A discussion on the significance associated with Pearson’s correlation in precision agriculture studies

Taylor, James A.; Bates, Terence R.

doi:10.1007/s11119-013-9314-9

Cited by 31 publications

(16 citation statements)

References 11 publications

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“…This confirms, for non‐irrigated cool‐climate juice grape production, the findings of Tisseyre et al. () in non‐irrigated warm‐climate wine grape production systems. The first inference from this result is that it would appear redundant to be taking intensive, expensive annual measurements of vine size once the spatial pattern of vine size has been established in a vineyard.…”

Section: Resultssupporting

confidence: 90%

“…This is due to the strong relationships observed for all correlations ( r > 0.5). The difference, however, between n (1147) and the n eff on the raw (∼850) and interpolated (∼28) datasets indicates that for analyses where the r ‐values are not as strong, the choice of sample size ( n or n eff ) may affect the P ‐value and the interpretation of significance (Taylor and Bates ). The autocorrelation within data (and assumption of independence) should always be considered when performing Pearson's correlation on spatial data.…”

Section: Resultsmentioning

confidence: 99%

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Temporal and spatial relationships of vine pruning mass in Concord grapes

Taylor

Bates

2013

Australian Journal of Grape and Wine Research

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Background and Aims: Studies on vine size variation have generally been limited to small plot studies, particularly for correlation with canopy imaging. Research and anecdotal reports indicate there is a temporal stability in the spatial patterns of imagery of the canopy. This study directly examines and quantifies the spatial and temporal variation in vine size, rather than a canopy sensor response, at a block (∼1 ha) level. Methods and Results:The mass of pruned wood for each individual vine was measured for 3 consecutive years in a 0.93-ha vineyard. The spatial and temporal variability in pruning mass was interrogated with geostatistics and map comparison methods. Potential management units were derived from these data and used to verify the temporal response in vine size. Conclusions:The majority of variance in pruning mass occurred at a vine-to-vine scale; however, the autocorrelated variance exhibited a strong, stable spatial structure over 3 consecutive years. Map comparison methods were shown to be an alternate and visually demonstrable method of comparing spatio-temporal patterns. Significance of the Study: The temporal stability of spatial patterns in vine size would indicate that it is not necessary to measure vine size annually and that historical information can drive site-or zone-specific management decisions. The large vine-to-vine variation indicates that high spatial resolution, vine-specific sensing and decision support systems are needed if the objective is to manage as much of the variability in vine size as possible.

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Section: Resultssupporting

confidence: 90%

Section: Resultsmentioning

confidence: 99%

Temporal and spatial relationships of vine pruning mass in Concord grapes

Taylor

Bates

2013

Australian Journal of Grape and Wine Research

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“…Spatial autocorrelation may violate the assumption of independence in the correlation analysis, leading to a biased estimation of variances and correlation coefficient (Dutilleul et al, 1993). Although the Dutilleul correction is commonly applied in ecology, it is not widespread in agricultural studies, which often overestimate the significance of the Pearson's coefficient in spatial datasets (Taylor and Bates, 2013). The analysis at sub-field level was performed using PASSaGE v2 software (Pattern Analysis, Spatial Statistics and Geographic Exegesis; Tempe, AR, USA) to calculate the modified significance test as proposed by Dutilleul (Rosenberg and Anderson, 2011).…”

