Multivariate analysis of long-term climate data in connection with yield, earliness and the problem of global warming

Abstract

Climate change is the key challenge to agriculture in the XXI century. Future agricultural techniques in the Russian Federation should involve the optimization of land utilization. This optimization should apply algorithms for smart farming and take into consideration possible climate variations. Due to timely risk assessment, this approach would increase profitability and production sustainability of agricultural products without extra expenditures. Also, we should ground farming optimization not on available empirical data encompassing limited time intervals (month, year) or human personal evaluations but on the integral analysis of long-term information bodies using artificial intelligence. This article presents the results of a multivariate analysis of meteorological extremes which caused crop failures in Eastern and Western Europe in last 2600 years according to chronicle data and paleoreconstructions as well as reconstructions of heliophysical data for the last 9000 years. This information leads us to the conclusion that the current global warming will last for some time. However, subsequent climate changes may go in any direction. And cooling is more likely than warming; thus, we should be prepared to any scenario. Plant breeding can play a key role in solving food security problems connected with climate changes. Possible measures to adapt plant industry to the ongoing and expected climate changes are discussed. It is concluded that future breeding should be based on the use of highly adapted crops that have already been produced in pre-breeding programs, ready to meet future challenges caused by potential climate change.

Keywords

• climate
• global warming
• models
• next generation breeding
• adaptability
• earliness