Using big data to predict the likelihood of low falling numbers in wheat
Document Type
Article
Publication Date
2-13-2019
Journal Title
Cereal Chemistry
ISSN
ISSN: 0009-0352 eISSN: 1943-3638
Keywords
climate, falling number, pre-harvest sprouting, wheat
Disciplines
Agricultural Science | Agronomy and Crop Sciences | Plant Breeding and Genetics
Abstract
Background and objectives
Preharvest sprouting in wheat reduces quality and impacts farmer profitability. The international recognized falling number test can be used to measure that damage. Trying to understand the complex interactions that cause a reduction in wheat quality, equating to low falling number levels, is challenging. An alternative research approach to replicated experiments was to use a multiseason dataset of load-by-load delivery information to investigate whether correlations between falling number levels and 40 climate measurements could be identified.
Findings
This study used over 250,000 falling number data points from individual truckloads tested during seven harvests in Western Australia. The analyses identified relative humidity measured at the maximum temperature and daily temperature range as having consistent correlations with falling number levels over multiple seasons. Other climate measurements were also observed to have significant correlations with falling number, but these were less consistent within and between seasons.
Conclusions
The linkage of humidity and temperature range levels in the period before harvest commences to the occurrence of low falling number levels helps to further understand the complex interactions that change starch quality.
Significance and novelty
The findings demonstrate that value can be obtained from the use of a large, nonexperimentally designed dataset.
Recommended Citation
Williams, R M,
Diepeveen, D A,
and
Evans, F H.
(2019), Using big data to predict the likelihood of low falling numbers in wheat. Cereal Chemistry, 96 (3), 411-420.
https://library.dpird.wa.gov.au/fc_researchart/272
