Analysis of the results of the experiences conducted by FAO in the use of GPS for crop area measurement Elisabetta Carfagna University of Bologna, Department of Statistics FAO Addis Abeba, 27 – 28 November 2008

Aim of the Aim of the experiences project GCP/INT/903/FRA Statistics Division of FAO pilot surveys in Cameroon, Niger, Madagascar and Senegal Aim: –Assessing the capability of measuring areas on the ground with good accuracy with a standard GPS

Main characteristics of the data set A statistical sampling technique not adopted for selecting the sample of 207 plots (purposive sample) kinds of GPS used: –Garmin 12 xl (G12) –Garmin 72 (G72) –Garmin 60 (G60) –Garmin Etrex Ventura (GE) –Magellan Explorist 400 (M400)

Countries Cameroun 36 plots measured with G60, G72 and M400 Niger 46 plots with G12, G72, M400, GE (45). Senegal, 75 plots measured with G60, G72, M400 Madagascar 86 plots allocated but measures with GPS available only for 50 plots: 24 plots with GE only and 26 plots with G72 only. –Other measurements not available due to technical difficulties, for example problems with the signal? Considering also the 50 plots in Madagascar, 207 plots In many cases, the measurement with a kind of GPS repeated three times

Size of plots and tree canopy cover Size of plots ranges from 27 to 34,700 square meters, median , mean , standard deviation Tree canopy cover: 28 have dense cover (cover = 1) 5 have partial cover (cover = 2) 124 have no cover (cover = 3) Total 157 (no information about tree canopy is reported for Madagascar) Partial tree canopy cover is very little represented; thus we cannot assess if this kind of cover affects the measurement through GPSs.

Tree canopy cover by country Camerun –28 plots with dense cover measured with G60, G72, M400. –5 plots with partial cover with G60, G72 and M400 –3 plots with no cover with G60, G72 and M400. Niger –no plots were with dense cover and with partial cover –46 plots with no cover measured with G12, G72, M400 (45 with GE) Senegal –no plots with dense cover and with partial cover –75 plots with GPS G60, G72, M400.

Weather conditions On 18 plots with cloudy weather (climate 1) on 5 plots is raining (climate 2) on 182 is sunny (climate 3) We cannot say much about weather conditions different from sunny

Position For 205 plots information regarding the position available: –172 plain (position 4) –5 plots on the top of a hill (position 1) –11 on the side of a hill (position 2) –17 at the feet of the hill (position 3). We can draw conclusions valid almost only for plots on the plain

Position by country Camerun –All 36 plots on a plain Madagascar –4 plots G72, 1 GE are on the top of the hill –5 plots G72, 6 GE on the side of a hill –8 plots G72, 9 GE at the feet of the hill –8 plots G72, 9 GE on a plain. Niger –all 46 plots on the plain, measured with G12, G72 and M400, 45 with GE. Senegal –all the 75 plots on the plain, G60, G72 and M400.

Summary statistics all data set Difference between the are measured by meter and compass and the area measured by GPS –Mean 98 square meters, median 68 Relative difference = the difference divided by the real measure –Mean 8.3% median 3.7% –The area measured with compass and meter is generally larger than with GPS

Estimate the real measures through the measures made by GPS with a linear regression model R-squared = Coef. Std. Err. t P>|t| [95% Conf. Interval] super | cons | Prob > F =

How precise can be the measure of a plot area given by the GPS receiver? It depends on the tree canopy cover Plotsall coverdense coverno cover 5% 0.7% 3.0%0.6% 10% 1.4% 5.3%1.2 % 25% 3.6%17.8%3.1% 50%10.5%37.2%8.7 %

Does the plot size affect the precision of measurements? No evidence Difference Relative difference

Does the plot size affect the precision of measurements? 3 clusters Let us identify clusters with plots with similar area

Does the plot size affect the precision of measurements? 3 clusters cluster 1 medium size Obs Mean Std. Dev. Min Max __________________________________________________________ cluster 2 small size Obs Mean Std. Dev. Min Max __________________________________________________________ cluster 3 large size Obs Mean Std. Dev. Min Max

Difference Small size Medium size Large size

Relative difference Small size Medium size Large size

Has the type of GPS receiver an impact on the accuracy? Yes The Garmin 60 is the only GPS used in the FAO experiences which has produced almost unbiased measures. Variable | Obs Mean Std. Dev. Min MaxMedian s_1 | super | difference| rel_diff |

Estimate the compass measures through the measures made by GPS with a linear regression model G s_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] super | cons | Prob > F = R-squared =

Estimate the compass measures through the measures made by GPS with a linear regression model G60 medium plot size s_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] super | cons | Prob > F = R-squared =

Estimate the compass measures through the measures made by GPS with a linear regression model G60 small plot size s_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] super | cons | Prob > F = R-squared = Better R-squared than with plots of medium size >

G60 measurement 1 Student’s t test for two variables observed on one sample Normal distribution of the difference (real measure minus measure with GPS) is assumed the variances of the two variables on the sample are assumed to be equal, although unknown. One-sample t test Variable | Obs Mean Std. Err. Std. Dev. [95% Conf. Interval] Differ~e | Degrees of freedom: 108 Ho: mean(Difference) = 0 Ha: mean 0 t = t = t = P |t| = P > t =

Non parametric tests Test if the pared differences have median zero Assumption: the differences are continuous random variables, symmetric, independent and with the same median. Wilcoxon signed-rank test Two-sided test: Ho: median of compass – G60 = 0 vs. H1: median of compass – G60 different from 0 Pr(number of positive >= 55 or number of negative >= 55) = min(1, 2*Binomial(n = 106, x >= 55, p = 0.5)) = Ho not refused Ho: S_1 = SUPER z = Prob > |z| = Ho not refused

G12 All measurements made on plots without tree canopy cover Variable | Obs Mean Std. Dev. Min MaxMedian s_1 | super| Difference| Rel_diff |

G12 One-sample t test Ho: mean(Difference) = 0 Ha: mean 0 t = t = t = P |t| = P > t = Wilcoxon signed-rank test Ho: s_1 = super z = Prob > |z| = Sign test refused

G12 regression S_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] SUPER | cons | R-squared = Better R-squared than with G > But only in Niger and without tree canopy cover

M400 M400 Parametric and non parametric tests refused regression S_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] SUPER | cons | R-squared = Not as good as R-squared with G <

G72 G72 Parametric and non parametric tests refused regression s_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] super | cons | R-squared = Not as good as R-squared with G < Difference Mean 81 median 46 Relative difference Mean 8.3% median 3.2%

G60 measurement by tree canopy cover

Estimate compass measures by G60 with dense tree canopy cover s_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] super | _cons | Prob > F = Adj R-squared =

Estimate compass measures by G60 without tree canopy cover s_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] super | _cons | Prob > F = Adj R-squared =

Does the precision of measurement improve repeating the measurement? R squared for measurement 1, 2, 3 G G G GE M

Can we trust what the plot workers declare? NO s_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] s_11 | cons | R-squared =

Can we trust what the field enumerators declare? No s_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] s_12 | cons | Prob > F = R-squared =

How faster is it to measure areas with GPS? Traditional method long measuring an area with a GPS takes the time of walking around the plot, possible additional manipulations Magellan 400 GPS measures more than 4 times shorter than the traditional measures For large plots, this ratio can go up to 17 times No significant differences amongst the receivers tested Magellan 400 more complex to use according to the field enumerators.

Thank you for your kind attention