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• 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]