Interest is growing in applying simulation models for the South Texas conditions, to better assess crop water use and production with different crop management practices. The Environmental Policy Integrated Climate (EPIC) model was used to evaluate its application as a decision support tool for irrigation management of com (Zea mays L.) in South Texas of the U.S. We measured actual crop evapotranspiration (ETc) using a weighing lysimeter, soil moisture using a neutron probe, and grain yield by field sampling. The model was then validated using the measured data. Simulated ETc using the Hargreaves-Samani equation was in agreement with the lysimeter measured ETc. Simulated soil moisture generally matched with the measured soil moisture. The EPIC model simulated the variability in grain yield with different irrigation regimes with r2 value of 0.69 and root mean square error of 0.5~;ton~;ha-1 . Simulation results with farm data demonstrate that EPIC can be used as a decision support tool for com under irrigated conditions in South Texas. EPIC appears to be effective in making long term and pre-season decisions for irrigation management of crops, while reference ET and phenologically based crop coefficients can be used for inseason irrigation management.

Remote sensing data can be integrated into crop models, making simulation improved. A crop model that uses remote sensing data was evaluated for its capability, which was performed through comparing three different methods of canopy measurement for cotton(Gossypium hirsutum L.). The measurement methods used were leaf area index(LAI), hand-held remotely sensed perpendicular vegetation index(PVI), and satellite remotely sensed PVI. Simulated values of cotton growth and lint yield showed reasonable agreement with the corresponding measurements when canopy measurements of LAI and hand-held remotely sensed PVI were used for model calibration. Meanwhile, simulated lint yields involving the satellite remotely sensed PVI were in rough agreement with the measured lint yields. We believe this matter could be improved by using remote sensing data obtained from finer resolution sensors. The model not only has simple input requirements but also is easy to use. It promises to expand its applicability to other regions for crop production, and to be applicable to regional crop growth monitoring and yield mapping projects.

Expanded rice husk (ERR) is different from commercial rice seedling media in chemical and physical properties such as pH, permeability, and water content. This study was conducted to test a possibility of improving rice seedling growth by improving the texture of ERR as a rice seedling medium. The seedling media used were a commercial seedling medium (CSM), rice husk, and ERR 1, 2, 3, and 4 with different expansion degrees. The pH of the ERHs ranged from 6.3 to 6.8. As the expansion rate increased, ERR particle sizes decreased, and water permeability and absorption rates improved. No significant differences in shoot dry weight and rate of maturity were found among the seedlings cultivated in the different ERH media. However, the mat formation of seedling roots became loose as the expansion rates were decreased. Further studies are necessary to determine the cause of poor root growth in ERH media.

Rice farmers can save labor and expenses by using expanded rice husk (ERH) as a seedling medium since ERH is lighter and cheaper than other commercial seedling media (CSM). This study was carried out to develop a method for rice seedling cultivation using ERH as a seedling medium. It is suggested that a mixture of 60~% of ERH and 40~% of a CSM could be used as a seedling medium; the planting densities would be 240g per tray for infant seedlings and 200 g for young seedlings; and nitrogen (N) would be applied at a rate of 1g per tray for infant seedlings prior to planting and 2g per tray for young seedlings with division. Great care should be taken to use CO(NH2)2 as an N-source fertilizer. These results would lay a foundation for the rice seedling cultivation with ERH as a medium.