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Soil Sampling Strategies for Precision Agriculture Research under Sahelian Conditions
Author(s) -
Groenigen J. W.,
Gandah M.,
Bouma J.
Publication year - 2000
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/sssaj2000.6451674x
Subject(s) - soil fertility , sowing , yield (engineering) , mathematics , spatial variability , precision agriculture , environmental science , soil test , crop yield , multivariate statistics , sampling (signal processing) , agriculture , agronomy , soil water , soil science , statistics , geography , computer science , archaeology , filter (signal processing) , computer vision , biology , materials science , metallurgy
The cost of soil samples to characterize field variability is a key problem in precision agriculture. This study was conducted to investigate whether yield maps can be used to optimize soil sampling for characterizing soil variables that determine yield variability. Using an inexpensive, low‐tech scoring technique, yield maps of pearl millet [ Pennisetum glaucum (L.) Br.] were produced for a zero‐input farm in Niger. The soil was classified as a typic Plintustalf. The Spatial Simulated Annealing (SSA) algorithm was used to optimize three sampling schemes. Scheme 1 optimized coverage over the whole area. Scheme 2 covered the whole yield range. Scheme 3 covered the low‐producing areas. Yield varied from 0 to 2500 kg ha −1 , measured per planting hill. Using correlation coefficients, Scheme 2 found significant correlations between five soil variables and yield. Scheme 1 found only one significant correlation and explained 37% of the variation in yield using multivariate regression of yield on soil variables. Scheme 2 explained 70% of the variation in yield. Differences between Scheme 3 and Scheme 1 proved to be significant for distance to shrubs, relief, soil pH, and cation‐exchange capacity (CEC). We concluded that shrubs are the main factor influencing millet yield by means of catching eroded materials and improving soil fertility. The possibilities of planting shrubs to improve soil fertility should be investigated. Variograms of relief and yield suggested that spatial correlation is largely confined to distances of 3 to 5 m. Since Scheme 2 was most effective in establishing soil–yield relationships, we concluded that yield maps can be used to optimize soil sampling.