Section: Discussionmentioning

confidence: 99%

Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform

et al. 2020

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Aim: The recent availability of Sentinel-2 satellites has led to an increasing interest in their use in viticulture. The aim of this short communication is to determine performance and limitation of a Sentinel-2 vegetation index in precision viticulture applications, in terms of correlation and variability assessment, compared to the same vegetation index derived from an unmanned aerial vehicle (UAV). Normalised difference vegetation index (NDVI) was used as reference vegetation index.Methods and Results: UAV and Sentinel-2 vegetation indices were acquired for 30 vineyard blocks located in the south of France without inter-row grass. From the UAV imagery, the vegetation index was calculated using both a mixed pixels approach (both vine and inter-row) and from pure vine-only pixels. In addition, the vine projected area data were extracted using a support vector machine algorithm for vineyard segmentation. The vegetation index was obtained from Sentinel-2 imagery obtained at approximately the same time as the UAV imagery. The Sentinel-2 images used a mixed pixel approach as pixel size is greater than the row width. The correlation between these three layers and the Sentinel-2 derived vegetation indices were calculated, considering spatial autocorrelation correction for the significance test. The Gini coefficient was used to estimate variability detected by each sensor at the within-field scale. The effects of block border and dimension on correlations were estimated.Conclusions: The comparison between Sentinel-2 and UAV vegetation index showed an increase in correlation when border pixels were removed. Block dimensions did not affect the significance of correlation unless blocks were < 0.5 ha. Below this threshold, the correlation was non-significant in most cases. Sentinel-2 acquired data were strongly correlated with UAV-acquired data at both the field (R2 = 0.87) and sub-field scale (R2 = 0.84). In terms of variability detected, Sentinel-2 proved to be able to detect the same amount of variability as the UAV mixed pixel vegetation index.Significance and impact of the study: This study showed at which field conditions the Sentinel-2 vegetation index can be used instead of UAV-acquired images when high spatial resolution (vine-specific) management is not needed and the vineyard is characterised by no inter-row grass. This type of information may help growers to choose the most appropriate information sources to detect variability according to their vineyard characteristics.

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“…Pearson’s correlation analysis was performed to assess the relationship among seed and oil yields, Co - rbcL and Co-rbcS expressions, and P N . The significance of the correlations was tested using the critical value table ( Taylor and Bates, 2013 ).…”

Section: Methodsmentioning

confidence: 99%

Identification of Rubisco rbcL and rbcS in Camellia oleifera and their potential as molecular markers for selection of high tea oil cultivars

Chen¹,

Wang²,

Chen

et al. 2015

Front. Plant Sci.

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Tea oil derived from seeds of Camellia oleifera Abel. is high-quality edible oil in China. This study isolated full-length cDNAs of Rubisco subunits rbcL and rbcS from C. oleifera. The rbcL has 1,522 bp with a 1,425 bp coding region, encoding 475 amino acids; and the rbcS has 615 bp containing a 528 bp coding region, encoding 176 amino acids. The expression level of the two genes, designated as Co-rbcL and Co-rbcS, was determined in three C. oleifera cultivars: Hengchong 89, Xianglin 1, and Xianglin 14 whose annual oil yields were 546.9, 591.4, and 657.7 kg ha-1, respectively. The Co-rbcL expression in ‘Xianglin 14’ was significantly higher than ‘Xianglin 1’, and ‘Xianglin 1’ was greater than ‘Hengchong 89’. The expression levels of Co-rbcS in ‘Xianglin 1’ and ‘Xianglin 14’ were similar but were significantly greater than in ‘Hengchong 89’. The net photosynthetic rate of ‘Xianglin 14’ was significantly higher than ‘Xianglin 1’, and ‘Xianglin 1’ was higher than ‘Hengchong 89’. Pearson’s correlation analysis showed that seed yields and oil yields were highly correlated with the expression level of Co-rbcL at P < 0.001 level; and the expression of Co-rbcS was correlated with oil yield at P < 0.01 level. Net photosynthetic rate was also correlated with oil yields and seed yields at P < 0.001 and P < 0.01 levels, respectively. Our results suggest that Co-rbcS and Co-rbcL in particular could potentially be molecular markers for early selection of high oil yield cultivars. In combination with the measurement of net photosynthetic rates, the early identification of potential high oil production cultivars would significantly shorten plant breeding time and increase breeding efficiency.

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A discussion on the significance associated with Pearson’s correlation in precision agriculture studies

Cited by 31 publications

References 11 publications

Temporal and spatial relationships of vine pruning mass in Concord grapes

Temporal and spatial relationships of vine pruning mass in Concord grapes

Comparing vineyard imagery acquired from Sentinel-2 and Unmanned Aerial Vehicle (UAV) platform

Identification of Rubisco rbcL and rbcS in Camellia oleifera and their potential as molecular markers for selection of high tea oil cultivars

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