Using apparent soil electrical conductivity (EC<Subscript>a</Subscript>) to characterize vineyard soils of high clay content

The adoption of precision viticulture requires a detailed knowledge of variation in soil chemical, physical and profile properties. This study evaluates the usefulness of apparent electrical conductivity (EC_a) data within a GIS framework to identify variations in soil chemical and physical properties and moisture content. The work was conducted in a vineyard located in the Carneros Region (Napa Valley, California). The soil was sampled using 44 boreholes to quantify chemical and physical characteristics and 9 open pits to verify the borehole observations. Moisture content was determined using time domain reflectometry (TDR). To characterize soil EC_a, three campaigns were undertaken using a soil electrical conductivity meter (EM38). Linear regressions between soil EC_a and soil properties were determined. Boreholes and TDR data were interpolated by kriging to characterize the spatial distribution of soil variables. The resulting maps were compared to the results obtained using the best EC_a linear regressions. Using EC_a measurements, soil properties like extractable Na⁺ and Mg²⁺, clay and sand content were well estimated, while best estimates were obtained for extractable Na⁺ (r ²  = 0.770) and clay content (r ²  = 0.621). The best estimates for soil moisture content corresponded to moisture in the deeper soil horizons (r ²  = 0.449). The methods described above provided maps of soil properties estimated by EC_a in a GIS framework, and could save time and resources during vineyard establishment and management.     

Delineating productivity zones in a citrus grove using citrus production,tree growth and temporally stable soil data

The productivity of a citrus grove with variation in tree growth was mapped to delineate zones of productivity based on several indicator properties. These properties were fruit yield, ultrasonically measured tree canopy volume, normalized difference vegetation index (NDVI), elevation and apparent electrical conductivity (EC_a). The spatial patterns of soil series, soil color and EC_a, and their correspondence with the variation in yield emphasized the importance of variation in the soil in differentiating the productivity of the grove. Citrus fruit yield was positively correlated with canopy volume, NDVI and EC_a, and yield was negatively correlated with elevation. Although all the properties were strongly correlated with yield and were able to explain the productivity of the grove, citrus tree canopy volume was most strongly correlated (r = 0.85) with yield, explaining 73% of its variation. Tree canopy volume was used to classify the citrus grove into five productivity zones termed as ‘very poor’, ‘poor’, ‘medium’, ‘good’ and ‘very good’ zones. The study showed that productivity of citrus groves can be mapped using various attributes that directly or indirectly affect citrus production. The productivity zones identified could be used successfully to plan soil sampling and characterize soil variation in new fields.     

Spatial variability and temporal stability of apparent soil electrical conductivity in a Mediterranean pasture

The general objectives of this study were to evaluate (i) the specificity of the spatial and temporal dynamics of apparent soil electrical conductivity (EC_a) measured by a electromagnetic induction (EMI) sensor, over 7 years, in variable conditions (of soil moisture content (SMC), soil vegetation cover and grazing management) and, consequently, (ii) the potential for implementing site-specific management (SSM). The DUALEM 1S sensor was used to measure the EC_a in a 6 ha pasture experimental field four times between June 2007 and February of 2013. Soil spatial variability was characterized by 76 samples, geo-referenced with the global positioning system (GPS). The soil was characterized in terms of texture, moisture content, pH, organic matter content, nitrogen, phosphorus and potassium. This study shows a significant temporal stability of the EC_a patterns under several conditions, behavior that is an excellent indicator of reliability of this tool to survey spatial soil variability and to delineate potential site-specific management zones (SSMZ). Significant correlations were obtained in this work between the EC_a and relative field elevation, pH, silt and soil moisture content. These results open perspectives for using the EMI sensor as an indicator of SMC in irrigation management and of needs of limestone correction in Mediterranean pastures. However, it is interesting to extend the findings to other types of soil to verify the origin of the lack of correlation between the EC_a data measured by DUALEM sensor and properties such as the clay, organic matter or phosphorus soil content, fundamental parameters for establishment of pasture SSM projects.     

