A Sampling Strategy in Mapping of Soil Parameters of Paddy Field* I MADE Anom Sutrisna Wijaya*1, Sakae SHIBUSAWA*1, Akira SASAO*'

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1 A Sampling Strategy in Mapping Soil Parameters Paddy Field* I MADE Anom Sutrisna Wijaya*1, Sakae SHIBUSAWA*1, Akira SASAO*' Abstract The study to evaluate sampling design were conducted in a 100mX50m paddy located in Kyoto, in a l56mx77m paddy located in Ishikawa prefecture, Japan. The variability five parameters: moisture, SOM, NO3-N, ph EC was described by collecting reflectance at interval 1mX5m 1mX10m Kyoto Ishikawa, respectively. The Kriged stard errors seven subsets Kyoto data, eight subsets Ishikawa data resulted different scanning intervals, were calculated. As a result, cumulative Kriged stard error decreased with an increment in sampling, followed by slightly increase after a minimum value was achieved. The optimal sampling s mapping each parameters are presented. [Keyword] sampling strategy, mapping, real time spectrophotometer I Introduction Understing providing accurate inmation about spatial variability is a basic step in application site-specific management to precision farming. Consequently, accurate maps should be produced to provide a good quality inmation acquired in. In order to reduce error in an interpolated map a high amount samples is required, but cost sampling by means conventional methods is in many cases unafdable. In our laboratory a real-time spectrophotometer9) 10) has been developed variability moisture (MC), organic matter (SOM), nitrate nitrogen (NO3-N), ph electric conductivity (EC) solution has been described in a 1mX5m grid5) This sensor allows us to collect larger quantity data, but a high amount inmation does not mean a higher accuracy. The best sampling design a specific should be used in development maps, taking account a specific parameter every individual, which has an optimal sampling with a minimum interpolation error. Several studies have been done to provide a good map. Webster, R. TM. Burgess11) described method minimizing sampling eft estimating properties in small region. Burgess, * Partly presented at 60th JSAM Annual Meeting (Tottori University), April 2001 * 1 JSAM member, Faculty Agriculture, Tokyo University Agriculture Technology, Fuchu-shi, Saiwai-cho , Tokyo, Japan TEL , TM. R. Webster2) observed optimal boundaries spacing mapping, Chang, J. et al.4) determined impact grid distance on reproducibility spatial variability measurements. McBratney, A.B, M.J. Pringle5) studied use averaging proportional variogram in planning an optimal sampling scheme precision farming. Mueller T.G., et al.7) assessed impact spatial structure, sampling intensity, interpolation method on map quality. Mulla D.J., AC. Sekely, M. Beatty8) evaluated compared cost benefits various sampling scheme in mapping lime application. The objective this study is to observe spatial variability in paddy, evaluate sampling design describing spatial variability parameters evaluated with real-time spectrophotometer. II Materials Methods 1. Data collection The study was conducted in a 0.5 ha (100mx50m) paddy located at an experimental farm Kyoto University, Kyoto, Japan, in a 1.2 ha (156mX77m) private paddy located in Ishiwaka prefecture, Japan. The type both s are clay loam with 23% clay, 30% silt, 47% s Kyoto, 18% clay, 29% silt, 53% s Ishikawa, respectively. The parameters s were measured by collecting reflectance using revised real-time spectrophotometer9)10) that was developed in our laboratory. The reflectances were scanned at wavelengths nm with a resolution 3.2 nm in wavelength between 400 nm 950 nm, 6.0 nm in wavelength between 950 nm

