Relationship between cotton yield and soil electrical conductivity, topography, and Landsat imagery

Guo, Wenxuan; Maas, Stephan J.; Bronson, K. F.

doi:10.1007/s11119-012-9277-2

Cited by 63 publications

(55 citation statements)

References 33 publications

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“…The same results were obtained for the comparison between PL/PR and G/NDVI and inversely with MSI. Guo et al (2012) obtained similar results for the comparison of PL/PR with cotton yield and four bands in a Landsat 5 TM image. The types of curvature (Figure 2) indicate the directions of soil water and nutrient movement.…”

Section: Resultssupporting

confidence: 58%

“…Theoretically, a concave area should provide more available water and nutrients supporting plant growth. In this study with oat and winter wheat and in another study by Guo et al (2012) with cotton, yield was positively correlated with PR, which means that yield was higher at the convex curvature locations. Kaspar et al (2003) stated that maize yield was negatively correlated with both curvatures, especially in dry seasons.…”

Section: Resultssupporting

confidence: 50%

“…The types of curvature (Figure 2) indicate the directions of soil water and nutrient movement. Convex curvature is associated with soil erosion and concave curvature is with deposition (Guo et al 2012). Theoretically, a concave area should provide more available water and nutrients supporting plant growth.…”

Section: Resultsmentioning

confidence: 99%

“…The following topography attributes are used most widely for explaining topography influence on yield: digital elevation models (DEM) (Iqbal et al 2005, Murphy et al 2009), relative field elevation (Serrano et al 2013), slope (Pilesjö et al 2005), curvature (Guo et al 2012), flow accumulation (Marques da Silva and Silva 2008, Kumhálová et al 2013), topography wetness index (TWI) (Schmidt andPersson 2003, Sørensen et al 2006), distance to flow lines (Marques da Silva and Silva 2006) and compound topographic index (Momm et al 2013).…”

mentioning

confidence: 99%

“…Spatial resolution of Landsat TM image is 30 m. Nevertheless, Landsat images are often used for explaining plant and soil variability in agricultural plots because of the possibility to use several spectral bands. Guo et al (2012) evaluated spatial variability of cotton yield in a 50-ha field in relation to soil apparent electrical conductivity, topography, and bare soil brightness obtained from remote sensing image (Landsat 5 TM) over multiple growing seasons. Julien et al (2011) tested a method of distinguishing plant species in agricultural area in Spain on the basis of multitemporal data from Landsat 5 TM image using the Yearly Land Cover Dynamics method.…”

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confidence: 99%

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Use of Landsat images for yield evaluation within a small plot

et al. 2014

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Many factors can influence crop yield. One of the most important factors is topography, which can play a crucial role especially in dry years. Plant variability can be monitored by many methods. This paper evaluates the suitability of vegetation indices derived from satellite Landsat 5 TM data in comparison with yield, curvature and topography wetness index over a relatively small field (11.5 ha). Imageries were chosen from the years 2006 and 2010, when oat was grown and from 2005 and 2011, when winter wheat was grown. These images were taken in June in the same growth stage for every crop. It was confirmed that derived indices from Landsat images can be used for comparison with yield and selected topographic attributes and it can explain yield variability, which can be influenced by water distribution during growth stages. Correlation coefficient between moisture stress index and winter wheat yield was -0.816 in the image acquisition date of 4. 6. 2011.

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Section: Resultssupporting

confidence: 58%

Section: Resultssupporting

confidence: 50%

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Use of Landsat images for yield evaluation within a small plot

et al. 2014

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Statistical models of yield in on‐farm precision experimentation

et al. 2021

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On-farm precision experimentation (OFPE) is increasingly conducted using variablerate technology and precision agriculture (PA) equipment to measure the effect of changes in input application rates on yields and profits at specific fields. Classical linear regression models and new Bayesian and machine learning regressions for spatial data can be used to investigate site-specific crop response from georeferenced data. The objective of this work was to compare statistical models that can be used by researchers analyzing OFPE data to estimate crop response and better describe its spatial within-field variability. Three statistical models estimating the responses to N rates, seed rates, and site-specific soil properties from eight OFPEs were compared:(a) linear regression (LR) for spatially correlated errors, (b) Bayesian regression (BR) with random site effects, and (c) random forest regression (RF) with kriged residuals. Models were adjusted to account for spatial variation in yield response, and with and without field characteristic covariates. Modeling spatial correlation and including plot covariates improved yield predictions. Differences among methods proved to be indistinguishable with respect to average explained variance, correlation between predicted and observed values, and mean square prediction errors (PEs).However, BR and RF outperformed LR in site-specific prediction accuracy, with BR predictions having lower predictive uncertainty than RF predictions. The hierarchical Bayesian model for spatial data is a useful tool to process OFPE data, allowing direct derivation of linear coefficients and prediction uncertainty measures related to site-specific yield responses.

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Apparent electrical conductivity measurements in an olive orchard under wet and dry soil conditions: significance for clay and soil water content mapping

et al. 2016

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Mediterranean olive trees traditionally grow under rainfed conditions, on poor soils with steep slopes. Rainfall is mainly concentrated during autumn and winter and is characterized by intense rain pulses, separated by dry periods. The use of electromagnetic induction (EMI) techniques in these olive orchards might be questioned since EMI surveys are generally recommended to be performed under moist soil conditions. A 6.7 ha olive orchard was surveyed for EMI-based apparent electrical conductivity (ECa), both under wet and dry soil conditions. In addition, 48 soil samples were analyzed for soil texture and for soil water content (SWC) under both soil conditions. The relationships between ECa, soil texture and SWC, under both soil conditions were evaluated. Despite the significantly larger ECa values measured during the wet survey as compared to the dry survey, a similar spatial correlation structure was found, indicating temporally stable ECa patterns. Significant correlations (r) were found between both surveys for ECa (r = 0.67) and for SWC (r = 0.63). The correlation between SWC and clay content exceeded 0.60 for both surveys, and the correlation between ECa and clay content was twice as high under wet soil conditions as compared to dry soil. In both situations, the ECa surveys revealed the same patterns of soil texture, indicating that moist soil conditions are not an absolute prerequisite for the use of EMI to map the spatial variability of these soil properties. Nonetheless, measuring the ECa under different moisture conditions can provide additional information about soil moisture dynamics

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Relationship between cotton yield and soil electrical conductivity, topography, and Landsat imagery

Cited by 63 publications

References 33 publications

Use of Landsat images for yield evaluation within a small plot

Use of Landsat images for yield evaluation within a small plot

Statistical models of yield in on‐farm precision experimentation

Apparent electrical conductivity measurements in an olive orchard under wet and dry soil conditions: significance for clay and soil water content mapping

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