Comparative Analysis of Bioenergy Crop Yields Under Climate Variability

Thesis Abstract

The study addresses the reliability of bioenergy crop yields under climate variability, recognizing that fluctuations in temperature, precipitation, and extreme events influence biomass productivity and supply security for renewable energy pipelines. The aim is to quantify comparative yield responses of maize, switchgrass, and miscanthus across multiple agricultural landscapes and to identify climate-driven drivers of yield divergence to inform adaptive management and policy. Specific objectives are (i) to quantify inter-species yield differences under decadal climatic variability (2005–2024) using replicated field trial and farm-plot data; (ii) to evaluate the influence of temperature and precipitation anomalies, drought indices, and soil properties on yield performance; (iii) to compare stability and risk metrics (coefficient of variation, yield-at-risk) among the three bioenergy crops; (iv) to develop predictive models integrating climate variables with agronomic inputs to guide cultivar selection and management practices; and (v) to translate findings into actionable guidelines for farmers and policymakers to sustain bioenergy feedstock supply under climate change. The methodology adopts a comparative cross-sectional design combining multi-site field experiments with observational farm data. The population comprises bioenergy crop production systems across five representative agro-ecological zones within a temperate-to-subtropical transition region. A stratified random sampling scheme yields 60 farm-plot observations per species (maize, switchgrass, miscanthus), totaling 180 observational units, complemented by existing experimental trial data (n=30 plots per species) to strengthen temporal coverage. Data collection instruments include standardized yield measurement protocols, climate data from on-site weather stations and gridded reanalysis products, soil property analyses (pH, organic matter, texture, field capacity), and management records (fertilization, irrigation, planting density). For validation, remote sensing-derived vegetation indices (NDVI, EVI) with ground-truth calibration are employed to monitor biomass accumulation. Analytical approaches integrate descriptive statistics, Pearson/Spearman correlations, and advanced econometric modeling. A fixed-effects panel regression framework assesses yield responses to climate variables (growing-season mean temperature, total precipitation, precipitation days, drought indices such as SPEI) while controlling for soil and management covariates. Nonlinear effects and threshold behavior are explored via generalized additive models (GAMs) to capture potential saturation or tipping points in water stress responses. Stability analysis utilizes Monte Carlo simulations to compute yield-at-risk (YAR) and the coefficient of variation (CV) across crops. Interaction terms between climate variables and soil properties test context-dependent sensitivities. A structural equation model (SEM) examines direct and indirect pathways from climate drivers to yield through phenology and biomass partitioning. Model selection employs information criteria (AIC, BIC), cross-validation, and out-of-sample prediction accuracy. Theoretical grounding draws on the Stress-Response Theory for plant performance under abiotic stress and the Resource-Based View of agronomic adaptability, with explicit consideration of climate resilience and risk diversification. The study also integrates a risk-hedging perspective to compare, on a probabilistic basis, feedstock reliability for energy supply. Expected findings indicate that miscanthus and switchgrass exhibit greater yield stability under moderate drought and warmer temperatures relative to maize, with maize showing pronounced yield volatility during extreme heat and water shortage episodes. The results are anticipated to reveal crop-specific yield sensitivities to precipitation variability and to identify critical thresholds in growing-season moisture (e.g., SPEI below -1.0) beyond which significant yield penalties occur. Soil organic matter and higher infiltration capacity are expected to mitigate negative climate impacts, supporting partial resilience in perennial grasses. Predictive models are projected to achieve acceptable out-of-sample R-squared values (0.55–0.70) with reasonable RMSEs, enabling scenario-based guidance for cultivar selection and management adjustments under projected climate trajectories. The study contributes to knowledge by delivering cross-crop, climate-responsive yield characterizations and a robust modeling framework adaptable to regional planning and farm-level decision-making. It offers practical guidelines on crop choice and management to maintain bioenergy supply chains amidst climate uncertainty and informs policy on feedstock risk assessment, incentives for drought-tolerant cultivars, and investments in soil and water conservation. The main conclusion is that perennial miscanthus and switchgrass generally outperform maize in yield stability under climate variability, given appropriate soil and management practices; recommendations emphasize diversified cropping and targeted agronomic interventions to optimize resilience and feedstock reliability.