2 IMADE, SHIBUSAWA, SASAO A Sampling Strategy in Mapping Soil Parameters Paddy Field 1700nm. The scanning time was set at 20ms with integration 20times. The depth measurements was 20 }2 cm. In Kyoto, reflectances were collected at interval 1m in longitudinal direction, 5m in lateral direction. While in Ishikawa, reflectances were collected at intervals 1m in longitudinal direction 10m in lateral direction. For calibrating spectrophotometer, samples were collected Kyoto Ishikawa, respectively, analyzed in laboratory. The samples were collected at same depth location scanning point. Five parameters were analyzed including moisture (MC), organic matter (SOM), nitrate nitrogen (NO3-N), ph electric conductivity (EC). In this study, NO3-N, ph EC were analyzed after samples were dried at 110 Ž 24 hours. These resulted in higher measured values NO3-N EC, lower value ph, compared with conventional method in which samples were air dried in room temperature one week bee NO3-N, ph EC were measured. From comparison experimental results showed that average value NO3-N, EC ph measured oven dried (dried at 110 Ž) samples were 2.2, times value conventional method, respectively. In calibration processes, reflectance that collected s were pretreated by applying moving average smoothing method, Kubelka-Munk (KM) transmation, multiple scatter correction (MSC), derivation bee development prediction models. Using best prediction models obtained, values parameters at each point interest were n estimated. The calibration results were presented in Table 1. To investigate an optimal sample in mapping, collected data, scanning spaces 1 mx5m, 2mX5m, 3mX5m, 4mX5m, 5mX5m, 10mX 10m, 20mX20m were selected Kyoto data, scanning spaces 1mX10m, 2mX10m, 4mX10 m, 6mX10m, 8mX10m, 10mX10m, 20mX20m, 30mX30m were selected Ishikawa data. These spacing resulted in sample s 860, 410, 280, 210, 180, 50, 15 Kyoto, 968, 476, 243, 168, 127, 104, 32, 15 Ishikawa, respectively. 2. Geostatistic analysis Optimal sample mapping was evaluated by Kriged stard error. Prior to calculation, inmation on spatial variation measured data must be known". Estimating semivariance measured data can provide this inmation. The semivariance is defined as The detail equation within block variance was described in Webster Burgess". The analyses in this study were permed using GS + Geostatistic Stware Version 5.1 Windows (Gamma Design Stware)1) V Results Discussion Table 1 presents statistics values parameters that collected in interval 1mx5m Kyoto, 1mx10m Ishikawa. The skewness values showed that population distribution EC Kyoto was not skewed, NO3-N was skewed toward larger value. The NO3-N EC Ishikawa also skewed toward larger value. The population distribution moisture, SOM ph both s were skewed toward smaller value. The probability distribution all parameters both s had tails smaller than normal distribution. The coefficient variation (CV) showed that both had lowest ph value, 5.48% Kyoto 3.02% Ishikawa. In Kyoto, highest variation was appeared EC, followed by NO3-N, moisture (MC) SOM, while in Ishikawa NO3-N had highest variation, followed by EC, MC SOM. From comparison data variation in lateral longitudinal direction showed that data variation in lateral direction tended to have higher variation than in longitudinal direction. This might be due to fact that spacing interval in lateral direction was wider than in longitudinal direction. The data variations SOM EC Kyoto, data variation MC NO3-N

3 Journal Japanese Society Agricultural Machinery Vol. 64, No.3 (2002) Table1 Summary calibration results real-time spectrophotometer Table2 Statistics parameters data collected at spacing interval 1mX5m Kyoto, 1mx10m Ishikawa Note: CVpop (%)=coefficient variation data population CViat (%)=coefficient variation in lateral direction CV,ong (%)=coefficient variation in longitudinal direction The same character behind number in same column mean not significantly different (a=5%)

4 IMADE, SHIBUSAWA, SASAO: A Sampling Strategy in Mapping Soil Parameters Paddy Field 89 Table3 Summary semivariance values parameters collected at spacing interval 1mx5m Kyoto, 1mX10m Ishikawa Table4 Summary average Kriged stard error in mapping parameters at different sample Ishikawa, however, were not significantly different (see Table 2) Table3 shows semivariance parameter data that calculated most intensive data spacing. All parameters data fitted with spherical model scored at high R2 values, except moisture data Ishikawa. In addition, ph both s SOM Ishikawa had no nugget effect. Based on spatial dependence classification reported by Cambardella (1994), spatial dependence parameters data showed that all parameters data Kyoto were moderately spatially dependence. It can be seen proportion value in which values were spread between The parameters Ishikawa, however, only EC had moderate spatial dependence. The or parameters, MC, SOM, NO3-N ph, had strong spatial dependence with proportion value more than It can be also seen that range spatial dependence Kyoto data ranged 27.00m SOM to 42.50m EC. But spatial dependence range Ishikawa data spread between 13.20m ph 61.20m EC. This indicated that structure spatial variation was observed within range. Table4 contains summary average Kriged stard error parameter mapping at different data, plots average kriged stard error against data are presented in Figure 1. The Kriged stard error was calculated as square root Kriged variance as explained in Equation 2. Prior to calculation, spatial variations each subset data were analyzed by calculating semivariance as define in Equation 1. The results showed that average Kriged stard error decreased with an increased in data. As can be seen Figure 1, all curves had same pattern, in which Kriged stard error firstly decreased sharply followed by flattened curves. The flattened part suggests that additional data in mapping will not much affecting quality or error produced map. For Kyoto

