|DHALIWAL, DALJEET - University Of Illinois|
Submitted to: PLOS ONE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/23/2020
Publication Date: 2/7/2020
Citation: Dhaliwal, D., Williams II, M.M. 2020. Understanding variability in optimum plant density and recommendation domains for crowding stress tolerant processing sweet corn. PLoS One. 15(2):e0228809. https://doi.org/10.1371/journal.pone.0228809.
Interpretive Summary: Sweet corn growers and processors are poised to improve profitability of their crop by growing density tolerant hybrids at plant populations higher than current. How much higher? That is the central question of this research. Density-response data from growers' fields throughout the Upper Midwest were divided into different 'recommendation domains' - where each domain represented a group of fields, such as rainfed or irrigated. Several recommendation models were developed and tested, to determine which approach to adopting higher plant densities maximized profitability for growers and processors. For instance, should plant density recommendations be based on water supply (e.g. rainfed versus irrigation), yield level (e.g. low, medium, and high), state, etc.? We found the model built around 'production area' was most suitable. Conveniently, production area also captures how management decisions of other aspects of sweet corn production are implemented in the Upper Midwest. The impact of this work is providing direct transfer of technology that makes US sweet corn production more competitive for both growers and processors.
Technical Abstract: Recent research shows significant economic benefit if the processing sweet corn industry grew crowding stress tolerant (CST) hybrids at their optimum plant densities, which may exceed current plant densities by up to 14,500 plants ha-1. However, optimum plant density of individual fields varies over years and across the Upper Midwest (Illinois, Minnesota and Wisconsin), where processing sweet corn is concentrated. The objectives of this study were to: (1) determine the extent to which environmental and management practices affect optimum plant density and, (2) identify the most appropriate recommendation domain for making decisions on plant density. To capture spatial and temporal variability in optimum plant density, on-farm experiments were conducted at thirty fields across the states of Illinois, Minnesota and Wisconsin, from 2013 to 2017. Exploratory factor analysis of twelve environmental and management variables revealed two factors, one related to growing period and the other defining soil type, which explained the maximum variability observed across all the fields. These factors were then used to quantify the strength of associations with optimum plant density. Pearson’s partial correlation coefficients of ‘growing period’ and ‘soil type’ with optimum plant density were low ('1 = -0.14 and '2 = -0.09, respectively) and non-significant (P = 0.47 and 0.65, respectively). To address the second objective, six candidate recommendation domain models (RDM) were developed and tested. Linear mixed effects models describing crop response to plant density were fit to each level of each candidate RDM. The difference in profitability observed at the current plant density for a field and the optimum plant density under RDM level represented the additional processor profit ($ ha-1) from a field. The RDM built around ‘Production Area’ (RDMPA) appears most suitable, because plant density recommendations based on RDMPA maximized processor profit as well grower returns better than other RDMs. Compared to current plant density, processor profits and grower returns increased by $448 ha-1 and $82 ha-1, respectively at plant densities under RDMPA.