Selecting the high-yield subpopulation for diagnosing nutrient imbalance in crops

Plant nutrient status is currently diagnosed using empirically derived nutr ient norms from an arbitrarily defined high-yield subpopulation above a qua ntitative yield target. Generic models can assist Compositional Nutrient Di agnosis (CND) in providing a yield cutoff value between low- and high-yield subpopulations for small databases, Our objective was to compute the minim um yield target for sweet corn (Zea mays L.) and the corresponding critical CND nutrient imbalance index using a cumulative variance ratio function an d the chi-square distribution function. Population (40 observations) and va lidation (20 observations) data were selected at random from a survey datab ase of 240 observations including commercial yields and leaf nutrient conce ntrations. 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 + ...)]. Th e CND nutrient expressions were the row-centered ratios of N, P, and Rd pro portions in tissue specimens. Variance ratio computations of CND nutrient e xpressions among two subpopulations arranged in a decreasing yield order we re iterated across population data, The proportion of low-yield subpopulati on computed at the inflection point of a cubic cumulative variance ratio Fu nction 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 in dependent samples and the sum of the squared CND nutrient indices. The proc edure is applicable to small-size crop nutrient databases for solving nutri ent imbalance problems in specific agroecosystems. A calculation example is presented.