Wheat (Triticum aestivum L.) cultivar mixtures may stabilize yield across environments, control air‐borne diseases, and manage pest populations in both conventional and organically managed systems. The objective of this study was to evaluate agronomic and end‐use quality characteristics of wheat cultivar mixtures. Five Canada Western Red Spring wheat cultivars (‘Go Early’, ‘Carberry’, ‘Glenn’, ‘CDC Titanium’, and ‘Lillian’) differing in agronomic and quality traits were selected to compose 20 possible two‐way and three‐way combinations. Field experiments were conducted in four conventional and two organic environments in Alberta and Saskatchewan, Canada in 2016 and 2017. Wheat cultivar mixtures out‐yielded their mid‐component averages in both conventional and organic environments. Averaged across locations, two mixtures, Glenn–Lillian and Go Early–Glenn–Lillian, significantly out‐yielded their mid‐components. The overall yield increase ranged from 3.3 to 14.1%, with a mean of 0.18 Mg ha–1 over different environments. Grain yield was negatively correlated with protein content (r = –.53) and falling number was negatively correlated with sedimentation volume (r = –.66) in conventional systems. Protein content was positively correlated with falling number in both conventional (r = .60) and organic (r = .40) systems. Days to maturity was positively correlated with yield (r = .40) and sedimentation volume (r = .40), but negatively correlated with falling number (r = –.80) in organic system. Sole cultivars were more stable under conventional, and mixtures were more stable under organic management. Our results suggest that wheat cultivar mixtures may provide yield advantage under abiotic stresses in conventional, whereas superior yield and grain quality in organic management.

No abstract

This paper proposes the models to solve the optimal generator dispatching problem in an islanded Micro grid with different uncertainties in the constraint and in the objective coefficient. The optimal problem with interval in the power balance constraint is considered as a linear parametric optimization problem, focusing on optimal solutions based on the lower and upper ends of this interval. When the coefficients of cost per power unit caused by load shedding are imprecise and expressed as intervals, the proposed model will be based on the two ends of interval and the problem is converted to a two-objective problem. With uncertainties in both constraint and objective coefficient, the problem will be treated as a fourobjective one, considering the lower and upper ends of all intervals. All models are expressed in the linear forms and the linearization is carried out by Max-Affine method. To solve this multi-objective problem, the Bellman-Zadeh approach and Particle Swarm Optimization (PSO) algorithm are applied. The rationality of the proposed models is confirmed in the case study with one low voltage Micro Grid.