During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water crisis, deforestation, and climate change threaten the global food security. An understanding of the variables that caused past changes in crop yields can help improve future crop prediction models. In this article, we present a comprehensive global analysis of the changes in the crop yields and how they relate to different large‐scale and regional climate variables, climate change variables and technology in a unified framework. A new multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Niño‐southern oscillation (ENSO), Palmer drought severity index (PDSI), geopotential height anomalies (GPH), historical carbon dioxide (CO2) concentration and country‐based time series of GDP per capita as an approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. Results indicate that these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out‐of‐sample verifications. While some countries were not generally affected by climatic factors, PDSI and GPH acted both positively and negatively in different regions for crop yields in many countries.

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The US Environmental Protection Agency (EPA)'s Response Protocol Toolbox provides a list of recommendations on actions that may be taken to minimize the potential threats to public health following a contamination threat. This protocol comprises three steps: (1) detection of contaminant presence, (2) source identification and (3) consequence management. This paper intends to explore consequence management under source uncertainty, applying Minimize Maximum Regret (MMR) and Minimize Total Regret (MTR) approaches. An ant colony optimization algorithm is coupled with the EPANET network solver for structuring the MMR and MTR models to present a robust method for consequence management by selecting the best combination of hydrants and valves for isolation and contamination flushing out of the system. The proposed models are applied to network number 3 of EPANET to present its effectiveness and capabilities in developing effective consequence management strategies. Key words | ant colony algorithm, consequence management, minimize maximum regret, water network contamination NOTATION G k à gb objective function value for the ant with the best performance within the past total iterations L set of options {l ij } α, β parameters which control the relative importance of the pheromone trail against heuristic value η ij heuristic value representing the desirability of state transition ij ρ coefficient of pheromone evaporation τ ij (t) total pheromone deposited on path ij at iteration t k à gb ant with the best performance within the past total iterations P ij (k, t) likelihood that ant k selects option l ij for decision point i at iteration t q random variable uniformly distributed over [0, 1] q 0 tunable parameter ∈[0, 1]