Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology. Issue 3 (8th March 2018)

• Record Type: Journal Article
• Authors: Najafi, Ehsan; Devineni, Naresh; Khanbilvardi, Reza M.; Kogan, Felix
• Is Part Of: Earth's future. Volume 6:Issue 3(2018)
• Page Start: 410
• Page End: 427
• Publication Date: 2018-03-08
• Subjects: Crop Yields -- Multilevel Modeling -- Food Security; Environmental sciences -- Periodicals; Environmental sciences; Periodicals; 550
• DOI: 10.1002/2017EF000690
• Languages: English
• ISSNs: 2328-4277

Abstract

Abstract: 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. Key Points: A new Bayesian multilevel model for understanding and predicting crop yield changes over time Capturing yield trends due to climate variability using large‐scale and regional climate factors Exploring the impact of technology advancement on crop yield fluctuations using per capita gross domestic production