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.
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]
Diagnosing potential predictability of global crop yields in the near term is of utmost importance for ensuring food supply and preventing socioeconomic consequences. previous studies suggest that a substantial proportion of global wheat yield variability depends on local climate and larger-scale oceanatmospheric patterns. the science is however at its infancy to address whether synergistic variability and volatility (major departure from the normal) of multinational crop yields can be potentially predicted by larger-scale climate drivers. Here, using observed data on wheat yields for 85 producing countries and climate variability from 1961-2013, we diagnose that wheat yields vary synergistically across key producing nations and can also be concurrently volatile, as a function of shared larger-scale climate drivers. We use a statistical approach called robust Principal Component Analysis (rPCA), to decouple and quantify the leading modes (pc) of global wheat yield variability where the top four PCs explain nearly 33% of the total variance. Diagnostics of PC1 indicate previous year's local Air Temperature variability being the primary influence and the tropical Pacific Ocean being the most dominating larger-scale climate stimulus. Results also demonstrate that worldwide yield volatility has become more common in the current most decades, associating with warmer northern Pacific and Atlantic oceans, leading mostly to global supply shortages. As the world warms and extreme weather events become more common, this diagnostic analysis provides convincing evidence that concurrent variability and worldwide volatility of wheat yields can potentially be predicted, which has major socioeconomic and commercial importance at the global scale, underscoring the urgency of common options in managing climate risk. Wheat accounts for around 20% of the calories that humans consume and as such is the leading source of plant protein. It is well-known that wheat productivity is sensitive to both natural climate variability and extreme weather 1-10. As a result, extreme weather disasters such as heatwaves, droughts, floods, cold spells, and the co-occurrence of compound extremes (e.g. hot and dry spell events) have caused significant production losses 11-14. The relationships between climate, wheat production variability and stability, and socioeconomic outcomes has received growing attention recently 7,10,14-17. Separate lines of evidence indicate that weather extremes across the globe can occur concurrently, due to mutual larger-scale climate drivers 18-20 , and that such larger-scale drivers influence global and regional crop productivity 7,10,21-29. While agricultural influence of climate is well-established 1-17,21-29 , a detailed account of the characteristics of synergistic multinational variability and worldwide volatility of crop yields, whereby many countries undergo harmonizing influences of climate to thwart or facilitate wheat productivity, needs more attension 10. History indicates that such synchronous volatility-led whe...
We present the output data of Robust Principal Component Analysis (RPCA) applied to global crop yield variability of maize, rice, sorghum and soybean (MRSS) as presented in the publication “Climate drives variability and joint variability of global crop yields” (Najafi et al., 2019). Global maps of the correlation between all the principal components (PCs) acquired from the low rank matrix (L) of MRSS and Palmer Drought Severity Index (PDSI), air temperature anomalies (ATa) and sea surface temperature anomalies (SSTa) are provided in this article. We present co-varying countries, impacted cropland areas across global countries, and 10 global regions by climate and the association between PCs and multiple atmospheric and oceanic indices. Moreover, the joint dependency between PCs of MRSS yields are presented using two different approaches.
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