Apparent Soil Electrical Conductivity Research Articles

Spatial variability of soil properties and yield of a grazed alfalfa pasture in Brazil

Knowledge of the spatial variability of soil properties and of forage yield is needed for informed use of soil inputs such as variable rate technology (VRT) for lime and fertilizers. The objective of this research was to map and evaluate the spatial variability of soil properties, yield, lime and fertilizer needs and economic return of an alfalfa pasture. The study was conducted in a 5.3 ha irrigated alfalfa pasture in Sao Carlos, SP, Brazil that was directly grazed and intensively managed in a 270-paddock rotational system. Alfalfa shoot dry matter yield was evaluated before grazing. Soil samples were collected at 0–0.2 m depth, and each sample represented a group of 2 or 3 paddocks. Apparent soil electrical conductivity (ECa) was measured with a contact sensor. The cost of producing 1 ha of alfalfa was estimated from the amount of lime and fertilizer needed and was then used to estimate the total cost of production for the dairy system. The alfalfa dry matter yield was used to simulate the pasture stocking rate, milk yield, gross revenue and net profit. The spatial variability of soil properties and site-specific liming and fertilizer needs were modeled using semi-variograms with VESPER software, the soil fertility information and economic return were modeled with SPRING software. The results showed that geostatistics and GIS were effective tools for revealing soil and pasture spatial variability and supporting management strategies. Soil nutrients were used to classify the soil spatial distribution map and design site-specific lime and fertilizer application maps. Spatial variation in forage and spatial estimates of stocking and milk yield are adequate pasture management tools. Spatial analyses of needs, forage availability and economic return are management tools for avoiding economic problems, as well as potential environmental problems, caused by unbalanced nutrient supplies and over- or under-grazing.

Monitoring scale-specific and temporal variation in electromagnetic conductivity images

In semiarid and arid landscapes, irrigation sustains agricultural activity but because of increasing demands on water resources there is a need to make gains in efficiency. As such spatial variation of soil properties such as clay and salinity needs to be understood because they strongly influence soil moisture availability. One way is to use electromagnetic induction because apparent soil electrical conductivity (ECa) is related to volumetric soil moisture (θ), clay and salinity (ECe). However, depth-specific variation has not been explored. Our aim is to generate electromagnetic conductivity images (EMCIs) by inverting DUALEM-421 ECa and show how true electrical conductivity (σ) can be correlated with θ, clay, ECe and bulk density (ρ) on different days post-irrigation (i.e., 1, 4 and 12 days). Two-dimensional multi-resolution analysis (MRA) is used to show how spatio-temporal variation in σ is scale-specific and how soil properties influence σ at different scales. We study this beneath a pivot irrigated alfalfa crop. We found that σ on days 1 and 4 was correlated with θ (Pearson’s r = 0.79 and 0.61) and clay (0.86 and 0.80) and the dominant scale of variation occurred at 9.3–18.7 m (50.21 % of total variation), >74.7 m (23.18 %) and 4.7–9.3 m (16.29 %). Between 9.3–18.7 and 4.7–9.3 m the variation may be a function of the cutter width (8 m), while >74.7 m may be change in clay and ECe and gantry spacing (~48 m). The sprinkler spacing (1.2 and 1.6 m) explains short-scale variation at 1.2–2.3 m.

Evaluation of vegetation indices and apparent soil electrical conductivity for site-specific vineyard management in Chile