Linking Microbial-Scale Findings to Farm-Scale Outcomes in a Dryland Cropping System

Soil biological response to management is best evaluated in field-scale experiments within the context of the soil environment and crop; however, cost-effective methods are lacking to relate these data which span multiple spatial scales. We hypothesized that zones of apparent electrical conductivity (EC_a) could be used to integrate soil properties (sampling-site scale), microbial-scale measures of vesicular-arbuscular mycorrhizal (VAM) fungi, and field-scale wheat yields from yield maps. An on-farm dryland experiment (250 ha) was established wherein two (32-ha) fields were assigned to each phase of a winter wheat (Triticum aestivum L.) – corn (Zea mays L.) – proso millet (Panicum miliaceum L.) – fallow rotation. Each field was mapped and classified into four zones (ranges) of EC_a. Soil samples were collected from geo-referenced sites within EC_a zones and analyzed for multiple soil properties associated with productivity (0–7.5 and/or 0–30 cm). Additionally, VAM fungi were assessed using C16:1(cis)11 fatty acid methyl ester biomarker (C16_vam), glomalin immunoassay, and wet-aggregate stability (WAS) techniques (1–2mm aggregates from 0- to 7.5-cm soil samples). Concentrations of C16_vam and WAS increased among cropping treatments as: fallow < wheat < corn < millet. Glomalin across crops and replicates, C16_vam and WAS in fallow (crop effect removed), soil properties associated with productivity, and wheat yields were negatively correlated with EC_a and different among EC_a zones (P 0.05). Zones of EC_a provide a point of reference for relating data collected at different scales. Monitoring cropping system parameters and profitability, over time, may allow linkage of microbial-scale processes to farm-scale economic and ecological outcomes.     

Delineating site-specific management zones for pH-induced iron chlorosis

Iron chlorosis can limit crop yield, especially on calcareous soil. Typical management for iron chlorosis includes the use of iron fertilizers or chlorosis tolerant cultivars. Calcareous and non-calcareous soil can be interspersed within fields. If chlorosis-prone areas within fields can be predicted accurately, site-specific use of iron fertilizers and chlorosis-tolerant cultivars might be more profitable than uniform management. In this study, the use of vegetation indices (VI) derived from aerial imagery, on-the-go measurement of soil pH and apparent soil electrical conductivity (EC_a) were evaluated for their potential to delineate chlorosis management zones. The study was conducted at six sites in 2004 and 2005. There was a significant statistical relationship between grain yield and selected properties at two sites (sites 1 (2005) and 3), moderate relationships at sites 2 and 4, and weak relationships at site 5. For sites 1 (2005) and 3, and generally across all sites, yield was predicted best with the combination of NDVI and deep EC_a. These two properties were used to delineate chlorosis management zones for all sites. Sites 1 and 3 showed a good relationship between delineated zones and the selected properties, and would be good candidates for site-specific chlorosis management. For site 5, differences in the properties between mapped zones were small, and the zones had weak relationships to yield. This site would be a poor candidate for site-specific chlorosis management. Based on this study, the delineation of chlorosis management zones from aerial imagery combined with soil EC_a appears to be a useful tool for the site-specific management of iron chlorosis.     

Soil apparent electrical conductivity and geographically weighted regression for mapping soil

To resolve the spatial variation in soil properties intensively is expensive, but such knowledge is essential to manage the soil better and to achieve greater economic and environmental benefits. The objective of this study was to determine whether the soil apparent electrical conductivity (EC_a), alone or combined with other variables, is a useful alternative for providing detailed information on the soil in the Extremadura region of Spain. Apparent soil electrical conductivity was measured and geographically weighted regression was used to characterize the spatial variation in soil properties, which in turn can be used for soil management. This study shows that soil cation exchange capacity, calcium content, clay percentage and pH have a relatively strong spatial correlation with EC_a in the soil of the study area.     

