Department of Crop Sciences, University of Illinois, Urbana, Illinois, United States of America.
Global Change and Photosynthesis Research Unit, USDA-ARS, Urbana, Illinois, United States of America.
PLoS One. 2020 Feb 7;15(2):e0228809. doi: 10.1371/journal.pone.0228809. eCollection 2020.
Recent research shows significant economic benefit if the processing sweet corn [Zea mays L. var. rugosa (or saccharata)] 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 (ρ1 = -0.14 and ρ2 = -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 profits as well grower returns better than other RDMs. Compared to current plant density, processor profits and grower returns increased by $448 ha-1 and $82 ha-1, respectively at plant densities under RDMPA.
最近的研究表明，如果加工甜玉米[Zea mays L. var. rugosa（或saccharata）]产业在最佳种植密度下种植耐拥挤胁迫（CST）杂交种，将带来显著的经济效益，最佳种植密度可能比当前种植密度增加多达 14500 株/公顷。然而，加工甜玉米集中的美国中西部上地区（伊利诺伊州、明尼苏达州和威斯康星州），各个田地的最佳种植密度会随着年份而变化。本研究的目的是：（1）确定环境和管理措施在多大程度上影响最佳种植密度，以及（2）确定用于做出种植密度决策的最合适的推荐域。为了捕捉最佳种植密度的空间和时间变化，本研究于 2013 年至 2017 年在伊利诺伊州、明尼苏达州和威斯康星州的 30 个田地进行了田间试验。对 12 个环境和管理变量的探索性因子分析揭示了两个因子，一个与生长周期有关，另一个定义土壤类型，这两个因子解释了所有田地观察到的最大变异性。然后，这些因子被用于量化与最佳种植密度的关联强度。“生长周期”和“土壤类型”与最佳种植密度的皮尔逊部分相关系数较低（ρ1=-0.14 和 ρ2=-0.09，分别）且不显著（P=0.47 和 0.65，分别）。为了解决第二个目标，开发并测试了六个候选推荐域模型（RDM）。对每个候选 RDM 级别拟合描述作物对种植密度响应的线性混合效应模型。当前田地种植密度和 RDM 级别下的最佳种植密度之间观察到的盈利能力差异代表了从田地获得的额外加工商利润（$ ha-1）。围绕“生产区”（RDMPA）构建的 RDM 似乎最适用，因为基于 RDMPA 的种植密度建议可以使加工商利润最大化，同时使种植者收益最大化，优于其他 RDM。与当前种植密度相比，在 RDMPA 下的种植密度下，加工商利润和种植者收益分别增加了 448 美元/公顷和 82 美元/公顷。
