Benefitting productivity and the environment: Current and future maize cropping systems improve yield while reducing nitrate load

Author:

Dohleman Frank G.1,Barten Ty J.2,Helland Nicholas2,Dahal Subash2,Arrizia Juan Lopez2,Gehlhar Sarah2,Foresman Charles2,Mack David2,Gillespie Kelly2,Archontoulis Sotirios3,Castellano Michael J.3ORCID

Affiliation:

1. Climate, Agriculture and Partnership Solutions Consulting LLC Hawthorn Woods Illinois USA

2. Bayer Crop Science Chesterfield Missouri USA

3. Department of Agronomy Iowa State University Ames Iowa USA

Abstract

AbstractIncreases in cereal crop yield per area have increased global food security. “Era” studies compare historical and modern crop varieties in controlled experimental settings and are routinely used to understand how advances in crop genetics and management affect crop yield. However, to date, no era study has explored how advances in maize (Zea mays L.) genetics and management (i.e., cropping systems) have affected environmental outcomes. Here, we developed a cropping systems era study in Iowa, USA, to examine how yield and nitrate losses have changed from “Old” systems common in the 1990s to “Current” systems common in the 2010s, and to “Future” systems projected to be common in the 2030s. We tested the following hypothesis: If maize yield and nitrogen use efficiency have improved over previous decades, Current and Future maize systems will have benefits to water quality compared to Old systems. We show that not only have maize yield and nitrogen use efficiency (kg grain kg−1 N), on average, improved over time but also yield‐scaled nitrate load + soil nitrate was reduced by 74% and 91% from Old to Current and Future systems, respectively. Continuing these trajectories of improvement will be critical to meet the needs of a growing and more affluent population while reducing deleterious effects of agricultural systems on ecosystem services.

Funder

Bayer

Publisher

Wiley

Reference40 articles.

1. Short‐stature maize reduced wind damage during the 2020 midwestern derecho, improving yields and greenhouse gas outcomes

2. Brien C.(2016).asremlPlus: Augments the use of ASReml‐R in fitting mixed models. R package version.

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