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Selecting the High‐Yield Subpopulation for Diagnosing Nutrient Imbalance in Crops
Author(s) -
Khiari Lotfi,
Parent Léon-Étienne,
Tremblay Nicolas
Publication year - 2001
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2001.934802x
Subject(s) - nutrient , mathematics , statistics , yield (engineering) , population , crop yield , crop , agronomy , biology , ecology , physics , demography , thermodynamics , sociology
Plant nutrient status is currently diagnosed using empirically derived nutrient norms from an arbitrarily defined high‐yield subpopulation above a quantitative yield target. Generic models can assist Compositional Nutrient Diagnosis (CND) in providing a yield cutoff value between low‐ and high‐yield subpopulations for small databases. Our objective was to compute the minimum yield target for sweet corn ( Zea mays L.) and the corresponding critical CND nutrient imbalance index using a cumulative variance ratio function and the chi‐square distribution function. Population (40 observations) and validation (20 observations) data were selected at random from a survey database of 240 observations including commercial yields and leaf nutrient concentrations. A filling value ( R d ) was computed as the difference between 100% and the sum of d nutrient proportions [ R d = 100 − ( N + P + K + …)]. The CND nutrient expressions were the row‐centered ratios of N, P , and R d proportions in tissue specimens. Variance ratio computations of CND nutrient expressions among two subpopulations arranged in a decreasing yield order were iterated across population data. The proportion of low‐yield subpopulation computed at the inflection point of a cubic cumulative variance ratio function was 67.5%, the minimum proportion of low‐yield specimens. That exact probability corresponded to a theoretical chi‐square value (CND r 2 ) of 1.5 for three components. The critical CND r 2 value was validated using independent samples and the sum of the squared CND nutrient indices. The procedure is applicable to small‐size crop nutrient databases for solving nutrient imbalance problems in specific agroecosystems. A calculation example is presented.