{"id":2015,"date":"2020-04-10T12:00:27","date_gmt":"2020-04-10T10:00:27","guid":{"rendered":"http:\/\/cliniquedugazon.fr\/?p=2015"},"modified":"2020-04-11T10:43:30","modified_gmt":"2020-04-11T08:43:30","slug":"minimal-sampling-on-hybrid-sports-field-pitches-with-pogo-mini-sensor","status":"publish","type":"post","link":"https:\/\/cliniquedugazon.fr\/index.php\/2020\/04\/10\/minimal-sampling-on-hybrid-sports-field-pitches-with-pogo-mini-sensor\/","title":{"rendered":"Minimal sampling of soil moisture on hybrid sports fields with POGO Mini sensor"},"content":{"rendered":"

Introduction<\/em> <\/i><\/h1>\n
\n

\u201cPrecision greenkeeping<\/em>\u201d requires usage of complex technological decision support tools to help greenkeepers or intendants. Soil moisture sensors are part of this tool panel for proper irrigation practices.<\/p>\n

In practice, intendants make soil moisture measurements on a regular basis to get a mean of the whole pitch. This average is highly dependent on sampling strategy and number of measurements for each measurement set.<\/p>\n

The aim of this article is to estimate the minimum number of measurements with the POGO Mini<\/a> sensor (Hydraprobe<\/a> Sensor from Stevens Water<\/a> company) on hybrid football pitch to get a mean statistically representative of the whole pitch.<\/p>\n<\/div>\n

Material and method<\/h1>\n

The pitch used in this study is a hybrid first league stadium made of Airfibr<\/a><\/em> system (technology made of fine sand, cork and synthetic fibers produced by the French company Natural Grass<\/a>). It is equipped with 35 sprinklers (7×5 Hunter<\/a> I40<\/em> sprinklers).<\/p>\n

During 10 months (from June 2019 to March 2020), 64 soil moisture measurement series have been taken with the soil sensor POGO Mini<\/a><\/em>. Between 20 and 105 measurements has been made for each series with a standard series of 63 measurements that I consider reliable but is time consuming for an intendent if realized daily.<\/p>\n

Measurements are realized on a grid sampling strategy made of 3 parallel lines with a fixed spacing between two points (see figure 1). For the standard series, spacing is 5 meters. For series of 20 and 105 measurements, spacing is 3 and 15 meters respectively. This grid sampling strategy has been set initially to take in account spatial variation of soil moisture in the field (geostatistic<\/em>) and identify eventual tendencies with dry and wet areas in the pitch. For this study, the nonrandom strategy implies statistical bias that is important to mention. In a review made by Lawrence et al.1<\/sup> in 2020<\/a><\/em> on soil sampling strategies, a similar bias was identified on data used for the study.<\/p>\n

\"Strat\u00e9gie<\/a>
Figure 1 : Sampling strategy of the pitch consisting of 3 longitudinal transects. Licence :<\/em><\/strong> Clinique du gazon<\/em><\/a>, <\/em>tous droits r\u00e9serv\u00e9s<\/em><\/a><\/figcaption><\/figure>\n

For the 64 available series, only data set with more than 60 measurements have been selected for this study. Method described by Micah Woods<\/a><\/em> (in his article about the minimal number of soil samples to take for calcium concentration assessment<\/a>) which was initially derived from the review from Lawrence et al. has been adapted and used for this data set.<\/p>\n

The method consists of :<\/p>\n

a)<\/em><\/strong> high number of random draws (1000 in the present study) made on sub-samples of each data set of more than 60 measurements. Arithmetic or geometric mean is then calculated.<\/p>\n

Geometric mean is used if soil moisture distribution follows a lognormal law and arithmetic mean is used if distribution follows a normal law made on Lawrence et al.<\/a><\/em> recommandations1<\/sup>. A Shapiro-Wilk<\/em> statistical test has been used to assess if distribution or his logarithm was normal for each data set.<\/p>\n

1000 pairs, triplets, quadruplets, until the full number of measurements n<\/em> are therefore used from each data set and arithmetic or geometric means are then calculated.<\/p>\n

The aim is to see what would have been the mean if 2,3,4, \u2026, n-1<\/em> measurements would have been realized instead of total number n<\/em> of measurements for each date.<\/p>\n

Precision is fixed at 1% of soil moisture around the mean of n total measurements for each data set (mean of n total measurements +\/-1%: <\/em>all the points located in the rectangle formed by dotted lines in figure 2).<\/p>\n

\"Graphique<\/a>
Figure 2 : Means of soil moisture (%) for 200 random draws get for each number of subsamples (between 1 and the n maximal number of measurement for each data set). Only 200 draws has been taken for this graphic to have more readability (every vertical line has 200 points). Licence :<\/em><\/strong>\u00a0Clinique du gazon<\/em><\/a>,\u00a0<\/em>tous droits r\u00e9serv\u00e9s<\/em><\/a><\/figcaption><\/figure>\n

b)<\/em><\/strong> Means between this precision interval are then selected. Number of means in this precision interval is then evaluated for each sub-sample of 2,3,4, \u2026, n measurements.<\/p>\n

c)<\/em><\/strong> The number of sub-samples from which 95% of means are located in the precision interval (1% soil moisture around the mean of total number of measurements n<\/em>) is then assessed (see figure 3). Precision is finally 1% soil volumetric moisture with an error of 5%.<\/p>\n

\"Proportion<\/a>
Figure 3 : Percentage of means values that are included in the precision interval of 1% soil moisture for each subsamples and for one data set. Licence :<\/em><\/strong> Clinique du gazon<\/em><\/a>, <\/em>tous droits r\u00e9serv\u00e9s<\/em><\/a><\/figcaption><\/figure>\n

Steps a)<\/em><\/strong>, b)<\/em><\/strong> and c)<\/em><\/strong> are repeted for each of 26 data sets.<\/p>\n

The minimal number of measurements that would have been done to get the mean of total number of measurements is finally assessed for each date.<\/p>\n

Mean, quartiles of these minimal numbers of subsamples are then calculated on 26 data sets and an interval of minimal number of measurements is estimated.<\/p>\n

All statistical computing is made with R free and open source software<\/a>.<\/p>\n

Results<\/h1>\n

Soil moisture distribution<\/h2>\n

For Pogo Mini<\/em> soil sensor, soil moisture distribution follows a normal in 80% of cases (see figure 4). Only 4 series do not follow a normal law. These series do not follow a lognormal law too. Arithmetic mean has been consequently used for all data sets.<\/p>\n

\"Histogramme<\/a>
Figure 4 : Histogram of soil moisture measured with POGO Mini sensor for one data set with a distribution following a normal law. Licence :<\/em><\/strong> Clinique du gazon<\/em><\/a>, <\/em>tous droits r\u00e9serv\u00e9s<\/em><\/a><\/figcaption><\/figure>\n

Observed cases where histograms which do not follow normal distribution are:<\/p>\n