Crop physiological considerations for combining variable-density planting to optimize seed costs and weed suppression. Issue 6 (11th November 2022) - Ethridge, Sandra R.; Locke, Anna M.; Everman, Wesley J.; Jordan, David L.; Leon, Ramon G.
Abstract: High crop densities are valuable to increase weed suppression, but growers might be reluctant to implement this practice due to increased seed cost. Because it is also possible to lower planting densities in areas with no or low weed interference risk, the area allocated to each planting density must be optimized considering seed cost and productivity per plant. In this study, the growth and yield of maize ( Zea mays L.), cotton ( Gossypium hirsutum L.), and soybean [ Glycine max (L.) Merr.] were characterized in response to low planting densities and arrangements. The results were used to develop a bioeconomic model to optimize the area devoted to high- and low-density plantings to increase weed suppression without increasing seed cost. Physiological differences seen in each crop varied with the densities tested; however, maize was the only crop that had differences in yield (per area) between densities. When a model to optimize low and high planting densities was used, maize and cotton showed the most plasticity in yield per planted seed (g seed −1 ) and area of low density to compensate for high-density area unit. Maize grown at 75% planting density compared with the high-planting density (200%) increased yield (g seed −1 ) by 229%, return by 43%, and profit by 79% while decreasing the low-density area needed to compensate for high-density area. Cotton planted at 25% planting density compared with the 200% planting density increased yield (g seed −1 ) by 1, 099%, return by 46%, and profit by 62% while decreasing the low-density area needed to compensate for high-density area. In contrast, the high morphological plasticity of soybean did not translate into changes in area optimization, as soybean maintained return, profit, and a 1:1 ratio for area compensation. This optimization model could allow for the use of variable planting at large scales to increase weed suppression while minimizing costs to producers.