Spatial variability of Chilean vineyards, in terms of yield and quality, is high, which fully justifies site-specific management, particularly differential harvest. In this study, the most common zoning tools (NDVI and ECa measurements) were evaluated and compared. Comparisons also included a calibrated GVI. Two contrasting large field experiments (pruning, irrigation, and N fertilization treatments) were established in vineyards to (1) evaluate two vegetation indices: (i) a non-calibrated airplane-based NDVI and (ii) calibrated satellite-based GVI and to (2) evaluate the ECa measurements. The GVI was also assessed at the commercial level, in different vineyards and valleys. The GVI was more sensitive in discriminating grape yields and quality while the NDVI failed to adequately sense vigor patterns and fruit quality in the more homogeneous site. Thus, a calibrated GVI can be recommended as a better tool than NDVI for defining management zones as well as making spatial and temporal comparisons among fields and seasons. In general, ECa explained few differences in the alluvial soil properties and did not predict differences in plant vigor as measured by either vegetation indices, therefore ECa by itself was not a good estimator of the most commonly measured soil properties for establishing management zones in these fields with low variability in terms of EC and other soil characteristics.

The temporal stability of the variability in apparent soil electrical conductivity

Apparent soil electrical conductivity (EC a ) measurements can be used for crop management in precision agriculture. However, EC a is a soil attribute that presents spatial and temporal variability. It is affected by a group of factors that act simultaneously on the soil, such as the soil texture, moisture content, organic matter content and ionic concentrations in the soil solution, which complicates analysis. For soil and crop management, it is important to determine whether the pattern of the EC a spatial variability changes over time. Thus, EC a measurements have the potential for delimiting management zones that are stable over time. The objective of this work was to determine whether the spatial variability pattern of EC a is maintained over time and under different soil conditions. To this end, the EC a was measured at different soil depths using a portable sensor on two crop fields. The first step was to measure and generate an EC a map for each area. By defining a path with the maximum EC a variability, 50 sampling points were located on each field. The EC a values were measured on 20 different dates in the 0 – 20 cm, 0 – 40 cm and 0 – 60 cm soil layers. The soil water content was measured at the same points in the 0 – 20 cm layer on the same dates. The temporal stability of the EC a was analyzed using spatial and temporal variability maps, a correlation analysis and a coefficient of variation over time for each field. In both areas, the EC a exhibited temporal stability in the spatial pattern variability at the three evaluated depths, even though the soil water content values changed on each date. EC a determination presents an important alternative for mapping agricultural fields for crop management in precision agricultural systems.

Quantitative Evaluation and Uncertainty Assessment on Geostatistical Simulation of Soil Salinity Using Electromagnetic Induction Technique

Diagnosis of soil salinity and characterizing its spatial variability both vertically and horizontally are needed to establish control measures in irrigated agriculture. In this regard, it is essential that salinity development in varying soil depths be known temporally and spatially. Apparent soil electrical conductivity, measured by electromagnetic induction instruments, has been widely used as an auxiliary variable to estimate spatial distribution of field soil salinity. The main objectives of this paper were adopted a mobile electromagnetic induction (EMI) system to perform field electromagnetic (EM) survey in different soil layers, to evaluate the uncertainty through Inverse Distance Weighted (IDW) and Ordinary Kriging (OK) methods, and to determine which algorithm is more reliable for the local and spatial uncertainty assessment. Results showed that EM38 data from apparent soil electrical conductivity are highly correlated with salinity, more accurate for estimating salinity from multiple linear regression models, which the correlation coefficient of 0 - 20, 20 - 40, 40 - 60 and 60 - 80 cm were 0.9090, 0.9228, 0.896 and 0.9085 respectively. The comparison showed that the prediction accuracy of two methods also displays good performance for soil salinity, the estimation precision of IDW method (with E = 0.8873, 0.9075, 0.8483 and 0.901, RPD = 9.64, 8.01, 8.17 and 11.23 in 0 - 20, 20 - 40. 40 - 60 and 60 - 80 cm soil layers, respectively) was superior to that of OK (with E = 0.8857, 0.872, 0.8744 and 0.8822, RPD = 9.44, 7.83, 8.52 and 10.88, respectively), but differences of two methods in predictions are not significant. The obtained salinity map was helpful to display the spatial patterns of soil salinity and monitor and evaluate the management of salinity.

