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Sesame ( Sesamum indicum L.) yield response to nitrogen (N) application has been well documented, but the temporal dynamics of N accumulation and partitioning by sesame are poorly understood. Such knowledge is needed to better inform fertilization decisions and improve N use efficiency (NUE). The objective of this study was to characterize N and biomass accumulation, partitioning, and remobilization among diverse sesame cultivars grown in the humid southeastern USA. A two-year field study was conducted at the University of Florida Plant Science Research and Education Unit in Citra, Florida in 2013 and 2014. Four commercially available cultivars (S30, S32, S36, and S39) with variable maturity and branching characteristics were sampled at multiple growth stages and divided into vegetative (stalks plus leaves), reproductive (capsules plus seed), and root (only S30 and S36) components to determine biomass and N accumulation. Medium-maturing cultivars (S30 and S32) reached peak aboveground biomass accumulation at late-bloom stage and accumulated ∼5100 kg biomass ha −1 by dry down, while peak aboveground biomass accumulation for late-maturing cultivars (S36 and S39) was reached at physiological maturity and amounted to 8242–8739 kg biomass ha −1 at dry down. Cultivars reached peak N accumulation at mid-bloom (S30), late-bloom (S32 and S39), and physiological maturity (S36). Total N accumulated by cultivars ranged from70 to 99 kg N ha −1 in aboveground biomass, of which 33–60% was taken up during the reproductive growth phase. It was also estimated that 31–66% of aboveground vegetative N was remobilized to capsules and seed during reproductive growth. Root N accumulation peaked at mid-bloom stage, at which point S30 and S36 had accumulated approximately 4 kg N ha −1 . Removal of N with harvested seed amounted to 29–49 kg N ha −1 , with S36 removing more total N with harvest than both medium-maturing cultivars. This pattern of N accumulation suggests that plant available N has the potential to impact sesame reproductive nutrient allocation, but additional research is needed to determine if the impact of the timing of N fertilization differs among more diverse sesame maturity classes. [ABSTRACT FROM AUTHOR]

• Irrigation decision support systems (DSS) are needed for new crops introduced to regions to ensure efficient use of water resources. • Irrigation DSS may be particularly important for drought tolerant crops to capitalize on inherent crop water deficit resistance. • SesameFARM1 was developed using FAO crop coefficients for sesame to calculate daily plant available water based on weather data, crop evapotranspiration, and maximum rooting depth. • SesameFARM2 was an improvement of the original model made by using annual leaf area index and root architecture measurement over six years of research. • In yearly simulations, SesameFARM2 was a more conservative irrigation DSS than SesameFARM1, reducing water application recommendation and likely improving overall irrigation crop water productivity. Irrigation decision support systems (DSS) are tools that can help achieve higher system level water use efficiency by more accurately targeting water application to crop need. They also have a role to play in preventing over-irrigation of drought tolerant crops that can be sensitive to flooded conditions. However, there are challenges in developing DSS for newly introduced crops in regions where they have not typically been produced. One such crop is sesame, a recently introduced drought tolerant crop in the southeastern United States where up to 50% of the agronomic crop production is irrigated. A first irrigation DSS, called SesameFARM1, was developed in 2013 by estimating a water demand curve using the Food and Agricultural Organization (FAO) crop coefficient (Kc) values, seasonal measures of the leaf area index (LAI) for the crop measured in 2012, and an estimated maximum rooting depth. On a daily time-step, SesameFARM1 estimated crop evapotranspiration using the product of the Kc and the ETo obtained from a local weather station, and an estimation of the maximum plant available water based on the available water capacity for the soil type and the maximum crop rooting depth. To improve SesameFARM1, LAI data from 6 years of field trials along with root data from a previous study were used to develop a breakpoint regression model for LAI and total functional root length. In SesameFARM2, the resulting curve for LAI was used to estimated continuous Kc values; and total measured functional root length replaced the estimation of maximum rooting depth in a new variable called root water access. SesameFARM2 performance was compared to SesameFARM1 using 6 years of weather data from Citra FL. SesameFARM2 consistently recommended less water be applied, and recommendations of water application during the senescence phase of the crop were reduced. Because of sesame's inherent drought tolerance, a more conservative irrigation recommendation is likely more appropriate. Therefore, SesameFARM2 is likely a better model than SesameFARM1 and may help growers unfamiliar with sesame achieve irrigation higher irrigation crop water productivity. [ABSTRACT FROM AUTHOR]