Estimating soil organic matter using interpolation methods with a electromagnetic induction sensor and topographic parameters: a case study in a humid region

Soil organic matter (SOM) is a key indicator of soil quality although, usually, detailed data for a given area is difficult to obtain at low cost. This study was conducted to evaluate the usefulness of soil apparent electrical conductivity (EC_a), measured with an electromagnetic induction sensor, to improve the spatial estimation of SOM for site-specific soil management purposes. Apparent electrical conductivity was measured in a 10-ha prairie in NW Spain in November 2011. The EC_a measurements were used to design a sampling scheme of 80 locations, at which soil samples were collected from 0 to 20 cm depth and from 20 cm to the boundary of the A horizon (ranging from 25 to 48 cm). The SOM values determined at the two depths considered were weighted to obtain the results for the entire A Horizon. SOM distribution maps were obtained by inverse distance weighting and geostatistical techniques: ordinary kriging (OK), cokriging (COK), regression kriging either with linear models (LM-RK) or with random forest (RF-RK). SOM ranged from 46.3 to 78.0 g kg^?1, whereas EC_a varied from 6.7 to 14.7 mS m^?1. These two variables were significantly correlated (r = ?0.6, p < 0.05); hence, EC_a was used as an ancillary variable for interpolating SOM. A strong spatial dependence was found for both SOM and EC_a. The maps obtained exhibited a similar spatial pattern for SOM; COK maps did not show a significant improvement from OK predictions. However, RF-RK maps provided more accurate spatial estimates of SOM (error of predictions was between four and five times less than the other interpolators). This information is helpful for site-specific management purposes at this field.     

Apparent Electrical Conductivity,Soil Properties and Spatial Covariance in the U.S. Southern High Plains

Site-specific soil and crop management will require rapid low-cost sensors that can generate position-referenced data that measure important soil properties that impact crop yields. Apparent electrical conductivity (EC_a) is one such measure. Our main objective was to determine which commonly measured surface soil properties were related to EC_a at six sites in the Texas Southern High Plains, USA. We used the Veris 3100 and Geonics EM-38 EC mapping systems on 12 to 47 ha areas in six center-pivot irrigation sites. Soil samples were taken from 0–150 mm on a 0.1 to 0.8 ha grid and analyzed for routine nutrients and particle size distribution. At four of the six sites, shallow EC_a measured with the Veris 3100 (EC_a-sh) positively correlated to clay content. Clay content was negatively related with EC_a-sh at one site, possibly due to low bulk density of the shallow calcic horizon at that site. Other soil properties that were often correlated with EC_a included soil extractable Ca²⁺, Mg²⁺, Na⁺, CEC, silt and soluble salts. Extractable K⁺, NO₃^–, SO₄^–, Mehlich-3-P, and pH were not related to EC_a. Partial least squares regression (PLS) of seven soil properties explained an average of 61%, 51% and 37% of the variation in observed shallow EC_a-sh, deep EC_a with the Veris 3100 (EC_a-dp) and EC_a with the Geonics EM-38 (EC_a-em), respectively. Including nugget, range and sill parameters from a spherical semivariance model of the residuals from PLS regression improved the fit of mixed models in 15 of 18 cases. Apparent EC, therefore can provide useful information to land-users about key soil properties such as clay content and extractable Ca²⁺, but that spatial covariance in these relationships should not be ignored.     

Evaluation of methods to determine residual soil nitrate zones across the northern Great Plains of the USA

A four-year study was conducted from 2000 to 2004 at eight field sites in Montana, North Dakota and western Minnesota. Five of these sites were in North Dakota, two were in Montana and one was in Minnesota. The sites were diverse in their cropping systems. The objectives of the study were to (1) evaluate data from aerial photographs, satellite images, topographic maps, soil electrical conductivity (EC_a) sensors and several years of yield to delineate field zones to represent residual soil nitrate and (2) determine whether the use of data from several such sources or from a single source is better to delineate nitrogen management zones by a weighted method of classification. Despite differences in climate and cropping, there were similarities in the effectiveness of delineation tools for developing meaningful residual soil nitrate zones. Topographic information was usually weighted the most because it produced zones that were more correlated to actual soil residual nitrate than any other source of data at all locations. The soil EC_a sensor created better correlated zones at Minot, Williston and Oakes than at most eastern sites. Yield data for an individual year were sometimes useful, but a yield frequency map that combined several years of standardized yield data was more useful. Satellite imagery was better than aerial photographs at most locations. Topography, satellite imagery, yield frequency maps and soil EC_a are useful data for delineating nutrient management zones across the region. Use of two or more sources of data resulted in zones with a stronger correlation with soil nitrate.     

