Crop spatial uniformity, yield and weed suppression
Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
Most crop sowing methods result in a highly clumped two-dimensional spatial pattern of individual crop plants. Ecological theory predicts that increasing the spatial uniformity of crop plants should result in increased suppression of weeds and higher yields. We reviewed experiments in which crop spatial pattern was an independent variable and effects on yield and/or weeds were measured. We included (a) all studies we could identify in which the uniformity of spacing within the crop rows was a variable, and (b) all studies from 1996 to 2016 in which row distance was the only spatial variable investigated. In most experiments, increased crop spatial uniformity resulted in increased weed suppression. In a large majority of cases, yield was higher when seeds were more evenly spaced within the rows. Effects of reducing row distance without improving the distribution of seeds within the rows were variable, usually resulting in increases in yield, but in some cases the effects were small or absent. Increased crop spatial uniformity through reduced row spacing together with improved uniformity within the rows increased weed suppression and increased yield for a wide range of crops and sowing densities, both in the presence and the absence of weeds. Positive effects of increased spatial uniformity were stronger under mesic than dry conditions, and for crops or varieties with relatively determinate growth form. Increased crop spatial uniformity can improve plant production in the future by increasing weed suppression, crop yields and agricultural sustainability.
Original language | English |
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Title of host publication | Advances in Agronomy |
Volume | 161 |
Publisher | Academic Press |
Publication date | 2020 |
Pages | 117-178 |
DOIs | |
Publication status | Published - 2020 |
Series | Advances in Agronomy |
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ISSN | 0065-2113 |
- Crop yield, Crop-weed competition, Planting pattern, Precision sowing, Spatial pattern, Weed biomass
Research areas
ID: 239667635