Genetic Algorithm Applying to Calculate the Great Wall Soil Water Electrical Conductivity

In this paper, we report a simple but effective algorithm to estimate the undetermined coefficients in the formula of soil water electrical conductivity value by the soil apparent electrical conductivity method. By doing so, the coefficients can be accurately obtained to further calculate the soil water electrical conductivity. According to the process of calculating the soil solution electrical conductivity based on the apparent conductivity of soil, the error of the calculation equation is effectively calculated by the two order curve fitting method. Meanwhile the genetic algorithm is adopted to reduce the influence of the data error generated by the proposed algorithm, and improve the accuracy of the data as well. The experimental results demonstrate that the solution by the proposed algorithm can be effectively obtained and the error change is in one direction simultaneously. With these advantages, we can use soil water electrical conductivity to calculate the content of soil salt.

CORRELACIÓN ESPACIAL ENTRE MALAS HIERBAS EN UNA PRADERA Y SU RELACIÓN CON LA CONDUCTIVIDAD ELÉCTRICA APARENTE DEL SUELO (CE A )

RESUMEN El objetivo de este trabajo fue evaluar la ecología y la correlación lineal y espacial entre malas hierbas de una pradera y su relación con la conductividad eléctrica aparente del suelo (CEa, mS m-1). La toma de muestras se hizo los días: 23/06/2008 (CEa) y el 24/06/2008 (malas hierbas). Los datos fueron analizados por medio de estadística clásica y de herramientas de geoestadística. La mayoría de los datos presentaron distribución de frecuencia logarítmica, siendo necesaria la utilización del krigeado residual. Se encontraron bajos valores de correlación para las medidas de CEa con las mediciones de malas hierbas en el área de estudio, estando las mismas influidas por el relieve del área. Los mapas de variabilidad espacial mostraron que existía relación entre las malas hierbas y los valores de CEa (CEa-V y CEa-H), principalmente con relación al número de especies de malas hierbas, ya que hay un aumento del número de especies cuando se produce disminución de los valores de CEa. Los mapas de variabilidad espacial para las malas hierbas demuestran la dominancia de ciertas especies y el endemismo de otras.

Electromagnetic induction: A support tool for the evaluation of soil CO2 emissions and soil organic carbon content in olive orchards under semi-arid conditions

Electromagnetic Induction (EMI), a non-invasive geophysical technique, can be a useful tool to study soil distribution of physical–chemical characters that strongly influence total soil respiration. Soil respiration emission flux (FCO2) was followed in an orchard (0.7ha) with olive trees placed at irregular distances. FCO2 was measured in four different days at 6:00 and 15:00h. Correlations between soil respiration and soil apparent electrical conductivity (ECa), measured by the EMI technique, were assessed. Statistically significant linear relationships were found between ECa, measured at 7kHz, and FCO2 (R2>0.6). The strong relations found between daily FCO2 and ECa values allowed to spatialize soil respiration rate at field scale. The EMI technique combined with the statistical software called ESAP (Electrical conductivity Sampling, Assessment, and Prediction) seemed to be a very efficient tool to choose representative soil sites within the field on where to measure FCO2. The EMI/ESAP procedure was also compared with two soil sampling procedures, Joint Research Centre European Method (JRC-EU) and regular grid sampling, in order to estimate average soil organic carbon (SOC) value within the olive orchard. Results suggested that the above mentioned approach could be an interesting solution to reduce number of samplings and their cost reaching, in the meantime, reliable assessments of FCO2 and SOC at field scale.

