Modelling partitioning between structure and storage in sugar beet: Effects of drought and soil nitrogen

In the UK sugar beet is grown on contrasting soils that vary both in their nutritional status and water-holding capacities. Water shortage and low nitrogen reduce canopy growth and dry matter production, which is compensated in part by an increase in the fraction of assimilates partitioned to storage. Conversely, high nitrogen and ample water encourage growth of the canopy, increase assimilation of carbon dioxide but reduce the proportion of assimilates stored as sugar. This paper sets out to examine simple relationships between sugar yield, total dry matter and soil nitrogen in rain-fed and irrigated sugar beet crops (Beta vulgaris L.) from 46 field experiments spanning 12 years and a range of soil types, in order to improve prediction of sugar yields. Two partitioning functions were fitted to the data. The first represents a useful alternative formulation of the allometric growth function that overcomes some of the difficulties in the interpretation of the parameters. This model adequately described the seasonal progress of sugar yield (Y) in relation to total dry matter (W), but it was difficult to postulate biological mechanisms as to how the parameters should vary in relation to varying soil nitrogen or to drought. The second partitioning function, given by Y = W − (1/k) log(1 + kW), also described the data well, but had the more useful parameter, k, the decay rate of the fraction of assimilates partitioned to structural matter. This was shown to be greater in crops which had experienced significant drought and was inversely proportional to the amount of nitrogen taken up by the crops. Relationships between k and amounts of nitrogen fertilizer applied and/or amounts of residual nitrogen in the soil at sowing, however, were more variable. These could be improved by additionally taking account of soil type and rainfall following nitrogen fertilizer application in late spring. The models are a useful extension to yield forecasting models because they provide a simple means of estimating sugar yield from total dry matter in relation to factors that affect partitioning of assimilates such as drought and soil nitrogen availability. This revised version was published online in June 2006 with corrections to the Cover Date.