; above 40°C the response of the instrument was unpredictable. In field experiments, where we placed the EM-38 in a fixed location we observed an unexpected response at air temperatures below 40°C. Temperature sensors in different locations on the instrument demonstrated that temperature differences between the instrument's transmitting and receiving coils and the control panel (CP) were as great as 20°C. As the instrument is temperature compensated from this CP, erroneous compensation occurred when the instrument was placed in direct sunlight. In this study, we demonstrate that differential heating of the EM-38 is one cause of drift and erroneous bulk electrical conductivity measurement; shading the instrument substantially reduced this problem, effectively extending the reliable working temperature range by minimizing drift.

Greenhouse experiments were conducted to determine the effect of salinity, imazethapyr, and chlorimuron on weed growth. Five species, barnyardgrass, common cocklebur, ivyleaf morningglory, common purslane, and yellow nutsedge, were grown in potting soil and irrigated with nonsaline (EC ∼ 2 dS m−1) or sulfate-dominated saline (EC ∼ 7 dS m−1) nutrient solution. Plants were treated after emergence with imazethapyr (Pursuit formulation) at 70 g ae ha−1 or chlorimuron ethyl (Classic formulation) at 8.8 g ai ha−1. Results indicated that irrigation with saline water had no overall effect on the growth or survival of most tested weed species. Growth of yellow nutsedge (maximum height and stem diameter) was reduced for plants irrigated with saline water. Observed growth and survival trends in saline and nonsaline treatments were consistent with information on the herbicide label. Complete control of common purslane was not achieved by either chlorimuron or imazethapyr. Growth and survival of ivyleaf morni...

Farm managers are becoming increasingly aware of the spatial variability in crop production with the growing availability of yield monitors. Often this variability can be related to differences in soil properties (e.g., texture, organic matter, salinity levels, and nutrient status) within the field. To develop management approaches to address this variability, high spatial resolution soil property maps are often needed. Some soil properties have been related directly to a soil spectral response, or inferred based on remotely sensed measurements of crop canopies, including soil texture, nitrogen level, organic matter content, and salinity status. While many studies have obtained promising results, several interfering factors can limit approaches solely based on spectral response, including tillage conditions and crop residue. A number of different ground-based sensors have been used to rapidly assess soil properties “on the go” (e.g., sensor mounted on a tractor and data mapped with coincident position information) and the data from these sensors compliment image-based data. On-the-go sensors have been developed to rapidly map soil organic matter content, electrical conductivity, nitrate content, and compaction. Model and statistical methods show promise to integrate these groundand image-based data sources to maximize the information from each source for soil property mapping.

Due in large measure to the prodigious research efforts of Rhoades and his colleagues at the George E. Brown, Jr., Salinity Laboratory over the past two decades, soil electrical conductivity (EC), measured using electrical resistivity and electromagnetic induction (EM), is among the most useful and easily obtained spatial properties of soil that influences crop productivity. As a result, soil EC has become one of the most frequently used measurements to characterize field variability for application to precision agriculture. The value of spatial measurements of soil EC to precision agriculture is widely acknowledged, but soil EC is still often misunderstood and misinterpreted. To help clarify misconceptions, a general overview of the application of soil EC to precision agriculture is presented. The following areas are discussed with particular emphasis on spatial EC measurements: a brief history of the measurement of soil salinity with EC, the basic theories and principles of the soil EC measurement and what it actually measures, an overview of the measurement of soil salinity with various EC measurement techniques and equipment (specifically, electrical resistivity with the Wenner array and EM), examples of spatial EC surveys and their interpretation, applications and value of spatial measurements of soil EC to precision agriculture, and current and future developments. Precision agriculture is an outgrowth of technological developments, such as the soil EC measurement, which facilitate a spatial understanding of soil-water-plant relationships. The future of precision agriculture rests on the reliability, reproducibility, and understanding of these technologies.

Crop yield inconsistently correlates with apparent soil electrical conductivity (EC a ) because of the influence of soil properties (e.g., salinity, water content, texture, etc.) that may or may not influence yield within a particular field and because of a temporal component of yield variability that is poorly captured by a state variable such as EC a . Nevertheless, in instances where yield correlates with EC., maps of EC, are useful for devising soil sampling schemes to identify soil properties influencing yield within a fleld. A west side San Joaquin Valley field (32A ha) was used to demonstrate how spatial distributions of EC a can guide a soil sample design to determine the soil properties influencing seed cotton (Gossypium hirsutum L.; 'MAXXA' variety) yield. Soil sample sites were selected with a statistical sample design utilizing spatial EC, measurements. Statistical results are presented from correlation and regression analyses between cotton yield and the properties of pH, B, NO 3 -N, Cl - , salinity, leaching fraction (LF), gravimetric water content, bulk density, percentage clay, and saturation percentage. Correlation coefficients of -0.01, 0.50, -0.03, 0.25, 0.53, -0.49, 0.42, -0.29, 0.36, and 0.38, respectively, were determined. A site-specific response model of cotton yield was developed based on ordinary least squares regression analysis and adjusted for spatial autocorrelation using maximum likelihood. The response model indicated that salinity, plant-available water, LF, and pH were the most significant soil properties influencing cotton yield at the study site. The correlations and response model provide valuable information for site-specific management.

