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Using Nonlinear Geostatistical Models in Estimating the Impact of Salinity on Crop Yield Variability
Author(s) -
Eldeiry Ahmed A.,
Garcia Luis A.
Publication year - 2013
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2012.0350
Subject(s) - yield (engineering) , salinity , geostatistics , environmental science , crop , soil science , hydrology (agriculture) , agronomy , geology , mathematics , statistics , spatial variability , oceanography , biology , materials science , geotechnical engineering , metallurgy
Three nonlinear geostatistical models—disjunctive kriging (DK), indicator kriging (IK), and probability kriging (PK)—were used to develop conditional probability (CP) maps based on soil salinity thresholds for two crops, alfalfa ( Medicago sativa L.) and corn ( Zea mays L.). The CP maps divide alfalfa and corn fields into zones with different probabilities to reach a specific yield potential (YP) at a specific soil salinity threshold. The objectives of this study were to: (i) compare the performance of the DK, IK, and PK models in developing CP maps; (ii) compare actual alfalfa and corn yield samples with the YP estimated by the three models; and (iii) provide guidance for precision management of agriculture by considering the output of the models used in this study. Yield data were collected at alfalfa and corn fields to compare the actual data with those estimated by the models. The results of this study show that the CP maps developed using the three geostatistical models are efficient in assessing the impact of soil salinity on the spatial variability of alfalfa and corn yield. The comparison of the actual yield data with the estimated CP maps from the three models showed good agreement. The IK and PK models generated very similar estimates for each of the zones; however, the zones generated by both of these models are slightly different from the zones generated using the DK model. The information from this study can be used for precision management of agricultural resources.