开枪
生物量(生态学)
干重
扎梅斯
作物
农学
园艺
生物
环境科学
作者
Charles Hunt Walne,K. Raja Reddy
出处
期刊:Agriculture
[MDPI AG]
日期:2022-03-22
卷期号:12 (4): 443-443
被引量:27
标识
DOI:10.3390/agriculture12040443
摘要
Temperature is a critical environmental factor regulating plant growth and yield. Corn is a major agronomic crop produced globally over a vast geographic region, and highly variable climatic conditions occur spatially and temporally throughout these regions. Current literature lacks a comprehensive study comparing the effects of temperature on above versus below-ground growth and development and biomass partitioning of corn measured over time. An experiment was conducted to quantify the impact of temperature on corn’s early vegetative growth and development. Cardinal temperatures (Tmin, Topt, and Tmax) were estimated for different aspects of above- and below-ground growth processes. Plants were subjected to five differing day/night temperature treatments of 20/12, 25/17, 30/22, 35/27, and 40/32 °C using sun-lit controlled environment growth chambers for four weeks post-emergence. Corn plant height, leaves, leaf area, root length, surface area, volume, numbers of tips and forks, and plant component part dry weights were measured weekly. Cardinal temperatures were estimated, and the relationships between parameters and temperature within these cardinal limits were estimated using a modified beta function model. Cardinal temperature limits for whole plant dry weight production were 13.5 °C (Tmin), 30.5 °C (Topt), and 38 °C (Tmax). Biomass resources were prioritized to the root system at low temperatures and leaves at high temperatures. Root growth displayed the lowest optimum temperature compared to root development, shoot growth, and shoot development. The estimated cardinal temperatures and functional algorithms produced in this study, which include both above and below-ground aspects of plant growth, could be helpful to update crop models and could be beneficial to estimate corn growth under varying temperature conditions. These results could also be applicable when considering management decisions for maximizing field production and implementing emerging precision agriculture technology.
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