Estimation of rainfall distribution and its relation to rice production in LaosData was interpolated using Kriging interpolation, taking into account spatial correlation quantified by a ‘semi-variogram’. The experimental semi-variogram ?(h) is defined by the following equation (1):  Υ(h) : Semi-variogram N(h): Number of pairs of observed points separated by distance h Zi, Zj : Observed rainfall value at points i, j The semi-variogram chart with Υ(h) versus Distance h shows that Υ(h) will increase with h over a certain distance, called ‘range’. Υ(h) will level out beyond the range and the spatial correlation will be lost over the range. The value of ?(h) when flat is called the ‘sill’ and the value of Υ(h) at h = 0 is the ‘nugget’. The nugget effectively prevents erratic swinging over very short distances. Accordingly, the semi-variogram expressing spatial correlation is represented by determination of range, sill, nugget, and function fitting the 2-d scattering plot with Υ(h) and h (Robertson 1987, Jayawardene et al. 2005). Those semi-variogram parameters, determined for each 72 months respectively, were applied as a weighting factor in Kriging interpolation to generate a rainfall distribution map. Using the above process, 72 maps with a grid resolution of 1 km were produced. 2.2 Estimation of rice production by rainfall factor Multi regression analysis was applied to reveal the relation between rainfall pattern and rice production. The harvested area and yield of lowland rice in the provinces were assigned as response variables. The monthly mean rainfall summarized by provinces and dummy variables corresponding provinces were assigned as predictor variables. In addition, the ratio of increased rice production over the previous year was added to the predictor variables to estimate yield, taking time trends into account. Non-significant variables were eliminated by t-value and p-value, and the two regressions to estimate harvested area and yield were finally obtained. The regression that performed accurate estimation was applied to map calculation in GIS to generate a spatial estimation map. 3. Results and discussion  By means of Kriging interpolation with the semi-variogram for each month (Table 1), monthly rainfall in 1-km grids over 72 months was estimated (Figure 2). The estimation error in observed rainfall value at the 72 points ranged from 1–30 mm, equivalent to less than 5% of the monthly average (Table 2). In addition, to verify the adaptability with respect to points which were not applicable to semi-variogram modeling, the observed value of 100 data items recorded in 2001 and 2002 by a data logger in 10 locations (Shown in Figure 1) was compared to the estimated value at these points. It tended to underestimate in low-rainfall parts and to overestimate in heavy rainfall parts; however, the correlation of all pairs was 0.7902 (Figure 3). These verification results suggest that rainfall was reasonably well estimated. Estimated rainfall was summarized by province. Annual mean rainfall by province was 1200 – 2300 mm; however, Huaphanh, Attapeu, and Champasak showed large yearly fluctuations (Table 3). Table 2. Average of estimation error in ovserved rainfall at the 72 points used for modeling (mm) .
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