Location: Soil Management ResearchTitle: Modeling sugar content of farmer-managed sugar beets (Beta vulgaris L.)) Author
Submitted to: Communications in Biometry and Crop Science (CBCS)
Publication Type: Peer reviewed journal
Publication Acceptance Date: 4/2/2012
Publication Date: 4/24/2012
Publication URL: handle.nal.usda.gov/10113/54115
Citation: Jaradat, A.A., Rinke, J.L. 2012. Modeling sugar content of farmer-managed sugar beets (Beta vulgaris L.). Communications in Biometry and Crop Science. 7(1):22-34. Interpretive Summary: Sugar beet crops are usually composed of highly variable plant populations due to the fact that planted varieties may respond differently to weather, management practices, and soil factors. We measured or estimated physical and chemical traits on leaves and roots of field-grown, farmer-managed sugar beet. During three growing seasons, we developed a method to help identify which leaf and root traits may have positive or negative effect on sugar content in the developing roots. Most leaf and root traits displayed large variation early in the growing season; whereas, the carbon-to-nitrogen ratios in leaves and roots were less variable. However, all traits had significant impact on root sugar content. Root traits became increasingly more important than leaf traits in impacting root sugar content, displayed larger variation and maintained a significant impact on root sugar content during the middle part of the growing season. However, their carbon-to-nitrogen ratios were less variable and maintained a positive and significant impact on root sugar content. Late in the season, leaf and root traits maintained the same trends and displayed larger variation; whereas, their carbon-to-nitrogen ratios increased in magnitude and had stronger impact on root sugar content. The findings may help farmers and agronomists design management practices that enhance carbon storage in roots, maintain adequate levels of nitrogen in the developing leaves and roots and minimize variability while optimizing root sugar content.
Technical Abstract: We measured or estimated leaf and root physical and chemical traits of spatio-temporally heterogeneous field-grown sugar beet throughout its ontogeny during three growing seasons. The objective was to quantify the impact of temporal changes in these traits on root sugar content [S(R); g 100g**-1 root dry weight]. Artificial Neural Network, in conjunction with thermal time, adequately delineated the boundaries (mean ± standard deviation) between S(R) during early (41.6±6.2), med (54.5±3.0), and late ontogeny (63.4±2.4), corresponding, respectively to low, medium, and high S(R). Partial Least Squares (PLS) regression models, using plant physical and chemical traits, predicted and validated sugar content of sugar beet leaves [S(L)] and roots [S(R)] throughout its ontogeny with significant probabilities. Most physical and all chemical traits exhibited dynamic changes throughout plant ontogeny and, consequently, negatively or positively impacted S(R). The positive impact of S(L) and root volume on S(R) diminished towards the end of the growing season; whereas, the positive impact of root density and carbon:nitrogen (C:N) ratio in leaves [C:N(L)] and roots [C:N(R)] persisted throughout plant ontogeny. Specific leaf area, in particular, exhibited negative, then positive impact on S(R). The utility of physical and chemical traits of field-grown sugar beets in building reliable PLS models was confirmed using multivariate analysis on secondary statistics (residual mean square errors and validation coefficients of determination) which discriminated between and correctly classified low (100%), medium (95%) and high (97%) S(R) groups. The findings may have implications for designing management practices that can enhance C:N ratio and C-sequestration in roots; maintain optimum, but not excessive N levels in developing leaves and roots; optimize root sugar content; and minimize its variation under field conditions.