Selecting the High‐Yield Subpopulation for Diagnosing Nutrient Imbalance in Crops

• DOI: 10.2134/agronj2001.934802x
• Publication Date: 2010-07-28T15:54:00Z
• Authors: Lotfi Khiari; Léon-Étienne Parent; Nicolas Tremblay
• Keywords: 0106 biological sciences; 2. Zero hunger; 0401 agriculture, forestry, and fisheries; 04 agricultural and veterinary sciences; 01 natural sciences

Plant nutrient status is currently diagnosed using empirically derived norms from an arbitrarily defined high‐yield subpopulation above a quantitative yield target. Generic models can assist Compositional Nutrient Diagnosis (CND) in providing cutoff value between low‐ and subpopulations for small databases. Our objective was to compute the minimum target sweet corn ( Zea mays L.) corresponding critical CND imbalance index cumulative variance ratio function chi‐square distribution function. Population (40 observations) validation (20 data were selected at random survey database of 240 observations including commercial yields leaf concentrations. A filling R d ) computed as difference 100% sum proportions [ = 100 − N + P K …)]. The expressions row‐centered ratios N, , tissue specimens. Variance computations among two arranged decreasing order iterated across population data. proportion low‐yield inflection point cubic 67.5%, That exact probability corresponded theoretical (CND r 2 1.5 three components. validated independent samples squared indices. procedure applicable small‐size crop databases solving problems specific agroecosystems. calculation example presented.