Impact of variable-rate nitrogen on wheat yield and grain quality under drought stress

Thesis Abstract

Drought stress significantly constrains wheat productivity and grain quality, with nitrogen management playing a pivotal role in mitigating yield penalties while influencing protein content, gluten strength, and milling performance. The study aims to evaluate the effects of variable-rate nitrogen (VRN) application on wheat yield and grain quality under simulated drought conditions and to identify agronomic and physiological mechanisms driving observed responses. Specific objectives are to (i) quantify yield components and grain quality attributes under VRN regimes across varying drought intensities, (ii) determine optimal VRN patterns that maximize grain yield along with key quality traits (protein, gluten index, test weight), (iii) assess above- and below-ground physiological responses (nitrogen uptake efficiency, chlorophyll fluorescence, stomatal conductance) and (iv) develop predictive models linking VRN, drought intensity, and grain quality outcomes for farmers’ decision support. The methodological framework employs a two-factor field experiment conducted over two growing seasons at two semi-arid wheat-growing sites with contrasting soil textures. The population comprises commercial bread wheat cultivars ‘Nora’ and ‘Atlas’, selected for their differential drought tolerance. A split-plot design with three replicates is used, where main plots receive two irrigation regimes (well-watered and induced moderate drought) and subplots implement four VRN strategies uniform rate, site-specific adjustments based on leaf chlorophyll meter readings, nodal-point zoning guided by soil electrical conductivity, and a control with conventional uniform fertilization. Nitrogen rates range from 120 to 240 kg N ha?1, adjusted through VT fertilizer injections to create gradients of N availability. Data collection instruments include yield and yield components (grain weight, thousand-kernel weight, ears per square meter), grain quality analyses (total protein by Kjeldahl, glutensett and gluten index via NIR and glutenin assessment, Zeleny sedimentation value, milling performance), soil N pools (inorganic N, mineralizable N), phenological records, canopy measurements (SPAD chlorophyll, normalized difference vegetation index), and physiological indicators (gas exchange, chlorophyll fluorescence). Additionally, a subset of plants undergo stable isotope tracing (15N) to quantify N-use efficiency under each VRN-drought combination. Data analysis employs mixed-effects ANOVA to test main effects and interactions of VRN, drought, cultivar, and site, with year as a random factor. Regression and multivariate analyses (principal components analysis, partial least squares regression) identify key predictors of grain protein content and test weight. Structural equation modeling explores causal pathways from VRN to plant physiological status and ultimately to grain quality outcomes. Regression diagnostics, cross-validation, and model comparisons using AIC/BIC ensure robust predictive performance. Economic analysis estimates partial factor productivity and marginal returns of VRN strategies under drought, informing cost-benefit considerations. Key expected findings include (i) VRN strategies that dynamically adjust N supply to plant demand under drought will sustain higher grain yield relative to uniform application, particularly for drought-tolerant cultivars; (ii) site-specific VRN approaches will improve grain protein concentration and gluten-related attributes without compromising yield in moderately stressed conditions; (iii) physiological indicators such as enhanced N-use efficiency and moderated stomatal conductance will mediate yield stabilization and quality preservation; (iv) predictive models will quantify threshold N rates and timing that optimize both yield and quality across drought severities. This study contributes to knowledge by integrating VRN technologies with drought physiology to optimize the dual goals of yield and grain quality in wheat, offering a mechanistic understanding of nitrogen-use dynamics under water limitation. It advances precision agronomy by providing empirically derived guidelines for VRN implementation tailored to drought scenarios, cultivar performance, and soil contexts. The main conclusion is that responsive VRN management, informed by real-time plant and soil diagnostics, can sustain wheat yield and grain quality under drought while reducing nitrogen losses. Recommendations include adopting soil- and sensor-driven VRN protocols, integrating chlorophyll-based and soil-conductivity indices into fertilizer decision-support tools, and validating the approach across wider agro-ecological zones and long-term rotations to enhance resilience to climate variability.