The correlation structure between apparent soil electrical conductivity (EC a ) and various soil properties can often appear radically dissimilar in different field surveys. Ideally, some type of methodology for survey data validation should be developed that can predict the expected correlation structure between EC, survey data and various soil properties, given information about the soil properties themselves. In this paper, we review an existing model for EC a and hypothesize that this model can be used to accurately predict the expected correlation structure between EC, data and multiple soil properties of interest (such as soil salinity, saturation-paste percentage, and soil water content). Our objective is twofold: (i) to demonstrate how this model can be employed to produce the expected correlation structure and (ii) to extend this EC, model to handle survey data collected under low water content situations by dynamically adjusting the model's assumed water content function. This adjustment can be estimated using acquired EC, signal and soil sample data, and its statistical significance can be determined for each specific survey situation. We demonstrate both of these techniques using acquired electromagnetic induction signal data and measured soil properties of interest from 12 different field salinity surveys performed in California and Colorado and in Alberta, Canada. Results from these 12 surveys suggest that the ordinary model is able to accurately predict the expected correlation structure between conductivity and soil property when the water content is near field capacity and that the dynamically adjusted model is able to substantially improve the accuracy of the predicted correlation structure when the water content is significantly below field capacity.

Depth of standing water in rice paddy fields is an important agronomic parameter in the management of irrigation-related salinity problems. It was hypothesized that reductions in the yield of rice under salinity stress can be ameliorated by adjusting the water depth. This study was designed to determine the interactive effects of salinity and water depth on seedling establishment and grain yield in rice. Plants were grown in a greenhouse and irrigated with nutrient solutions amended with NaCl and CaCl2 (5:1 molar concentrations). Treatments were three salt levels with electrical conductivities at 0.9, 3.3 and 6.0 dS m−1 and six water depths at 4, 7, 10, 13, 16 and 20 cm. The effects of both salinity and water depth were significant on plant growth and yield. However, there was no interaction between the effects of salinity and water depth. Reductions in seedling establishment and grain yield with increases of salinity and water depth resulted from a simple combination of the two different stresses on plants. Highly significant negative correlations were identified between water depth and seedling establishment and also between water depth and grain yield when data were combined across salt levels. Generally, plants performed better with respect to seedling establishment and grain yield in shallow water (i.e. 10 cm). Under salt stress, the effect of water depth was significant for panicle number, but not significant for panicle weight. The loss of grain yield under salt stress with the increases of water depth was mainly due to reduction in fertile tiller number. We suggest that water depth be lowered during the initiation and growth of productive tillers. However, the practice by lowering water depth must be incorporated with appropriate field management such as the increase of irrigation frequency, precision leveling, and effective weed control methods.

Management options for reducing drainage water volumes on the west side of the San Joaquin Valley of California, such as reuse of saline drainage water and water table control, have the potential to adversely impact crop yields due to a build up in soil solution boron concentration. An earlier experiment had shown that extrapolation of B soil tests to field conditions provided poor predictability of B content of melons despite statistically significant relationships. Consequently, three tests for extractable soil B were evaluated for their ability to predict conditions of potential B toxicity in melons grown under controlled conditions. Melons were grown for 95 days in two consecutive years in containers of Lillis soil (very-fine, smectitic, thermic Halic Haploxerert) that had been pretreated with solutions containing B concentrations as great as 5.3 mmol L −1 . Extractable soil B was determined using ammonium acetate, DTPA-sorbitol, and a 1:1 aqueous soil extract at the beginning and end of the experiment. The B treatments caused various deleterious effects on melon growth and development. Fresh and dry plant matter decreased significantly with increasing B concentrations, while B concentration of plant leaves, stems, and fruits increased significantly with increasing B. The number of days to first flowering was significantly delayed from 35 days at B treatments 3 mmol L −1 . Fruit set was completely inhibited at the highest B treatment of 5.3 mmol L −1 . Plant analysis revealed a highly significant relationship between soil extract B obtained with all three extractants and leaf, stem, and fruit B content. Correlation coefficients for plant stems and fruits were much higher than for plant leaves. Correlation coefficients for all soil tests were almost equivalent, although the highest values were obtained for the DTPA-sorbitol extract indicating the greatest predictive capability. The soil tests were well able to predict B damage to melons in a container experiment.

