[Influence of interpolation method and sampling number on spatial prediction accuracy of soil Olsen-P].
Different from the large scale farm management in Europe and America, the scattered farmland management in China made the spatial variability of soil nutrients at county scale in this country more challenging. Taking soil Olsen-P in Wuhu County as an example, the influence of interpolation method and sampling number on the spatial prediction accuracy of soil nutrients was evaluated systematically. The results showed that local polynomial method, ordinary kriging, simple kriging, and disjunctive kriging had higher spatial prediction accuracy than the other interpolation methods. Considering of its simplicity, ordinary kriging was recommended to evaluate the spatial variability of soil Olsen-P within a county. The spatial prediction accuracy would increase with increasing soil sampling number. Taking the spatial prediction accuracy and soil sampling cost into consideration, the optimal sampling number should be from 500 to 1000 to evaluate the spatial variability of soil Olsen-P at county scale.
Sun
Yi-xiang
YX
College of Resource and Environmental Sciences, China Agricultural University, Beijing 100193, China. sunyixiang@126.com
Wu
Chuan-zhou
CZ
Zhu
Ke-bao
KB
Cui
Zhen-ling
ZL
Chen
Xin-ping
XP
Zhang
Fu-suo
FS
D002681:China; D018556:Crops, Agricultural / Q000254:growth & development*; D005308:Fertilizers; D040362:Geographic Information Systems; D010758:Phosphorus / Q000032:analysis*; D012494:Sampling Studies; D012987:Soil / Q000032:analysis*
D005308:Fertilizers; D012987:Soil; D010758:Phosphorus