Managing water scarcity in European and Chinese cropping systems


a Independent Researcher, Variability, Ashalim 85512, Israel
b Department of Applied Mathematics, Delft University of Technology, Mourik Broekmanweg 6, 2628 XE Delft, the Netherlands
c TerraVision Lab, Midreshet Ben-Gurion 8499000, Israel
d Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, Gilat Research Center, Mobile post Negev 2, 85280, Israel
e Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, P.O. Box 15159, Rishon LeZion 7505101, Israel
f The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, The Robert H. Smith Faculty of Agriculture, Food & Environment, Rehovot 76100, Israel
g Department of Soil and Water Sciences, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 7610001, Israel
h Department of Precision Agriculture, MIGAL Galilee Research Institute, Kiryat Shmona 11016, Israel
i Department of Agriculture and Oenology, Eastern R&D Center, Israel
j Department of Chemical Engineering, Ariel University, Ariel 40700, Israel

Computers and Electronics in Agriculture


Collection of accurate and representative data from agricultural fields is required for efficient crop management. Since growers have limited available resources, there is a need for advanced methods to select representative points within a field in order to best satisfy sampling or sensing objectives. The main purpose of this work was to develop a data-driven method for selecting locations across an agricultural field given observations of some covariates at every point in the field. These chosen locations should be representative of the distribution of the covariates in the entire population and represent the spatial variability in the field. They can then be used to sample an unknown target feature whose sampling is expensive and cannot be realistically done at the population scale.

An algorithm for determining these optimal sampling locations, namely the multifunctional matching (MFM) criterion, was based on matching of moments (functionals) between sample and population. The selected functionals in this study were standard deviation, mean, and Kendall’s tau. An additional algorithm defined the minimal number of observations that could represent the population according to a desired level of accuracy. The MFM was applied to datasets from two agricultural plots: a vineyard and a peach orchard. The data from the plots included measured values of slope, topographic wetness index, normalized difference vegetation index, and apparent soil electrical conductivity. The MFM algorithm selected the number of sampling points according to a representation accuracy of 90% and determined the optimal location of these points. The algorithm was validated against values of vine or tree water status measured as crop water stress index (CWSI). Algorithm performance was then compared to two other sampling methods: the conditioned Latin hypercube sampling (cLHS) model and a uniform random sample with spatial constraints. Comparison among sampling methods was based on measures of similarity between the target variable population distribution and the distribution of the selected sample.

MFM represented CWSI distribution better than the cLHS and the uniform random sampling, and the selected locations showed smaller deviations from the mean and standard deviation of the entire population. The MFM functioned better in the vineyard, where spatial variability was larger than in the orchard. In both plots, the spatial pattern of the selected samples captured the spatial variability of CWSI. MFM can be adjusted and applied using other moments/functionals and may be adopted by other disciplines, particularly in cases where small sample sizes are desired. View Full-Text

Keywords: Partially-observed data, Representative sampling given covariates, Two-phase study, Agricultural sampling, Spatial autocorrelation


You must be logged in to post a comment.