Determining the relationship between soil B and crop B content can help predict when crops will respond to B fertilizer and when B toxicity may be expected. Such a relationship can then be used to make fertilizer recommendations or to flag conditions of potential B toxicity. Soil samples were obtained from 65 sites located in the Broadview Water District in the San Joaquin Valley of California. A diverse set of extractants was evaluated including: hot water-soluble, 1:1 soil:distilled water and 1:2 soil:distilled water, ammonium acetate, calcium chloride-mannitol, and DTPA-sorbitol extracts. Soil extract B values were correlated significantly with various B reactive soil constituents, including aluminum and iron oxide, clay, organic matter, and calcium carbonate content. The 1:1 water extract B was highly significantly correlated (99% level) with other measures of extractable B used in the study. Extractants were compared on soil samples collected from six depths at 65 field sites in the San Joaquin Valley of California that were cropped to alfalfa, melons, and cotton. Boron concentrations of whole plants and composites of 10 leaves were determined. Plant sampling occurred at the time of soil sampling for the alfalfa. Cotton and melons were sampled at flowering and prior to fruit set, the recommended growth stages, respectively, for tissue sampling, and 6 weeks thereafter. Five weeks later the cotton was sampled a third time. Significant correlations (95% level) between extractable soil B and plant B were found for melons and cotton but not for alfalfa. Correlation coefficients for the ammonium acetate, DTPA-sorbitol, and 1:1 water extract were not statistically significantly different (95% level). Although significant correlations (95% level) were obtained, the equations provided relatively poor predictive capability. These results illustrate the difficulty of predicting plant B content based on soil B analyses from a single soil sampling.

Noninvasive electromagnetic (EM) induction techniques are used for salinity monitoring of agricultural lands and contaminant detection in soils and shallow aquifers. This study has four objectives. The first objective is to summarize an earlier linear model of the response of the EM38 ground conductivity meter and to discuss a more accurate nonlinear response model. The second objective is to verify experimentally whether the linear and nonlinear models derived for homogeneous media are valid in heterogeneous soil profiles. The third objective is to present an inverse procedure that combines the nonlinear model with Tikhonov regularization. The fourth objective is the experimental verification of inverse procedures with the linear and nonlinear models for inversion of soil conductivity profiles using aboveground electromagnetic induction measurements on fourteen saline Californian soil profiles. The linear and nonlinear models derived for homogeneous media are indeed valid in heterogeneous soil profiles. However, since the errors of the linear model are approximately double those of the nonlinear model, the latter is the preferred one. A small difference was found in the errors of the inverse procedures between the linear and nonlinear models. In this study, the inverse procedures with the linear model and with the nonlinear model produce equally good solutions at EM38 measurements below 100 mS m -1 , while at higher electrical conductivities the inverse procedure with the nonlinear model appears to yield slightly better results. The inverse procedure with the linear model is preferred for all conductivities since it needs considerably less computer resources.

Though more costly than petroleum-based fuels and a minor component of overall military fuel sources, biofuels are nonetheless strategically valuable to the military because of intentional reliance on multiple, reliable, secure fuel sources. Significant reduction in oilseed biofuel cost occurs when grown on marginally productive saline-sodic soils plentiful in California’s San Joaquin Valley (SJV). The objective is to evaluate the feasibility of oilseed production on marginal soils in the SJV to support a 115 ML yr−1 biofuel conversion facility. The feasibility evaluation involves: (1) development of an Ida Gold mustard oilseed yield model for marginal soils; (2) identification of marginally productive soils; (3) development of a spatial database of edaphic factors influencing oilseed yield and (4) performance of Monte Carlo simulations showing potential biofuel production on marginally productive SJV soils. The model indicates oilseed yield is related to boron, salinity, leaching fraction, and water content at field capacity. Monte Carlo simulations for the entire SJV fit a shifted gamma probability density function: Q = 68.986 + gamma (6.134,5.285), where Q is biofuel production in ML yr−1. The shifted gamma cumulative density function indicates a 0.15–0.17 probability of meeting the target biofuel-production level of 115 ML yr−1, making adequate biofuel production unlikely.

