CHLOROPHYLL METER TECHNOLOGY: A PROMISING DIAGNOSTIC TOOL IN THE PRODUCTION OF CORN AND PATCHOI IN TRINIDAD

Fertilizer management is crucial to competitive food production, mainly because plant nitrogen (N) is a major limiting factor in crop productivity. As such, affordable and efficient modern technologies like the use of chlorophyll meters (CMs), portable instruments that are not only easy to use but also non-destructively measures the 'greenness' of leaves and by extension the health of the plant, are constantly being developed and redefined. The operation of CMs are based on the principle that sample readings from plants in a homogenous area out in the field, can be correlated to leaf Ν concentration and then to Ν fertilizer recommendations, instantaneously. Research in leading temperate centres have been generally promising, especially with respect to corn, Zea mays L. In Trinidad, laboratory diagnostic testing is underexploited by farmers, most likely due to a lack of understanding of the long term costs of improper plant nutrition management and its effect on soil productivity. The following research sought to validate whether CMs, particularly the YARA N-Tester, can be an appropriate alternative in the cultivation of corn and patchoi (Brassica rapa L. subsp. chinensis) under limited resources. Field and greenhouse trials were developed around traditional practices in an attempt to locally calibrate the instrument, utilising a range of fertilizer treatments in a complete randomized (block) design. The data collected and analysed from these experiments produced very significant relationships (P< 0.05) for both plant species with respect to N-Tester values (NTV) and leaf Ν concentrations (LNC). However, the strongest correlations obtained for corn (var. ICTA Farm) was found to be within a particular time frame and patchoi (var. Pak Choy White) while highly sensitive to differing Ν levels, its fresh marketable yield proved to be greatly influenced by leaf area and number. An increased experimental population and number of replications would better serve to produce more sound regression models.


Issue Date:
2013
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/253520
Total Pages:
13




 Record created 2017-04-01, last modified 2017-04-26

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