Relationship between Rice Yield and Apparent Electrical Conductivity of Paddy Soils

Ezrin, M. H.; Amin, M. S. M.; Anuar, Abdul Rahim; Wayayok, Aimrun

doi:10.3844/ajassp.2010.63.70

Cited by 18 publications

(11 citation statements)

References 14 publications

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“…Johnson et al (2003) found corn yield to have a positive correlation with ECa. Lund et al (2000) and Ezrin et al (2010) both indicated that nonlinear models can explain the ECa-yield relationship better than linear models. However, Corwin et al (2003) pointed out that crop yield may inconsistently correlate with soil ECa.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a local regression kriging approach for mapping apparent electrical conductivity of soil (ECa) at high resolution

Sun

Whelan

Minasny

et al. 2012

Z. Pflanzenernähr. Bodenk.

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Apparent electrical conductivity of soil (ECa) is a property frequently used as a diagnostic tool in precision agriculture, and is measured using vehicle-mounted proximal sensors. Crop-yield data, which is measured by harvester-mounted sensors, is usually collected at a higher spatial density compared to ECa. ECa and crop-yield maps frequently exhibit similar spatial patterns because ECa is primarily controlled by the soil clay content and the interrelated soil moisture content, which are often significant contributors to crop-yield potential. By quantifying the spatial relationship between soil ECa and crop yield, it is possible to estimate the value of ECa at the spatial resolution of the crop-yield data. This is achieved through the use of a local regression kriging approach which uses the higher-resolution crop-yield data as a covariate to predict ECa at a higher spatial resolution than would be prudent with the original ECa data alone. The accuracy of the local regression kriging (LRK) method is evaluated against local kriging (LK) and local regression (LR) to predict ECa. The results indicate that the performance of LRK is dependent on the performance of the inherent local regression. Over a range of ECa transect survey densities, LRK provides greater accuracy than LK and LR, except at very low density. Maps of the regression coefficients demonstrated that the relationship between ECa and crop yield varies from year to year, and across a field. The application of LRK to commercial scale ECa survey data, using crop yield as a covariate, should improve the accuracy of the resultant maps. This has implications for employing the maps in crop-management decisions and building more robust calibrations between field-gathered soil ECa and primary soil properties such as clay content.

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Section: Introductionmentioning

confidence: 99%

Evaluation of a local regression kriging approach for mapping apparent electrical conductivity of soil (ECa) at high resolution

Sun

Whelan

Minasny

et al. 2012

Z. Pflanzenernähr. Bodenk.

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“…However, research relating EC a and paddy rice yield is rare. One exception is Ezrin et al (2010), but these authors used soil resistivity measurements (requiring soil contact) under dry land conditions. Yet, they reported a significant positive relation between EC a and paddy rice yield.…”

Section: Introductionmentioning

confidence: 98%

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

et al. 2011

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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 (ECa) information could be used to support soil management at a within-field level. A floating sensing system (FloSSy) was built to record ECa 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 x 1 m) ECa 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 ECa 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 ECa classes, with the smallest ECa 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

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“…The relationship between EC a and yield of various crops have been investigated, including corn (Zea mays L.) [16,24], soybean (Glycine max (L.) Merr.) [16,24], grain sorghum (Sorghum bicolor (L.) Moench) [25], winter wheat (Triticum aestivum L.) [26,27], and rice (Oryza sativa L.) [28][29][30]. Few field studies have investigated the relationship between cotton yield and EC a .…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Trends of Irrigated Cotton Yield in the Southern High Plains

Guo

2018

Agronomy

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Understanding spatial and temporal variability patterns of crop yield and their relationship with soil properties can provide decision support to optimize crop management. The objectives of this study were to (1) determine the spatial and temporal variability of cotton (Gossypium hirsutum L.) lint yield over different growing seasons; (2) evaluate the relationship between spatial and temporal yield patterns and apparent soil electrical conductivity (ECa). This study was conducted in eight production fields, six with 50 ha and two with 25 ha, on the Southern High Plains (SHP) from 2000 to 2003. Cotton yield and ECa data were collected using a yield monitor and an ECa mapping system, respectively. The amount and pattern of spatial and temporal yield variability varied with the field. Fields with high variability in ECa exhibited a stronger association between spatial and temporal yield patterns and ECa, indicating that soil properties related to ECa were major factors influencing yield variability. The application of ECa for site-specific management is limited to fields with high spatial variability and with a strong association between yield spatial and temporal patterns and ECa variation patterns. For fields with low variability in yield, spatial and temporal yield patterns might be more influenced by weather or other factors in different growing seasons. Fields with high spatial variability and a clear temporal stability pattern have great potential for long-term site-specific management of crop inputs. For unstable yield, however, long-term management practices are difficult to implement. For these fields with unstable yield patterns, within season site-specific management can be a better choice. Variable rate application of water, plant growth regulators, nitrogen, harvest aids may be implemented based on the spatial variability of crop growth conditions at specific times.

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Relationship between Rice Yield and Apparent Electrical Conductivity of Paddy Soils

Cited by 18 publications

References 14 publications

Evaluation of a local regression kriging approach for mapping apparent electrical conductivity of soil (ECa) at high resolution

Evaluation of a local regression kriging approach for mapping apparent electrical conductivity of soil (ECa) at high resolution

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

Spatial and Temporal Trends of Irrigated Cotton Yield in the Southern High Plains

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