Differential sensitivity during growth stages is one of the major issues in the management of saline water for irrigation. This study was designed to analyze the effects of salinity on plant growth and yield components of rice by composing 20-day periods of salinization at different growth stages. Plants were grown in sand tanks in a greenhouse and irrigated with nutrient solutions. Treatments were three levels of salinity with electrical conductivities at 1.8, 3.2 and 4.6 dS m−1 and five timing treatments. Plants were salinized on the day of seeding, 1-leaf, 3-leaf, panicle initiation (PI), and booting stages, respectively, and stress was relieved after 20 days in each timing treatment. Salinity-induced reductions in shoot dry weights of plants harvested before PI were significant, but there were no significant differences among timing treatments. Reduction in shoot dry weight of plants harvested at seed maturity was significant only when plants were salinized for a 20-day duration before booting, but not after booting. Reduction in tiller number per plant was significant only when plants were salinized for a 20-day duration before PI. The reductions in spikelets per panicle and seed weight per panicle were most pronounced when plants were stressed between the 3-leaf and PI stages or between PI and booting stages and minor when stressed at the other stages. A 20-day period between 3-leaf and PI stages was most sensitive to salinity in terms of seed yield. These results indicate that the differential sensitivity at growth stages can be clearly shown when stages are well defined in the timing treatments and the stress is quantified at growth stages based on the same duration of salinization. The interaction between cultivar and timing treatment was not significant. Uniform management options can be developed for irrigation using saline water for the cultivars with similar genetic backgrounds.

ADDITIONAL INDEX WORDS. salinity, salt stress, water reuse, irrigation water, drainage water ABSTRACT. Saline agricultural drainage water may be used as a resource to grow high value horticultural crops and reduce the volume of drainage for eventual disposal. To explore reuse options the effects of salinity and timing of application were tested on selected leafy vegetables grown in 24 sand culture plots in Riverside, Calif. The leafy winter vegetables included 'Ruby Red Chard' Swiss chard ( Beta vulgaris L. var. flavescens (Lam.) Lam.), 'Space' spinach ( Spinacia oleracea L.), 'Vitamin Green' salad greens ( Brassica rapa L. (Narinosa Group)), 'Red Giant' mustard greens ( Brassica juncea L. (Czerniak)), pac choi (Brassica rapa L. (Chinensis Group)), 'Winterbor' kale ( Brassica oleracea L. (Acephala Group)), tatsoi (Brassica rapa L. (Narinosa Group)), 'Salad King' curly endive ( Cichorium endivia L.), and 'Red Preco No. 1' radicchio (Cichorium intybus L.). All vegetables were planted at the same time and irrigated initially with tap water and nutrients. At 3 and 7 weeks after seeding (application times), six salinity treatments were initiated by adding salts to the irrigation water to represent the chemical compositions of drainage waters found typically in the San Joaquin Valley, Calif. The six salinity treatments had electrical conductivities of 3 (control), 7, 11, 15, 19, or 23 dS·m -1 . A randomized complete block design was used with (6 salinities × 2 application times × 2 replications). Within each plot a 1.5-m row of each of the nine vegetables was grown as split plots. Salinity reduced fresh weight (FW) yields of all species. Salt stress applied at 3 weeks after seeding reduced FWs for seven of the nine vegetables compared to salination at 7 weeks. Analyses of salt tolerance curves, maximum yields, and the point of 50% yield reduction (C 50) were conducted. Greens produced the highest biomass at 874 g/plant, but was the most affected by application time. Swiss chard and radicchio were not significantly affected by timing of salinity application, and Swiss chard was the most salt tolerant overall. Greens, kale, pac choi, and to a lesser extent, tatsoi, have potential as winter-grown, leafy vegetables in drainage water reuse systems. markets. Potential crops include winter vegetables, flowers, herbs, medicinals, and other species that are tolerant to drainage water irrigation and would provide high economic return (Shan- non and Grieve, 1999, 2000). In this study, we used synthetic drainage water with ion composition similar to that found typically in the San Joaquin Valley to irrigate nine leafy winter vegetables. The purpose of the study was to determine potential yield and salt tolerance over a range of salinities and to determine if improved yields could be obtained if initial salination were delayed past the early plant establishment stage in which most plants are considered to be salt sensitive (Shannon et al., 1994).

The constant capacitance model, a chemical surface complexation model, was applied to arsenate, As(V), adsorption on 49 soils selected for variation in soil properties. The constant capacitance model was able to fit arsenate adsorption on all soils by optimizing either three monodentate or two bidentate As(V) surface complexation constants. A general regression model was developed for predicting soil As(V) surface complexation constants from easily measured soil chemical characteristics. These chemical properties were cation exchange capacity (CEC), inorganic C (IOC) content, organic C (OC) content, iron oxide content, and surface area (SA). The prediction equations were used to obtain values for the As(V) surface complexation constants for five additional soils, thereby providing a completely independent evaluation of the ability of the constant capacitance model to describe As(V) adsorption. The model's ability to predict As(V) adsorption was quantitative on three soils, semi-quantitative on one soil, and poor on another soil. Incorporation of these prediction equations into chemical speciation-transport models will allow simulation of soil solution As(V) concentrations under diverse agricultural and environmental conditions without the requirement of soil specific adsorption data and subsequent parameter optimization.