Actual crop evapotranspiration (ET) and crop coefficient (Kc) of ratoon rice crop, which are necessary for irrigation planning, have been hardly reported. ET can be directly measured by lysimeter and eddy covariance but it is expensive, so it remains difficult to determine ET, especially in developing countries. The focus of this study was to evaluate the ET and Kc of ratoon cropping in a tropical region of Myanmar using a simplified method. Our method combined the manual observation of water depth in concrete paddy tanks and the ET model estimation using Bayesian parameter inference. The ET and Kc could be determined using this method with an incomplete observation dataset. The total ET of ratoon was 60–70% less than that of the main crop, but this difference was mainly attributed to climate conditions in each cultivation. The Kc regression curve between transplanted rice and ratoon crops was different because of the tillering traits. The results suggest that irrigation scheduling of ratoon cropping in the initial growth stage should take high crop water requirements into account. In addition, the productivity of ratoon crop is equivalent to transplanted rice, which was determined for cultivation in experiment conditions of small concrete tanks. Therefore, further study on ratoon in Myanmar is necessary for clarifying the viability of ratoon cropping.

The two-layer Shuttleworth–Wallace (SW) evapotranspiration (ET) model has been widely used for predicting ET with good results. Since the SW model has a large number of specific parameters, these parameters have been estimated using a simple non-hierarchical Bayesian (SB) approach. To further improve the performance of the SW model, we aimed to assess parameter estimation using a two-level hierarchical Bayesian (HB) approach that takes into account the variation in observed conditions through the comparison with a traditional one-layer Penman–Monteith (PM) model. The difference between the SB and HB approaches were evaluated using a field-based ET dataset collected from five agricultural fields over three seasons in Myanmar. For a calibration period with large variation in environmental factors, the models with parameters calibrated by the HB approach showed better fitting to observed ET than that with parameters estimated using the SB approach, indicating the potential importance of accounting for seasonal fluctuations and variation in crop growth stages. The validation of parameter estimation showed that the ET estimation of the SW model with calibrated parameters was superior to that of the PM model, and the SW model provided acceptable estimations of ET, with little difference between the SB and HB approaches.