Evaluating unoccupied aerial systems (UAS) imagery as an alternative tool towards cotton-based management zones

Unoccupied aerial system (UAS) imagery may serve as an additional tool towards management zone delineation. This is because UAS data collection is relatively flexible. However, it is unclear how useful UASs can be towards generating management zones, relative to preexisting tools (e.g. apparent soil electrical conductivity or EC_a). The purpose of this study, therefore, was to evaluate UAS imagery, relative to EC_a, in terms of their ability to: 1) predict cotton traits (i.e. height, seed cotton yield), and 2) define cotton management zones based on these traits. Single-season UAS images from multispectral/thermal sensors were collected and processed into Normalized Difference Vegetation Index (NDVI) and radiometric surface temperature (T_r), respectively. Management zones were also delineated using digital camera (RGB) imagery collected at periods before planting and near harvest. RGB management zones were delineated by a novel open boll mapping approach. In-season NDVI and T_r layers were significant (P?<?0.01) predictors of canopy height. Additionally, NDVI and T_r maps produced statistically different management zones during flowering and boll filling growth stages in terms of yield (P?=?0.001 or less). Open boll layers were all more accurate predictors of cotton seed yield than EC_a data—these two layers also produced statistically distinct management zones. ANOVA tests revealed that, given EC_a alone, adding UAS information via the RGB open boll map resulted in a significantly different yield prediction model (P?<?0.001). These results suggest that UAS imagery can offer valuable information for cotton management zone delineation that other techniques cannot.

     

Mapping management zones in a sandy pasture soil using an objective model and multivariate techniques

Soils occupied by dryland pastures usually have low fertility but can exhibit a high spatial variability. Consequently, logical application of fertilisers should be based on an appropriate knowledge of spatial variability of the main soil properties that can affect pasture yield and quality. Delineation of zones with similar soil fertility is necessary to implement site-specific management, reinforcing the interest of methods to identify these homogeneous zones. Thus, the formulation of the objective Rasch model constitutes a new approach in pasture fields. A case study was performed in a pasture field located in a montado (agrosilvopastoral) ecosystem. Measurements of some soil properties (texture, organic matter, nitrogen, phosphorus, potassium, cation exchange capacity and soil apparent electrical conductivity) at 24 sampling locations were integrated in the Rasch model. A classification of all sampling locations according to pasture soil fertility was established. Moreover, the influence of each soil property on the soil fertility was highlighted, with the clay content the most influential property in this sandy soil. Then, a clustering process was undertaken to delimit the homogeneous zones, considering soil pasture fertility, elevation and slope as the input layers. Three zones were delineated and vegetation indices (normalized difference vegetation index, NDVI, and normalized difference water index, NDWI) and pasture yield data at sampling locations were employed to check their differences. Results showed that vegetation indices were not suitable to detect the spatial variability between zones. However, differences in pasture yield and quality were evident, besides some key soil properties, such as clay content and organic matter.

     

Changes in yield classification in a soybean-rice rotation

For yield based site-specific management to be successful in fields with crop rotations, changes in management zones between crops must be determined. The study objectives were to determine if yield classes change between crops within a rotation and whether soil properties can predict the yield classes or the year-to-year changes. A percentile classification method was used to categorize yearly soybean (Glycine max) and rice (Oryza sativa) yield in two fields with soybean-rice-soybean rotations into low, medium and high yield classes. There was little agreement in yield classifications between years. Yield class based on soil properties was predicted accurately by linear discriminant analysis in Field 1 20–67% of the time and in Field 2 13–83% of the time. Predictions in Field 1 were based on soil available Mg and P, elevation and the deep soil apparent electrical conductivity (EC_a). Predictions in Field 2 were based on soil texture, soil available P, K and Mg, and pH. The linear discriminant analysis was also able to predict year-to-year changes in yield class. Changes in class in Field 1 could be predicted by total soil C and N, silt, and soil available Mg and P depending on the year. Soil texture, soil available P, K and Mg, total soil C and pH, elevation and deep soil EC_a predicted yield changes in Field 2 depending on the year. The results of this study indicate only limited success at management zone definition in a soybean-rice rotation. Further investigation is needed with other crop rotation sequences to verify the findings of this study.     