Monitoring of soil organic carbon over 10 years in a Mediterranean silvo-pastoral system: potential evaluation for differential management

Soil organic carbon (SOC) plays a vital role in determining the susceptibility to land degradation. The recommended procedure for the recovery of the characteristic poor soils of the Southern region of Portugal is the installation of grazed permanent pastures and increase of soil fertility. The objectives of this study were: (i) to identify the spatial and temporal patterns of soil nutrients at four points in time over a 10-year period in a perennial pastureland; (ii) to test new tools for survey of the spatial variability of soil nutrients; (iii) to evaluate the potential for differential organic management. A 6 ha permanent bio-diverse pasture field, grazed by sheep and improved by annual application of super phosphate fertilizer, was installed on a shallow soil in Mediterranean conditions. Spatial variability and temporal stability of topsoil macronutrients (phosphorus, nitrogen and potassium), SOC and pH were measured. The results indicate that SOC and pH have great potential for implementing differential management. In the case of SOC, the management classes map shows that over 80 % of the area has temporal stability, while more than 50 % of the area has low levels of SOC (<10 g kg−1), justifying the potential for differential application of C-rich organic soil amendments. The geospatial measurements of apparent soil electrical conductivity (ECa) and NDVI index showed significant correlation between these parameters and soil properties, revealing the potential of these tools for producing detailed soil maps, decisive for understanding the changes in soil properties under sustainable management systems.

Mapping soil moisture across an irrigated field using electromagnetic conductivity imaging

The ability to measure and map volumetric soil water (θ) quickly and accurately is important in irrigated agriculture. However, the traditional approach of using thermogravimetric moisture (w) and converting this to θ using measurements of bulk density (ρ—cm3/cm3) is laborious and time consuming. To speed up the process electromagnetic (EM) instruments have been used to assist in mapping average θ along a transect or across a field. This is because the apparent soil electrical conductivity (ECa) measured by EM instruments has been shown to be a function of θ, when other soil properties are uniform. However, mapping depth-specific soil θ has been little explored. One possible approach is to invert the ECa data to calculate estimates of true electrical conductivity (σ) at specific depths (i.e., 0.15, 0.45, 0.75, 1.05 and 1.35m) and couple this to measured θ. This research explores this possibility by using a single frequency multi-coil DUALEM-421 across a centre-pivot irrigated Lucerne field (Medicago sativa L.) in San Jacinto, CA, USA. The first aim is to determine an optimal set of inversion parameters (i.e., forward modelling, inversion algorithm and damping factor—λ) which are appropriate to establish a calibration between σ and θ. In this regard the largest coefficient of determination (R2=0.56) is achieved when we used the FS model, S2 algorithm and a λ=0.3. The second aim is to see if all the coil arrays of the DUALEM-421 are necessary. We conclude that while the DUALEM-1 produces a larger R2 (0.59), the use of the DUALEM-421 data is better (R2=0.56), because the total model misfit (4.70mSm−1) is smaller and because it better accounts for the spatial variation of θ in the subsoil. In terms of predicting θ, the calibration equation (θ=2.751+0.190×σ) was examined using a leave-one-out cross validation. The Lin’s concordance (0.73) between measured and predicted θ was good. The resulting 2-d depth slices and cross-sections gave insights into the spatial distribution of θ which allowed the inference of depth of saturated soil and location of the wetting front and identified areas where deep drainage may be problematic. The approach has applications for water use and management given it can identify inefficiencies in water application rates and use.

High-resolution prediction of soil available water content within the crop root zone

Summary A detailed understanding of soil hydraulic properties, particularly soil available water content (AWC) within the effective root zone, is needed to optimally schedule irrigation in fields with substantial spatial heterogeneity. However, it is difficult and time consuming to directly measure soil hydraulic properties. Therefore, easily collected and measured soil properties, such as soil texture and/or bulk density, that are well correlated with hydraulic properties are used as proxies to develop pedotransfer functions (PTF). In this study, multiple modeling scenarios were developed and evaluated to indirectly predict high resolution AWC maps within the effective root zone. The modeling techniques included kriging, co-kriging, regression kriging, artificial neural networks (NN) and geographically weighted regression (GWR). The efficiency of soil apparent electrical conductivity (ECa) as proximal data in the modeling process was assessed. There was a good agreement (root mean square error (RMSE) = 0.052 cm3 cm−3 and r = 0.88) between observed and point prediction of water contents using pseudo continuous PTFs. We found that both GWR (mean RMSE = 0.062 cm3 cm−3) and regression kriging (mean RMSE = 0.063 cm3 cm−3) produced the best water content maps with these accuracies improved up to 19% when ECa was used as an ancillary soil attribute in the interpolation process. The maps indicated fourfold differences in AWC between coarse- and fine-textured soils across the study site. This provided a template for future investigations for evaluating the efficiency of variable rate irrigation management scenarios in accounting for the spatial heterogeneity of soil hydraulic attributes.

