Modeling within field variation of the compaction layer in a paddy rice field using a proximal soil sensing system

Abstract

A key characteristic of flooded paddy fields is the plough pan. This is a sub‐soil layer of greater compaction and bulk density, which restricts water losses through percolation. However, the thickness of this compacted layer can be inconsistent, with consequences such as variable percolation and leaching losses of nutrients, which therefore requires precision management of soil water. Our objective was to evaluate a methodology to model the thickness of the compacted soil layer using a non‐invasive electromagnetic induction sensor (EM38‐MK2). A 2.7 ha alluvial non‐saline paddy rice field was measured with a proximal soil sensing system using the EM38‐MK2 and the apparent electrical conductivity (ECa) of the wet paddy soil was recorded at a high‐resolution (1.0 × 0.5 m). Soil bulk density (n = 10) was measured using undisturbed soil cores, which covered locations with large and small ECa values. At the same locations (within 1 m2) the depth of the different soil layers was determined by penetrometer. Then a fitting procedure was used to model the ECa – depth response functions of the EM38‐MK2, which involved solving a system of non‐linear equations and a R2 value of 0.89 was found. These predictions were evaluated using independent observations (n = 18) where a Pearson correlation coefficient of 0.87 with an RMSEE value of 0.03 m was found. The ECa measurements allowed the detail estimation of the compacted layer thickness. The link between water percolation losses and thickness of the compacted layer was confirmed by independent observations with an inverse relationship having a Pearson correlation coefficient of 0.89. This rapid, non‐invasive and cost‐effective technique offers new opportunities to measure differences in the thickness of compacted layers in water‐saturated soils. This has potential for site‐specific soil management in paddy rice fields.

Modeling Soil Processes: Review, Key Challenges, and New Perspectives

“…In addition, methods of applying three‐channel RGB data will open up the possibility of using digital cameras and mobile phones for PSS (Viscarra‐Rossel et al, 2009; Aitkenhead et al, 2014). Measurement of soil‐horizon characteristics, including depth of impermeable layers, is also possible with digital imagery (Islam et al, 2014). Based on hyperspectral camera records it has also been possible to provide maps of elemental concentrations for C, N, Al, Fe, and Mn for each mineral soil horizon.…”

The Application of EM38: Determination of Soil Parameters, Selection of Soil Sampling Points and Use in Agriculture and Archaeology

“…However in numerous cases, the alternating influencing factors impede the retrieval of adequate results; for example, both texture and salinity can cause strong vertical fluctuations. Sudduth et al [ 196 ], Sudduth and Kitchen [ 155 , 175 , 176 , 177 , 178 , 179 , 181 , 184 , 185 , 186 , 187 , 188 , 195 , 196 , 197 , 198 , 201 , 202 , 203 , 204 , 205 , 206 , 207 , 208 , 209 ], Kitchen et al [ 213 ] and Noellsch [ 214 ] used EC a to determine the depth to the claypan (the sublayer with 50 to 60% clay, varying in depth from 0.1 to 1 m) in nonsaline soils (Missouri). A high correlation between increasing EC a and decreasing depth to the claypan was observed by Doolittle et al [ 184 ].…”

Dynamic and migration characteristics of soil free amino acids during the whole growth period of rice after application of milk vetch

“…The test soil in this study was clay with an abundance of surface charges, which displayed a strong absorption of FAAs (Zhu et al, 2019), leading to a relatively weak infiltration of FAAS. Moreover, the plough pan of paddy soil is 20 cm below the surface, which has an intercept effect on water infiltration (Islam et al, 2014), so only part of FAAs migrated to the bottom.…”