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

2. 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

3. 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)

4. 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

5. Size of plots and tree canopy cover Size of plots ranges from 27 to 34,700 square meters, median 2613.41, mean 4413.637, standard deviation 5304.851. 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.

6. 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.

7. 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

8. 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

9. 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.

10. 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

11. Estimate the real measures through the measures made by GPS with a linear regression model R-squared = 0.9342 Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- super | 0.9316145 0.0062525 149.00 0.000 0.9193503 0.9438788 cons | 396.5125 43.52947 9.11 0.000 311.1302 481.8948 Prob > F = 0.0000

12. 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 %

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

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

15. Does the plot size affect the precision of measurements? 3 clusters cluster 1 medium size Obs Mean Std. Dev. Min Max ------------------------------------------------------- 585 8551.528 2293.875 5300 13000 __________________________________________________________ cluster 2 small size Obs Mean Std. Dev. Min Max -------------------------------------------------------- 1785 1728.818 1322.87 27 4900 __________________________________________________________ cluster 3 large size Obs Mean Std. Dev. Min Max -------------------------------------------------------- 165 18787.79 7045.429 13689 34700

16. Difference Small size Medium size Large size

17. Relative difference Small size Medium size Large size

18. 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 | 323 3547.424 5073.798 27 347001800 super | 323 3518.842 5269.395 8 378781210 difference| 323 28.58254 671.7525 -3178 82650 rel_diff | 323 0.0643715 0.2638027 -0.63 0.9720

19. Estimate the compass measures through the measures made by GPS with a linear regression model G60 ------------------------------------------------------------------------------ s_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- super | 0.9554438.0066666 143.32 0.000 0.9423281 0.9685596 cons | 185.3686 42.19637 4.39 0.000 102.3523 268.385 Prob > F = 0.0000 R-squared = 0.9846

20. 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 | 0.817605 0.0398015 20.54 0.000 0.7382234 0.8969867 cons | 1682.598 380.7117 4.42 0.000 923.2927 2441.904 Prob > F = 0.0000 R-squared = 0.8557

21. 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 | 0.9426844 0.0123935 76.06 0.000 0.9182725 0.9670962 cons | 128.5081 23.38868 5.49 0.000 82.43858 174.5775 Prob > F = 0.0000 R-squared = 0.9595 Better R-squared than with plots of medium size 0.9595 > 0.8557

22. 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 | 109 59.47789 88.87193 927.8502 -116.6817 235.6375 ------------------------------------------------------------------------------ Degrees of freedom: 108 Ho: mean(Difference) = 0 Ha: mean 0 t = 0.6693 t = 0.6693 t = 0.6693 P |t| = 0.5048 P > t = 0.2524

23. 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)) = 0.7709 Ho not refused Ho: S_1 = SUPER z = 0.002 Prob > |z| = 0.9988 Ho not refused

24. G12 All measurements made on plots without tree canopy cover Variable | Obs Mean Std. Dev. Min MaxMedian -------------+-------------------------------------------------------- s_1 | 507 4413.637 5309.043 27 347002613.41 super| 138 5853.696 5478.081 446 260643169 Difference| 138 253.622 864.3609 -3645 4093.7 338.695 Rel_diff | 138.1093388.1582909 -.2803846.51344990.0918371

25. G12 One-sample t test Ho: mean(Difference) = 0 Ha: mean 0 t = 3.4469 t = 3.4469 t = 3.4469 P |t| = 0.0008 P > t = 0.0004 Wilcoxon signed-rank test Ho: s_1 = super z = 4.615 Prob > |z| = 0.0000 Sign test refused

26. G12 regression S_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- SUPER |.9668884.0090154 107.25 0.000.9490848.984692 cons | 206.1412 62.99313 3.27 0.001 81.74188 330.5405 R-squared = 0.9862 Better R-squared than with G60 0.9862 > 0.9846 But only in Niger and without tree canopy cover

27. M400 M400 Parametric and non parametric tests refused regression S_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- SUPER | 0.7679898.0343822 22.34 0.000.7000717.835908 cons | 980.554 257.9426 3.80 0.000 471.0176 1490.09 R-squared = 0.7630 Not as good as R-squared with G60 0.7630 < 0.9846

28. G72 G72 Parametric and non parametric tests refused regression s_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- super | 0.8397707.0226164 37.13 0.000.7942974.8852439 cons | 749.9634 193.2398 3.88 0.000 361.4287 1138.498 R-squared = 0.9664 Not as good as R-squared with G60 0.9664 < 0.9846 Difference Mean 81 median 46 Relative difference Mean 8.3% median 3.2%

29. G60 measurement by tree canopy cover

30. Estimate compass measures by G60 with dense tree canopy cover s_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- super |.9434249.0152041 62.05 0.000.9131497.9737001 _cons | 435.2425 129.5037 3.36 0.001 177.3676 693.1174 Prob > F = 0.0000 Adj R-squared = 0.9801

31. Estimate compass measures by G60 without tree canopy cover s_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- super | 0.9765718 0.0056162 173.88 0.000 0.9655054 0.9876381 _cons | 24.15063 31.18738 0.77 0.440 -37.30172 85.60297 Prob > F = 0.0000 Adj R-squared = 0.9925

32. Does the precision of measurement improve repeating the measurement? R squared for measurement 1, 2, 3 G12 0.9815 0.9722 0.9790 G72 0.9862 0.98080.9856 G60 0.97280.99080.9934 GE 0.9664 0.90310.9616 M400 0.7630 0.9522 0.8853

33. Can we trust what the plot workers declare? NO s_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- s_11 |.5833111.0185587 31.43 0.000.5468382.6197841 cons | 1029.492 184.1744 5.59 0.000 667.5386 1391.445 R-squared = 0.6880

34. Can we trust what the field enumerators declare? No s_1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- s_12 |.8885893.0118509 74.98 0.000.8653076.9118711 cons | 195.6716 82.86972 2.36 0.019 32.86953 358.4736 Prob > F = 0.0000 R-squared = 0.9156

35. 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.

36. Thank you for your kind attention elisabetta.carfagna@unibo.it