Perspectives on delineating management zones for variable rate irrigation

This study aimed at investigating the performance of multiple irrigation zoning scenarios on a 73ha irrigated field located in west Tennessee along the Mississippi river. Different clustering methods, including k-means, ISODATA and Gaussian Mixture, were selected. In addition, a new zoning method, based on integer linear programming, was designed and evaluated for center pivot irrigation systems with limited speed control capability. The soil available water content was used as the main attribute for zoning while soil apparent electrical conductivity (ECa), space-borne satellite images and yield data were required as ancillary data. A good agreement was observed among delineated zones by different clustering methods. The new zoning method explained up to 40% of available water content variance underneath center pivot irrigation systems. The ECa achieved the highest Kappa coefficient (=0.79) among ancillary attributes, hence exhibited a considerable potential for irrigation zoning.

Characterization of field scale soil variability using remotely and proximally sensed data and response surface method

Soil salinization of the reclaimed tidelands is problematic. Therefore, there is a need to characterize the spatial variability of soil salinity associated with soil moisture and other soil properties across the reclaimed tidelands. One approach is the use of easily-acquired ancillary data as surrogates for the arduous conventional soil sampling. In a reclaimed coastal tideland in the south of Hangzhou Gulf, backscattering coefficient (σ0) from remotely sensed ALOS/PALSAR radar imagery (HH polarization mode) and apparent soil electrical conductivity (ECa) from a proximally sensed EM38 were used to indicate the spatial distribution of soil moisture and salinity, respectively. After that, response surface methodology (RSM) was employed to determine an optimal set of 12 soil samples using spatially referenced σ0 and ECa data. Spatial distributions of three soil chemical properties [i.e. soil organic matter (SOM), available nitrogen (AN), and available potassium (AK)] were predicted using inverse distance weighted method based on the 12 samples and were then compared with the predictions generated using 42 samples obtained from a conventional grid sampling scheme. It was concluded that combination of radar imagery and EM induction data can delineate the spatial variability of two key soil properties (i.e. moisture and salinity) across the study area. Besides, RSM-based sampling using radar imagery and EM induction data was highly effective in characterizing the spatial variability of SOM, AN and AK, compared with the conventional grid sampling. This new approach may be used to assist site specific management in precision agriculture.

Downscaling Landsat 7 canopy reflectance employing a multi-soil sensor platform

Crop growth and yield can be efficiently monitored using canopy reflectance. However, the spatial resolution of freely available remote sensing data is too coarse to fully understand the spatial dynamics of crop status. The objective of this study was to downscale Landsat 7 (L7) reflectance from the native resolution of 30 × 30 m to that typical of yield maps (ca. 5 × 5 m) over two fields in northeastern Colorado, USA. The fields were cultivated with winter wheat (Triticum aestivum L.) in the 2002–2003 growing season. Geospatial yield measurements were available (1 per ca. 20 m2). Geophysical (apparent soil electrical conductivity and bare-soil imagery) and terrain (micro-elevation) data were acquired (resolution <5 × 5 m) to characterize soil spatial variability. Geographically-weighted regressions were established to study the relationships between L7 reflectance and the geophysical and terrain data at the 30 × 30 m scale. Geophysical and terrain sensors could describe a large portion of the L7 reflectance spatial variability (0.83 < R2 < 0.94). Maps for regression parameters and intercept were obtained at 30 × 30 m and used to estimate the L7 reflectance at 5 × 5 m resolution. To independently assess the quality of the downscaling procedure, yield maps were used. In both fields, the 5 × 5 m estimated reflectance showed stronger correlations (average increase in explained variance = 3.2 %) with yield than at the 30 × 30 m resolution. Land resource managers, producers, agriculture consultants, extension specialists and Natural Resource Conservation Service field staff would be the beneficiaries of downscaled L7 reflectance data.

