Selecting the High-Yield Subpopulation for Diagnosing Nutrient Imbalance in Crops
- Lotfi Khiaria,
- Léon-Étienne Parent *a and
- Nicolas Tremblayb
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.Please view the pdf by using the Full Text (PDF) link under 'View' to the left.
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