Crop physiological considerations for combining variable density planting to optimize seed costs and weed suppression

Authors: ETHRIDGE, SANDRA - North Carolina State University; Locke, Anna; EVERMAN, WESLEY - North Carolina State University; JORDAN, DAVID - North Carolina State University; LEON, RAMON - North Carolina State University

Submitted to: Weed Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/2/2022
Publication Date: 11/11/2022
Citation: Ethridge, S., Locke, A.M., Everman, W., Jordan, D., Leon, R. 2022. Crop physiological considerations for combining variable density planting to optimize seed costs and weed suppression. Weed Science. https://doi.org/10.1017/wsc.2022.62.
DOI: https://doi.org/10.1017/wsc.2022.62

Interpretive Summary: With advances in farm equipment technology, farmers can use variable planting density and planting arrangements as tools to suppress weeds. Higher planting density can help suppress weeds in parts of a field that are known to have a weed patch. However, this higher planting density would come with higher seed costs for the farmer. In order to balance out these seed costs, farmers could plant at lower density in areas of a field that have not historically been weedy. To evaluate if yield can be maintained at a lower planting density, an experiment was conducted with lower-than-normal planting densities and different planting patterns in corn, cotton, and soybean. These field data were used to calculate a grower’s potential profit in terms of yield per seed planted at different densities. These values were then used to determine how much area a grower would need to plant at low density to compensate for an area planted at higher density, for the purpose of weed control. The results showed that for maize and cotton, the low-density planting area required to compensate for an area of high-density planting depended on how low the density way, ranging from 25% to 75% of normal planting density. For soybean, however, the low-density area required to compensate was the same across all of the lower planting densities tested.

Technical Abstract: An important component of integrated weed management is the use of high crop densities to increase weed suppression, but growers might be reluctant to implement this practice due to increased seed cost. It is also possible to lower planting densities in areas with no or low weed interference risk. Based on these premises, planting densities need to be optimized for balance between weedy portions of the field and weed free portions. Also, physiological and yield responses of crops to changes in density must be understood. 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 using a model to optimize low- and high-planting densities, 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 to 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 to the 200% planting density increased yield (g seed-1) by 1099%, return by 46%, and profit by 62% while decreasing the low-density area needed to compensate for high-density area. Alternatively, the high morphological plasticity of soybeans did not translate into changes in area optimization, as soybeans maintained return, profit, and a 1:1 ratio for area compensation. The application of this optimization model could allow for the use of an integrated weed management strategy at large scales to increase weed suppression while minimizing costs to producers.