Modelling soil salinity across a gilgai landscape by inversion of EM38 and EM31 data

SummaryThe soil in arid and semi‐arid areas is often markedly saline, which can severely limit agricultural productivity. Increasingly, geophysical methods are being implemented to map the levels and areal extent of soil salinity. One of the most effective methods is electromagnetic (EM) induction with instruments designed to measure apparent soil electrical conductivity (ECa). This study describes the generation of electromagnetic conductivity images (EMCIs) by inverting ECa data obtained with the EM38 and EM31 devices along two closely‐spaced transects by the EM inversion approach in the EM4Soil package. The EM38 ECa data are shown to be a more effective predictor of soil ECe. Calibration equations based on calculated true electrical conductivity (σ) and measured electrical conductivity of a saturated soil‐paste extract (ECe) provide reliable estimates of ECa. The patterns of σ in a test of the method in soil developed over thick alluvium on a clay plain in central New South Wales, Australia, compare favourably with existing pedological mapping; the mounds and depressions of gilgai were strongly differentiated from the more sandy alluvial sediments that characterize prior stream channels. The overall approach is potentially useful for generating a single calibration equation that can be used to predict ECe at various depths in the soil. Improvements in EMCI modelling can also be sought by joint inversion of EM with other geophysical datasets.

Evaluation of vineyard growth under four irrigation regimes using vegetation and soil on-the-go sensors

Abstract. Precision agriculture is a useful tool to assess plant growth and development in vineyards. The present study focused on spatial and temporal analysis of vegetation growth variability, in four irrigation treatments with four replicates. The research was carried out in a vineyard located in the southwest of Spain during the 2012 and 2013 growing seasons. Two multispectral sensors mounted on an all-terrain vehicle (ATV) were used in the different growing seasons/stages in order to calculate the vineyard normalized difference vegetation index (NDVI). Soil apparent electrical conductivity (ECa) was also measured up to 0.8 m soil depth using an on-the-go geophysical sensor. All measured data were analysed by means of principal component analysis (PCA). The spatial and temporal NDVI and ECa variations showed relevant differences between irrigation treatments and climatological conditions.

Estimation of soil salinity in a drip irrigation system by using joint inversion of multicoil electromagnetic induction measurements

AbstractLow frequency electromagnetic induction (EMI) is becoming a useful tool for soil characterization due to its fast measurement capability and sensitivity to soil moisture and salinity. In this research, a new EMI system (the CMD mini‐Explorer) is used for subsurface characterization of soil salinity in a drip irrigation system via a joint inversion approach of multiconfiguration EMI measurements. EMI measurements were conducted across a farm where Acacia trees are irrigated with brackish water. In situ measurements of vertical bulk electrical conductivity (σb) were recorded in different pits along one of the transects to calibrate the EMI measurements and to compare with the modeled electrical conductivity (σ) obtained by the joint inversion of multiconfiguration EMI measurements. Estimates of σ were then converted into the universal standard of soil salinity measurement (i.e., electrical conductivity of a saturated soil paste extract – ECe). Soil apparent electrical conductivity (ECa) was repeatedly measured with the CMD mini‐Explorer to investigate the temperature stability of the new system at a fixed location, where the ambient air temperature increased from 26°C to 46°C. Results indicate that the new EMI system is very stable in high temperature environments, especially above 40°C, where most other approaches give unstable measurements. In addition, the distribution pattern of soil salinity is well estimated quantitatively by the joint inversion of multicomponent EMI measurements. The approach of joint inversion of EMI measurements allows for the quantitative mapping of the soil salinity distribution pattern and can be utilized for the management of soil salinity.

