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Kaspar, T. C.; Colvin, T. S.; Jaynes, D. B.; Karlen, Douglas L.; James, David; Meek, D. W.; Pulido, Daniel James; Butler, H.

doi:10.1023/a:1021867123125

Cited by 100 publications

(21 citation statements)

References 31 publications

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“…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. On the other hand, Ebeid et al (1995) reported that (Figure 1).…”

Section: Resultsmentioning

confidence: 99%

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: Resultsmentioning

confidence: 99%

Use of Landsat images for yield evaluation within a small plot

et al. 2014

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“…Several studies have estimated the effects of climatic and soil physical and chemical properties on crop productivity using either simulation models or regression-based techniques. Several researchers have demonstrated the strength of coupling crop models with GIS for agricultural decision support and resource planning at various spatial scales (Dent and Thornton, 1988;Curry et al, 1990;Kaspar et al, 2003;Sarangi et al, 2005). Hansen and Jones (2000) demonstrated several approaches to scale-up field-scale crop model predictions to larger scales.…”

Section: Abstract Understanding the Relationships Between Climatic Vmentioning

confidence: 99%

Development and Evaluation of Ordinary Least Squares Regression Models for Predicting Irrigated and Rainfed Maize and Soybean Yields

Sharma

Rudnick²,

Irmak

2013

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“…Modification of existing schemes is possible by incorporating prior knowledge of spatial variability within the P field. Field elevation in the form of digital elevation models (DEMs) is among the most important attributes that can provide information about the spatial variability in the field (Kravchenko and Bullock, 2000;Kaspar et al, 2003;Rampant and Abuzar, 2004;Jiang and Thelen, 2004). This article reports on research to investigate a method to efficiently implement vehicle-based sampling to collect elevation data in farm fields.…”

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confidence: 99%