The measurement,prediction, and development of soil management zones in low-relief sodic soils

Inexpensive, accurate, and rapid measurements of sodicity are required to identify the restoration options for degraded sites. This study determined the spatial variability of the percent of ammonium acetate extractable Na (%Na), apparent electrical conductivity (EC_a), pH_1:1, elevation and topographic wetness index, and used this information to create %Na management zones. In an 8.1 ha North Dakota field that contained Natraquolls and Calciaquolls, 1088 soil samples from the 0–0.3 and 0.3–0.6 m were collected from a 12.2 by 12.2 m geo-referenced grid. At each grid point, the elevation and ECa was determined using a differential corrected global positioning system and EM38m, respectively. Soil samples were analyzed for the %Na, EC_1:1, pH_1:1, and soil dispersion. Exponential semi-variogram models explained 96.7% of the ln-transformed %Na data in the 0–0.3 m soil depth, and %Na was correlated to EC_1:1 (r = 0.54), pH_1:1 (r = 0.68), clay dispersion (r = 0.68), ECav (r = 0.49), and ECah (r = 0.57). Forward stepwise regression models based on elevation, EC_1:1, pH_1:1, and ECah explained 64 and 74% of the %Na variability in the surface 0.3 m and subsurface 0.3–0.6 m, respectively. Management zones were identified that reduced the %Na variability up to 82%.     

Imaging the electrical conductivity of the soil profile and its relationships to soil water patterns and drainage characteristics

Soil water content (θ) measurement is vital for accurate irrigation scheduling. Electromagnetic induction surveys can be used to map spatial variability of θ when other soil properties are uniform. However, depth-specific θ variations, essential for precision irrigation management, have been less investigated using this method. A quasi-2-dimensional inversion model, capable of inverting apparent soil electrical conductivity (EC_a) data to calculate estimates of true electrical conductivity (σ) down the entire soil profile, was developed using EC_a data collected by a multi-coil Dualem-421S sensor. The optimal relationships between σ and volumetric water content (θv) were established using all coil arrays of the Dualem-421S, a damping factor of 0.04, an initial model of 35 mSm^?1, and with ten iterations (R²?=?0.70, bias?=?0.00 cm³cm^?3, RMSE?=?0.04 cm³cm^?3). These relationships were then used to derive soil profile images of these properties, and as expected, θv and σ follow similar trends down the soil profile. The derived soil profile images for θv have potential use for irrigation scheduling to two EC_a-derived soil management zones under a variable rate irrigation system at this case study site. They reflect the intrinsic soil differences that occur between texture, texture transitions and drainage characteristics. The method can also be used to guide placement of soil moisture sensors for in-season monitoring of spatio-temporal variations of θv. This soil imaging method showed good potential for predicting 2D depth profiles of soil texture, moisture and drainage characteristics, and supporting soil, plant and irrigation management.

     

Field methods for making productivity classes for site-specific management of wheat

Reducing the decision-making unit to classes within fields can improve yields, efficiency in the use of nutrients and profitability of crops. The objectives were to compare methods for class delimitation in wheat (Triticum aestivum L.) crops based on apparent productivity levels and establish similarities among them in terms of spatial overlapping, productive attributes and the use of nitrogen. In three wheat fields, high and low apparent productivity classes (APC) were defined based on eight methodologies: yield maps, soil maps, gramineae vegetation index, rotation crop index, interpretation of satellite images, management records, elevation and integrated soil and yield maps. In each APC, soil and crop yield components were determined under five nitrogen fertilization levels. Among delimitation methodologies, the degree of coincidence varied from 1.4 to 81.7%. The differences in soil properties, nitrogen use efficiency and grain yields were greater among fields than among APC within each field. In each field, the delimitation methodologies identified different single factors that discriminated among the potential management classes and were partially associated with the crop grain yields. The wheat crops at the low APC yielded 39% less and 12% less than at the high APC, respectively. The nitrogen fertilization, at the rate for maximum productivity for each ACP, reduced the yield differences between contrasting APC. Nitrogen fertilization also modified clustering of classes based on expected yields. Making management classes for wheat based on expected productivity is more accurate when based on previous crop production information under similar nitrogen fertilization conditions than the targeted crop.