APPARENT ELECTRICAL CONDUCTIVITY AS A TOOL FOR EARTHWORM PARAMETERS EVALUATION IN A COMMERCIAL ORCHARD

Non-invasive geophysical methods, such as EMI (Electromagnetic Induction), are innovative tools to study soil biological parameters. This work was carried out to assess correlations between soil earthworm abundance and biomass and soil apparent electrical conductivity (ECa) measured by means of a Profiler GSSI EMP-400. The trial was performed in a 1-hectare commercial peach orchard characterized by a clay loam soil. Mustard method combined with hand-sorting technique were used to sample earthworms in areas (1 x 1 m) falling in cover cropped (CC) and chemically weeded (CW) strips. Statistically significant linear relationships were found between ECa, measured at 13 kHz, and earthworm biomasses in both CC and CW strips (R-2 > 0.72). Instead, abundance earthworm showed relations only in CW strips (R-2 > 0.50). EMI technique seems to be a very efficient tool to locate representative soil sampling areas and spatialize earthworm parameters at field level.

Distribuição espacial da condutividade elétrica do solo medida por indução eletromagnética e da produtividade de cana-de-açúcar

A agricultura atual requer a inserção de novas tecnologias que permitam a identificação dos padrões de solo e planta, possibilitando a determinação da sua variabilidade espacial. O objetivo deste trabalho foi determinar a relação espacial entre a produtividade da cana-de-açúcar, da condutividade elétrica do solo medida por indução eletromagnética e da textura do solo. A área experimental está localizada em Goiana (Pernambuco, Brasil) (Latitude 07°34’25”S, Longitude 34°55’39”W). O experimento foi conduzido em uma área de 6,5 ha. A produtividade da cana-de-açúcar e a condutividade elétrica aparente do solo (CEa) foram medidas em 90 pontos de amostragem distribuídos aleatoriamente na área de estudo. Os mapas de condutividade elétrica do solo (CEa-V e CEa-H) apresentaram comportamento semelhante ao da produtividade de cana-de-açúcar. A correlação linear apresentou valores de 0,74 (produtividade x CEa-H) e 0,85 (produtividade x CEa-V). A condutividade elétrica medida por indução eletromagnética apresentou-se como uma ferramenta importante para a previsão da produtividade da cana-de-açúcar. Os atributos texturais (argila, silte e areia) apresentaram elevada variabilidade espacial.

Impact of Soil Variability on Irrigated and Rainfed Cotton

Cotton is a vital component of the economies of Mid-South states. Producers and landowners are looking for ways to reduce the variability of irrigated yields, and soil apparent electrical conductivity (ECa) is a readily obtained parameter that can indicate soil variability. A study was conducted in 2011 and 2012 at the Fisher Delta Research Center in Portageville, MO, with the objective to determine the impact of soil spatial variability on yield and irrigation water use efficiency for cotton. Observed ECa values were low, consistent with average sand contents that ranged from 59 to 82% in the upper 0.76 m of the soil profile. Spatial autocorrelation was present in the data and thus spatial analyses were used. In 2011, yields for two treatments were not significantly different from the mean field effect; however, the ECa effect was significant, indicating that soil variability impacted yield more than irrigation differences for the two treatments. In 2012, yields for four of the six treatments were significantly different from the mean field effect; however, the ECa effect was not significant. A quadratic equation was fit to the combined data from irrigated and rainfed plots in 2012. The resulting equation had a maximum of 3,372 kg ha-1 at 135 mm total irrigation and the median observed ECa value (3.0 mS m-1). Future efforts will include additional fields and environments, which should increase the understanding of the impact of soil variability and allow for improved selection of optimum management zones for site specific application of water and other inputs.