Plant-based diagnostic techniques are used to determine the level of crop N nutrition but there is limited comparative research on the diff erent methods. Our objectives were to establish the relationship between chlorophyll meter (CM) readings and N nutrition index (NNI) during the corn (Zea mays L.) growing season, and to compare both methods as diagnostic tools for predicting grain yield response to N fertilization. Th e study was established at eight site-years using four to seven N fertilization rates. Th e CM readings from the youngest collared leaf were taken on fi ve to eight sampling dates in 2004, 2005, and 2006 along with NNI determinations. Generally, CM readings and NNI increased with increasing N rates. Chlorophyll meter readings and relative CM (RCM) readings were related to NNI, but the intercepts and/or slope of the response curves varied with site-year. Because they are site-specifi c, these relationships may not be reliable indicators of corn N status. Th e relationship between CM readings and relative grain yield (RY) at stage of development ≈V12 was also site-specifi c. Relative CM readings (RY = −0.64 + 1.65 RCM if RCM ≤ 0.98 and RY = 0.97 if RCM > 0.98; R 2 = 0.60) and NNI (RY = −0.34 + 1.47 NNI if NNI ≤ 0.88 and RY = 0.96 if NNI > 0.88; R 2 = 0.79) at stage of development ≈V12 were related to RY. Th ese two relationships were stable across site-years and could be used to detect and quantify N defi ciencies of corn.

Tissue analysis is commonly used in ecology and agronomy to portray plant nutrient signatures. Nutrient concentration data, or ionomes, belong to the compositional data class, i.e., multivariate data that are proportions of some whole, hence carrying important numerical properties. Statistics computed across raw or ordinary log-transformed nutrient data are intrinsically biased, hence possibly leading to wrong inferences. Our objective was to present a sound and robust approach based on a novel nutrient balance concept to classify plant ionomes. We analyzed leaf N, P, K, Ca, and Mg of two wild and six domesticated fruit species from Canada, Brazil, and New Zealand sampled during reproductive stages. Nutrient concentrations were (1) analyzed without transformation, (2) ordinary log-transformed as commonly but incorrectly applied in practice, (3) additive log-ratio (alr) transformed as surrogate to stoichiometric rules, and (4) converted to isometric log-ratios (ilr) arranged as sound nutrient balance variables. Raw concentration and ordinary log transformation both led to biased multivariate analysis due to redundancy between interacting nutrients. The alr- and ilr-transformed data provided unbiased discriminant analyses of plant ionomes, where wild and domesticated species formed distinct groups and the ionomes of species and cultivars were differentiated without numerical bias. The ilr nutrient balance concept is preferable to alr, because the ilr technique projects the most important interactions between nutrients into a convenient Euclidean space. This novel numerical approach allows rectifying historical biases and supervising phenotypic plasticity in plant nutrition studies.

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Plant‐based diagnostic tools of N deficiency can be based on the concept of critical N dilution curves describing whole‐plant critical N concentration (Nc; g kg−1 of dry matter [DM]) as a function of shoot biomass (W; Mg DM ha−1). This has been tested for several crops, including winter wheat (Triticum aestivum L.) but has not been tested for spring wheat. Our objectives were to determine a critical N dilution curve specific to spring wheat, to compare this curve with existing critical N dilution curves for winter wheat, and to assess the plausibility of using it to estimate the level of N nutrition. The study was conducted at six site‐years (2004–2006) in Québec, Canada, with four to eight N fertilization rates (0–200 kg N ha−1). Shoot biomass and N concentration were determined on five to eight sampling dates during the growing season, and grain yield was measured at harvest. A critical N dilution curve (Nc = 38.5 W−0.57) was determined for spring wheat and was different from those reported for winter wheat. The N nutrition index (NNI = Nobserved/Nc) calculated from this spring wheat critical N dilution curve was significantly related (R2 = 0.70; P < 0.001) to relative grain yield. This critical N dilution curve and the resulting NNI adequately identified situations of limiting and nonlimiting N nutrition and could be used to establish the N nutrition status.

A gronomy J our n al • Volume 10 0 , I s sue 2 • 2 0 0 8 ABSTRACT Plant-based diagnostic methods of N nutrition require the critical N concentration (N c ) to be defi ned, that is the minimum N concentration necessary to achieve maximum growth. A critical N curve (N c = 34.0W −0.37 with W being shoot biomass in Mg DM ha −1 ), based on whole plant N concentration, was determined for corn (Zea mays L.) in France. Our objectives were to validate this critical N curve in eastern Canada and to assess its plausibility to estimate the level of N nutrition in corn. Shoot biomass and N concentration were determined weekly during the growing season at three sites for 2 yr (2004 and 2005); four to seven N treatments were used at each site. Data points were divided into two groups representing either nonlimiting or limiting N conditions according to signifi cant diff erences in shoot biomass at each sampling date. All data points included in the limiting N group were under the critical N curve and most data points of the nonlimiting N group were on or above the critical N curve, hence confi rming the validity of the critical N curve determined in France. Th e nitrogen nutrition index (NNI), calculated as the measured N concentration divided by the predicted N c , ranged from 0.30 to 1.35. A signifi cant relationship between relative grain yield (RY) and NNI (RY = -0.11 + 1.17 NNI if NNI < 0.93 and RY = 0.98 if NNI > 0.93; R 2 = 0.89) was determined. Th e critical N curve from France is valid in eastern Canada and the NNI calculated from that curve is a reliable indicator of the level of N stress during the growing season of corn.

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limitations of, near infrared reflectance spectroscopy applications in soil analysis: A review. Can. J. Soil Sci. 89: 531Á541. Near infrared reflectance spectroscopy (NIRS) is a cost-and time-effective and environmentally friendly technique that could be an alternative to conventional soil analysis methods. In this review, we focussed on factors that hamper the potential application of NIRS in soil analysis. The reported studies differed in many aspects, including sample preparation, reference methods, spectrum acquisition and pre-treatments, and regression methods. The most significant opportunities provided by NIRS in soil analysis include its potential use in situ, the determination of various biological, chemical, and physical properties using a single spectrum per sample, and an estimated reduction of analytical cost of at least 50%. Contradictory results among studies on NIRS utilisation in soil analysis are partly related to variations in sample preparation and reference methods. The following calibration statistics appear to be most appropriate for comparing NIRS performance across soil attributes: (i) coefficient of determination (r 2 ), (ii) ratio of performance deviation (RPD), (iii) coefficient of regression (b), and (iv) ratio of the standard error of prediction (SEP) to the standard error of the reference method (SER), i.e., the ratio of standard errors (RSE). Further investigations on issues such as (i) RSE guidelines, (ii) correlation between NIRS spectrophotometers, (iii) correlation of different reference methods for a given attribute to soil spectra, (iv) identification of key factors affecting the accuracy of NIRS predictions, and (v) efficient use of spectral libraries are required to enhance the acceptability of NIRS as a soil analysis technique and to make it more user-friendly. Standardized guidelines are proposed for the assessment of the accuracy of NIRS predictions of soil attributes.