A Research Method for Semi-Automated Large-Scale Cultivation of Maize to Full Maturity in an Artificial Environment

被引:4
|
作者
Wiethorn, Matthew [1 ]
Penn, Chad [2 ]
Camberato, James [1 ]
机构
[1] Purdue Univ, Dept Agron, W Lafayette, IN 47907 USA
[2] USDA ARS, Natl Soil Eros Res Lab, 275 S Russell St, W Lafayette, IN 47907 USA
来源
AGRONOMY-BASEL | 2021年 / 11卷 / 10期
基金
美国农业部;
关键词
artificial growth environment; indoor maize growth; nutrient use research; precision maize growth; nutrient use efficiency; nutrient application timing; ROOT-GROWTH; DRY-MATTER; CORN ROOTS; PHOSPHORUS; YIELD; NUTRIENTS; ACCUMULATION; SENESCENCE; MORPHOLOGY; MECHANICS;
D O I
10.3390/agronomy11101898
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
There are unique advantages and disadvantages to using the field, greenhouse, growth chamber, and media-less techniques for growing maize (Zea mays L.) for research purposes. Soil-buffered nutrients such as phosphorus (P) do not allow for precise control of solution concentrations in the field, while greenhouses, growth chambers, and hydroponics provide limiting conditions. The objectives of this study were to develop a practical technique for productively cultivating several maize plants from seed to physiological maturity (R6) in a grow room environment, with precise control of nutrient availability and timing, and evaluate its utility for the purpose of measuring plant responses to variations in nutrient concentrations. The construction and testing of a semi-automated grow room for conducting nutrient studies on 96 maize plants utilizing simulated or artificial conditions are described. Plant growth response to a range of solution phosphorus (P) concentrations was tested to evaluate the utility of the technique. Maize yield components were measured and compared to values for field-grown plants. Due to ideal conditions and successful simulation of light intensity, diurnal fluctuations in temperature and RH, and changing photoperiod, grain yield and tissue nutrient concentrations were comparable to field-grown maize, although with greater shoot biomass. Plants responded positively to increased P concentrations in fertigation. The technique can be used for large-scale plant nutrient studies that require precise control of bioavailability and timing as well as manipulation of light intensity and photoperiod duration.
引用
收藏
页数:21
相关论文
共 50 条
  • [31] Large-Scale Automated Analysis of News Media: A Novel Computational Method for Obesity Policy Research
    Hamad, Rita
    Pomeranz, Jennifer L.
    Siddiqi, Arjumand
    Basu, Sanjay
    OBESITY, 2015, 23 (02) : 296 - 300
  • [32] An Automated Analysis Method for Large-Scale Embedded Device Firmware
    Wang M.-T.
    Liu Z.-J.
    Chang Q.
    Chen Y.
    Shi Z.-Q.
    Sun L.-M.
    1600, Beijing University of Posts and Telecommunications (40): : 98 - 102
  • [33] An automated method for large-scale monitoring of seed dispersal by ants
    Audrey Bologna
    Etienne Toffin
    Claire Detrain
    Alexandre Campo
    Scientific Reports, 7
  • [34] An automated method for large-scale monitoring of seed dispersal by ants
    Bologna, Audrey
    Toffin, Etienne
    Detrain, Claire
    Campo, Alexandre
    SCIENTIFIC REPORTS, 2017, 7
  • [35] Large Scale Semi-Automated Labeling of Routine Free-Text Clinical Records for Deep Learning
    Trivedi, Hari M.
    Panahiazar, Maryam
    Liang, April
    Lituiev, Dmytro
    Chang, Peter
    Sohn, Jae Ho
    Chen, Yunn-Yi
    Franc, Benjamin L.
    Joe, Bonnie
    Hadley, Dexter
    JOURNAL OF DIGITAL IMAGING, 2019, 32 (01) : 30 - 37
  • [36] Research on optimization design method of large-scale pile-raft foundation in complex environment
    Xie Yun-fei
    Chi Shi-chun
    Zhou Xiong-xiong
    ROCK AND SOIL MECHANICS, 2019, 40 : 486 - 493
  • [37] Artificial intelligence in laparoscopic simulation: a promising future for large-scale automated evaluations
    Francisca Belmar
    María Inés Gaete
    Gabriel Escalona
    Martín Carnier
    Valentina Durán
    Ignacio Villagrán
    Domenech Asbun
    Matías Cortés
    Andrés Neyem
    Fernando Crovari
    Adnan Alseidi
    Julián Varas
    Surgical Endoscopy, 2023, 37 : 4942 - 4946
  • [38] Artificial intelligence in laparoscopic simulation: a promising future for large-scale automated evaluations
    Belmar, Francisca
    Gaete, Maria Ines
    Escalona, Gabriel
    Carnier, Martin
    Duran, Valentina
    Villagran, Ignacio
    Asbun, Domenech
    Cortes, Matias
    Neyem, Andres
    Crovari, Fernando
    Alseidi, Adnan
    Varas, Julian
    SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES, 2023, 37 (06): : 4942 - 4946
  • [39] Large Scale Semi-Automated Labeling of Routine Free-Text Clinical Records for Deep Learning
    Hari M. Trivedi
    Maryam Panahiazar
    April Liang
    Dmytro Lituiev
    Peter Chang
    Jae Ho Sohn
    Yunn-Yi Chen
    Benjamin L. Franc
    Bonnie Joe
    Dexter Hadley
    Journal of Digital Imaging, 2019, 32 : 30 - 37
  • [40] A Large-Scale Secure Image Retrieval Method in Cloud Environment
    Xu, Yanyan
    Zhao, Xiao
    Gong, Jiaying
    IEEE ACCESS, 2019, 7 : 160082 - 160090