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Integrating genetic gain and gap analysis to predict improvements in crop productivity
Author(s) -
Cooper Mark,
Tang Tom,
Gho Carla,
Hart Tim,
Hammer Graeme,
Messina Carlos
Publication year - 2020
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.1002/csc2.20109
Subject(s) - context (archaeology) , crop yield , predictability , productivity , crop simulation model , agricultural engineering , agriculture , yield (engineering) , biology , computer science , agronomy , ecology , mathematics , engineering , statistics , economics , paleontology , materials science , macroeconomics , metallurgy
A Crop Growth Model (CGM) is used to demonstrate a biophysical framework for predicting grain yield outcomes for Genotype by Environment by Management (G×E×M) scenarios. This required development of a CGM to encode contributions of genetic and environmental determinants of biophysical processes that influence key resource (radiation, water, nutrients) use and yield‐productivity within the context of the target agricultural system. Prediction of water‐driven yield‐productivity of maize for a wide range of G×E×M scenarios in the U.S. corn‐belt is used as a case study to demonstrate applications of the framework. Three experimental evaluations are conducted to test predictions of G×E×M yield expectations derived from the framework: (1) A maize hybrid genetic gain study, (2) A maize yield potential study, and (3) A maize drought study. Examples of convergence between key G×E×M predictions from the CGM and the results of the empirical studies are demonstrated. Potential applications of the prediction framework for design of integrated crop improvement strategies are discussed. The prediction framework opens new opportunities for rapid design and testing of novel crop improvement strategies based on an integrated understanding of G×E×M interactions. Importantly the CGM ensures that the yield predictions for the G×E×M scenarios are grounded in the biophysical properties and limits of predictability for the crop system. The identification and delivery of novel pathways to improved crop productivity can be accelerated through use of the proposed framework to design crop improvement strategies that integrate genetic gains from breeding and crop management strategies that reduce yield gaps.