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Meeting Global Food Needs: Realizing the Potential via Genetics × Environment × Management Interactions
Author(s) -
Hatfield Jerry L.,
Walthall Charles L.
Publication year - 2015
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj15.0076
Subject(s) - yield gap , yield (engineering) , productivity , variety (cybernetics) , agriculture , natural resource economics , business , environmental resource management , resource (disambiguation) , risk analysis (engineering) , microbiology and biotechnology , environmental planning , environmental science , computer science , ecology , biology , economics , economic growth , computer network , materials science , artificial intelligence , metallurgy
Global food needs are projected to double by 2050 to feed the 9 billion people and the challenge presented to agriculture is whether this is feasible. These goals will be faced with an increasing variability in climate and more extremes in temperature and precipitation in all parts of the world and a decreasing land resource base in extent and quality. There are many challenges to be faced; however, focusing on the interactions of genetics × environment × management (G × E × M) offers the potential to feed the 9 billion. Understanding and quantifying yield gaps offer a framework to assess the progress, and the challenge will be to determine the most effective and efficient way of closing the yield gap by using water and nutrients more efficiently. The more feasible approach of increasing potential will be to increase the actual yields rather than increasing potential yield. Actual yield increases and overall productivity can come from management systems focused on increasing land productivity because our ability to expand the available land resources are not a viable option. Development of methods of screening genotypes for a variety of responses to combinations of environmental and management scenarios offers the potential pathway to developing a robust structure for G × E × M. We can meet this challenge; however, the paradigm of how we currently conduct research will not be rapid enough and we need to develop the transdisciplinary teams to represent each component of the G × E × M interaction.