5 Journal Japanese Society Agricultural Machinery Vol. 64, No.3 (2002) data, Kriged stard error curves all parameters were flattened 180 data or (spacing interval 5mX5m). For Ishikawa data, Kriged stard error curves MC EC were flattened 168 data (interval spacing 6mX10m), while SOM NO3-N curves were flattened 243 data (a) Kyoto data (b) Ishikawa data Figure1 Plots average Kriged stard error against sample (spacing interval 4mX10m). The ph was flattened 104 data (10mX10m). The cumulative kriging errors as presented in Table 5 Figure 2 show, however, even though average stard error decreased with an increased data, cumulative Kriged stard errors were slightly increasing with addition data after minimum value was achieved. This might be affected by variation distribution data, which is affecting semivariogram parameters. The semivariogram parameters play an important role on results Kriged stard error. It can be seen Table5 that minimum cumulative MC, SOM, NO3-N, EC Kyoto appeared at data 180, or at spacing interval 5mX5m. The minimum cumulative Kriged error ph was achieved at data 240 with spacing interval 4mx5m. For mapping Ishikawa data, however, minimum cumulative Kriged errors were obvious at data 104 (10mX10m) ph, 168 (6mX10m) MC EC, at 243 (4mX10m) SOM NO3 -N. These results, in which entire minimum Kriged error appeared at data more than 100, suggest that to mapping parameters paddy using real-time spectrophotometer, should be scanned at data more than 100. In this study, since scanning scheme had different lateral interval, since all five parameters estimated at once, it can be suggested that if lateral scanning interval is 5m, scanning interval 4m in longitudinal is enough to give relatively good map. Also, if lateral scanning interval is 10m, scanning at longitudinal direction can be permed at maximum 4m interval. Examples map different data Table 5 Summary cumulative Kriged stard error in mapping parameters at different sample

6 IMADE, SHIBUSAWA, A Sampling are Figure presented 4 moisture in Ishikawa Figure. map 3 Kyoto Figure Kyoto SASAO: Strategy in Mapping 3a illustrates Soil Parameters Paddy data , Figure 3b with average stard 91 Field with average error Kriged error data , Figure 3c data 50 with average Kriged stard error 1.181, Figure 3d stard data 15 with Kriged error It can be seen Figure 3 that when too few data was used to generate map, contour line providing levels were not seen all. moisture As can be seen in Figure 3d, 9 levels provided map, only 4 contour lines are observed. Also, it was seen that moisture in norrn parts were overestimated, estimated. (a) Kyoto in sourn Figure 3 also shows parts were underthat more inten- sive data used in generating more contour lines were observed, rugged lines. many spot regions 3a). The moisture similar When too many data were used, were observed in map (Figure behavior data were used map as Ishikawa that different data in generating were used, smoor regions were eliminated. 2 Plots against cumulative sample Figure3 Kriged stard error Moisture (b) sample 180 (c) sample 50 (d) sample 15 maps Kyoto showed when regions were obless data contour line, spot When too few data were used to develop map, 7 providing levels moisture, only 5 levels were observed (Figure (a) sample 860 Kyoto. When too many map, contour lines were rugged, many spot served in map (Figure 4a).The Figure map, more different sample