     

土壤基本理化性质对外源镉蚯蚓慢性毒性的影响

为了给污染土壤生态风险评价和构建土壤生态筛选基准提供基础参考数据,参考经济合作与发展组织(OECD)颁布的蚯蚓慢性毒性试验方法,研究了镉在我国18种典型土壤中对赤子爱胜蚓(Eisenia fetida)的慢性毒性。结果表明,镉在不同类型土壤中对蚯蚓产茧量最大无影响浓度NOEC变化范围为10~100 mg·kg~(-1),半数有效抑制浓度EC_(50)变化范围为66.5~263.5 mg·kg~(-1)。土壤基本性质显著影响着镉的生物毒性,通过对EC_(50)与土壤的主要理化性质逐步多元回归分析,发现土壤pH值和有机质含量与EC_(50)呈显著正相关关系,黏粒与EC_(50)呈显著负相关关系,三种因子共同控制了EC_(50)预测回归模型变异的89.1%。同时分析不同类型土壤中蚯蚓体内镉积累量变化可知,土壤pH越高,有机质含量越高,土壤中镉生物有效性就越低。     

A floating sensing system to evaluate soil and crop variability within flooded paddy rice fields

Continuous paddy rice cultivation requires fields to be flooded most of the time limiting seriously the collection of detailed soil information. So far, no appropriate soil sensor technology for identifying soil variability of flooded fields has been reported. Therefore, the primary objective was the development of a sensing system that can float, acquire and process detailed geo-referenced soil information within flooded fields. An additional objective was to determine whether the collected apparent electrical conductivity (EC_a) information could be used to support soil management at a within-field level. A floating sensing system (FloSSy) was built to record EC_a using the electromagnetic induction sensor EM38, which does not require physical contact with the soil. Its feasibility was tested in an alluvial paddy field of 2.7 ha located in the Brahmaputra floodplain of Bangladesh. The high-resolution (1 × 1 m) EC_a data were classified into three classes using the fuzzy k-means classification method. The variation among the classes could be attributed to differences in subsoil (0.15–0.30 m below soil surface) bulk density, with the smallest EC_a values representing the lowest bulk density. This effect was attributed to differences in compaction of the plough pan due to differential puddling. There was also a significant difference in rice yield among the EC_a classes, with the smallest EC_a values representing the lowest yield. It was concluded that the floating sensing system allowed the collection of relevant soil information, opening potential for precision agriculture practices in flooded crop fields.     

Mapping subsoil acidity and shallow soil across a field with information from yield maps, geophysical sensing and the grower

Management decisions, such as subsoil liming or varying fertilizer inputs to take account of soil depth and anticipated yields require knowledge of where subsoil constraints to root growth occur across the field. We used selected yield maps based on criteria derived from crop simulation, apparent soil electrical conductivity (EC_a), gamma-ray emission maps and a soil type map drawn by the grower to predict the spatial distribution of subsoil acidity and shallow soil across a field. Yield maps integrate the effects of variation in soil and climate, and it was only under specific seasonal conditions that subsoil constraints depressed yields. We used crop simulation modelling to select yield maps with a large information content on the spatial distribution of these constraints and to omit those with potentially misleading information. Yield and other spatial data layers were used alone or in combination to develop subsoil mapping options to accommodate differences in data availability, access to precision agriculture techniques and the grower’s aptitude and preference. One option used gamma-ray spectrometry and EM38 survey as a dual-sensing system to improve data interpretation. Gamma-ray spectrometry helped to overcome the inability of current EC_a-based methods to sense soil depth in highly weathered sandy soil over cemented gravel. A feature of the approaches presented here is the use of grower and agronomist knowledge, and experience to help interpret the spatial data layers and to evaluate which approach is most suitable and likely to be adopted to suit an individual.