Concurrent temporal stability of the apparent electrical conductivity and soil water content

Pedrera‐Parrilla, Aura; Pachepsky, Yakov; Taguas, E. V.; Martos-Rosillo, Sergio; Giráldez, Juan Vicente; Vanderlinden, Karl

doi:10.1016/j.jhydrol.2016.10.017

Cited by 22 publications

(9 citation statements)

References 30 publications

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“…The main factors that determine the optimum sowing density are soil moisture 8 , 9 , soil nutrient availability 10 , and choice of hybrid 1 , 3 . Soil water content correlates highly with apparent electrical conductivity (EC a ) of the soil 9 , 11 , whose relatively simple measurement is a low-cost, convenient tool for characterizing soil physicochemical properties 12 , such as moisture, clay content, and organic matter.…”

Section: Introductionmentioning

confidence: 99%

Variable-rate in corn sowing for maximizing grain yield

Silva

Baio

Kolling

et al. 2021

Sci Rep

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Sowing density is one of the most influential factors affecting corn yield. Here, we tested the hypothesis that, according to soil attributes, maximum corn productivity can be attained by varying the seed population. Specifically, our objectives were to identify the soil attributes that affect grain yield, in order to generate a model to define the optimum sowing rate as a function of the attributes identified, and determine which vegetative growth indices can be used to predict yield most accurately. The experiment was conducted in Chapadão do Céu-GO in 2018 and 2019 at two different locations. Corn was sown as the second crop after the soybean harvest. The hybrids used were AG 8700 PRO3 and FS 401 PW, which have similar characteristics and an average 135-day cropping cycle. Tested sowing rates were 50, 55, 60, and 65 thousand seeds ha−1. Soil attributes evaluated included pH, calcium, magnesium, phosphorus, potassium, organic matter, clay content, cation exchange capacity, and base saturation. Additionally, we measured the correlation between the different vegetative growth indices and yield. Linear correlations were obtained through Pearson’s correlation network, followed by path analysis for the selection of cause and effect variables, which formed the decision trees to estimate yield and seeding density. Magnesium and apparent electrical conductivity (ECa) were the most important soil attributes for determining sowing density. Thus, the plant population should be 56,000 plants ha−1 to attain maximum yield at ECa values > 7.44 mS m−1. In addition, the plant population should be 64,800 plants ha−1 at values < 7.44 mS m−1 when magnesium levels are greater than 0.13 g kg−1, and 57,210 plants ha−1 when magnesium content is lower. Trial validation showed that the decision tree effectively predicted optimum plant population under the local experimental conditions, where yield did not significantly differ among populations.

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Section: Introductionmentioning

confidence: 99%

Variable-rate in corn sowing for maximizing grain yield

Silva

Baio

Kolling

et al. 2021

Sci Rep

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“…The EMI methods are widely used for soil mapping, given the high density "on-the-go" surveys of soil apparent electrical conductivity (EC a ) (Adamchuk et al, 2004). EMI studies have been conducted to estimate the spatial variability of SWC (Martínez et al, 2010;De Benedetto et al, 2013) and it seems that the method allows mapping the soil properties with high spatial resolution (Martini et al, 2017;Pedrera-Parrilla et al, 2017). Time-lapse EMI measurements allow separation of the temporally stable contribution of static properties (e.g.…”

Section: Introductionmentioning

confidence: 99%

Repeated geophysical measurements in dry and wet soil conditions to describe soil water content variability

Benedetto

Montemurro

Diacono

2020

Sci. agric. (Piracicaba, Braz.)

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There is an increasing interest in the application of geophysical surveys to assess the soil water content (SWC) variation in both spatial and temporal scales. In this work, a geophysical survey was carried out at an experimental farm in dry and wet conditions. We determined the SWC data measured with the gravimetric method, apparent electrical conductivity by electromagnetic induction (EMI) and amplitude of Ground Penetrating Radar (GPR) data at different frequencies. Geophysical sensors are an efficient tool for soil mapping at high resolution; however; there is a need to improve the knowledge on their capabilities and limitations under field conditions, especially for GPR. The geophysical survey provides an example of the application of these techniques to evaluate the spatial variability of SWC in two different water conditions. The contribution of geophysical data in understanding the spatial variability of SWC was investigated applying both the traditional analysis and spatial techniques. The results indicated that the geophysical data captured the spatial variation of SWC in non-invasively way especially in dry condition. However, they also showed the complex interplay between factors controlling SWC and geophysical responses and the drawbacks of geophysical sensors under inhomogeneous water conditions. Our findings also highlighted that EMI survey provides the potential to map the SWC variability within a relatively short time. The results obtained in this research are important from the agronomical viewpoint, since they allow increasing efficiency of irrigation practices, which is important in times characterized by climate change.

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“…Cassel et al (2015) already detailed the concept of applying electromagnetism for rapid and high-resolution assessment of salinity. The EGS techniques have been applied for salinity and water management across the globe including China (Yao et al, 2016), New Zealand (El-Naggar et al, 2017, Utah (Abdu et al, 2017), the Nile delta (Aboelsoud & Abdel-Rahman, 2017) and Spain (Pedrera-Parrilla et al, 2017). Lia et al (2015) used EGS measurements for studying the impact of saline water irrigation on desert ecology.…”

Section: Introductionmentioning

confidence: 99%

Geophysical Quantification of Water Percolation Quotient in an Alluvial Agricultural Soil

Cassel¹,

Sharma²

2017

JAS

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Efficient water use planning is crucial for the sustainability of irrigated agriculture in California, where alluvial geological materials with indigenous salts impinge on crop growth. To facilitate irrigation scheduling and cultivation planning, it is necessary to determine water percolation quotients (WPQ) required for removal of excess salts from the rhizosphere. In order to estimate real-time WPQ, we conducted electromagnetic geophysical surveys at a saline farmland followed by stochastic computations. Results showed a wide variability in salinity that reached 16 dS m-1 in some locations. About 95% of the surveyed samples surpassed 2 dS m-1. Despite spatially dependent asymmetric variability and skewness (-0.13 to 1.90), the WPQ distribution patterns were consistently quantified with low errors (< 0.06). The sensor responses in the fields reached 100% cumulative frequency at a threshold of 13.6 dS m-1. Up to 49% of WPQ data ranged from 0.1 to 0.2. The WPQ decreased with increasing salinity and the zones with low quotient values represented areas where plant growth could be impaired. High WPQ levels demarcated zones with potential solute dissolution and dispersion. Overall, evaluation of WPQ can benefit irrigation planning and crop management practices while enhancing water use efficiency for agricultural production in farms that have been affected by drought and water shortage, and crop growth can be sustained at WPQ level that maintains salts below the crop tolerance threshold.

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Concurrent temporal stability of the apparent electrical conductivity and soil water content

Cited by 22 publications

References 30 publications

Variable-rate in corn sowing for maximizing grain yield

Variable-rate in corn sowing for maximizing grain yield

Repeated geophysical measurements in dry and wet soil conditions to describe soil water content variability

Geophysical Quantification of Water Percolation Quotient in an Alluvial Agricultural Soil

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