7 92 Journal Figure4 Japanese Society Agricultural Machinery (a) sample 968 (b) sample 168 (c) sample 104 (d) sample 15 Moisture maps Ishikawa 4d). The Sampling two scheme paddy tometer in s had sampling was 180 (5mx5m N03 -N val) ph. 104 interval) s. interval) data sampling than (4mx5m Ishikawa 243 (4mx10m be term sampled. Acknowledgement with In using at addition, total 2) 3) Shinichi ir this Hirako pressional The Mr. advises, authors also Mr. T. Kaho experiment. real-time 4) number 4m : GS GeoStatistic Gamma Design Stware, Burgess, T.M., R. Webster. Environmental Sciences, Plainwell, Michigan, USA Optimal Sampling Strategies Mapping II. Risk Functions or Soil Types. in central Iowa s. Soil. Sci. Soc. Am. J. 58: p Chang, J., D.E. Clay, C.G. Carlson Precision Farming Protocols: properties D. Malo, S. A. Clay. Part 1. Grid Distance Soil Nutrient Impact ability Measurements. 5) Sampling Intervals. J. Soil Sci. 35: p Cambardella, C.A., T.B. Moorman, J.M. Novak, T.B. Parkin, D. L. Karlen, R.F. Turco, A.E. Konopka Field-scale variability to minimizing interval 1) was (6mx10m. Dr. Mr. C.K. Lee throughout References inter- direction 100, 168 in should assistance sample encouragement. to Mr. H. Sato, Kyoto SOM Ltd. (2002) parame- ph, NO3-N data MC, thank ir map, longitudinal more parameter a good spectropho- 210 SOM in EC, spectrophotometer, less EC, map get sampling interval) parameter among MC error, The (10mx10m) To varying ters real-time confirmed. were The different thank Omron suggestions, mapping with been s Conclusion authors Yamazaki, W Vol. 64, No.3 on Reproducibility Precision Agriculture. Spatial Vari1:p Made Anom S.W., S. Shibusawa, A. Sasao, S. Hirako Soil Parameters Maps in Faddy Field Using Real-Time Soil Spectrophotometer. J. Japanese Society Agri-

8 IMADE, SHIBUSAWA, SASAO: A Sampling Strategy in Mapping Soil Parameters Paddy Field cultural Machinery. 63(3): p ) McBratney, A.B., M.J. Pringle Estimating Average Proportional Variograms Soil Properties Their Potential Use in Precision Agriculture. Precision Agriculture. l : p ) Mueller, T.G., K.L. Wells, G.W. Thomas, RI. Barnhisel, N.J. Hartsock, S.A. Shearer, A. Kumar, CR. Dillon Soil Fertility Map Quality: Case Studies in Kentucky. Proceedings Fifth International Conference on Precision Agriculture. July 16-19, 2000, Bloomington, Minnesota, USA. (Compiled in CD-ROM) 8 ) Mulla D.J., AC. Sekely, M. Beatty Evaluation Remote Sensing Targeted Soil Sampling Variable Rate Application Lime. Proceedings Fifth International Conference on Precision Agriculture. July 16-19, 2000, Bloomington, Minnesota, USA. (Compiled in CD-ROM) 9 ) Shibusawa, S., Li M.Z., K. Sakai, A. Sasao, H. Sato, S. Hirako, A. Otomo Spectrophotometer Real-Time Underground Soil Sensing. An ASAE Meeting Presentation Paper No ) Shibusawa, S., H. Sato, S. Hirako, A. Otomo, A. Sasao A Revised Soil Spectrophotometer. Proceedings 2 ~ IFAC/CIGR International Workshop on BID-ROBOTICS II, eds. S. Shibusawa, M. Monta, H. Murase, Nov , Osaka, Japan 11) Webster, R., TM. Burgess Sampling Bulk Strategies Estimating Soil Properties in Small Regions. J. Soil Sci. 35 : p (Received : 11. October Question time limit : 31. July. 2002) [Referee's Comment] Regarding to proportion sampling maps that obtained by authors, is it also applicable even in upl? In addition, is it also applicable yield or parameters? Show us your opinion. [Authors' View] We think approach or recommendation this experiment can be applicable to upl. The optimum grid spacing or grid sampling can be obtained in any, but value will be changed by condition, such as texture,, distribution observed parameters, etc. For yield or parameters, our results might be possible to apply on it. From our discussion with Dr. Choung Keun Lee, mer doctor degree student at Kyoto Univefsity who did research on yield mapping in same with our experimental (experimental farm Kyoto University), a better yield map was obtained sampling interval 5 m x 5 m. However, that yield sampling interval was not based on calculation result, but visualization on map that different interval. We think to see applicability our results on yield or any or parameters, analysis on it are necessary.