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While cassava is an important crop in diverse regions of Thailand, little information is available to compare sites, select planting dates, and determine nitrogen (N) requirements. In recent years, the Decision Support System for Agrotechnology Transfer (DSSAT) has been used to develop this information. In order to use DSSAT, the cassava model, namely, CSM-CSCRP-Cassava, needs to be calibrated and validated. A cassava response to nitrogen study was conducted in Thailand during the 2011-2012 growing season. The data were also utilized to calibrate the DSSAT cassava model on cultivar Kasetsart 50. The model could be calibrated to predict the first branching date at 116 days, when it actually occurred at 117 days after planting. The overall average top dry weight and dry root yield were 7.39 and 15.69 t • ha −1 , which were predicted with a root mean square error of 0.496 and 0.702, respectively. Maximum leaf area index, leaf N (%), and harvested root N (%) were also adequately simulated. Validation experiments were conducted at the diverse Lopburi, Supanburi, and Chonburi sites. Top dry weight and dry root yield were predicted with indexes of agreement of 0.86 and 0.95 in Lopburi, 0.82 and 0.95 in Supanburi, and 0.83 and 0.55 in Chonburi. Nitrogen requirements for maximum yield were overpredicted by the model, indicating additional work is needed to account for negative effects of excessive N. Effects of regional weather conditions and soil types appeared to be adequately predicted by the calibrated model. Improved planting dates were suggested with the calibrated model.

The moisture characteristics and related pore size distributions of Thai Oxisols were determined for 11 representative pedons. These soils are Kandiustox and Kandiudox located in the North-east Plateau, South-east Coast, and Peninsular Thailand. They are generally acidic (pH 5–6), clayey, have low cation exchange capacity (5–15 cmolc/kg) and have low bulk densities (0.77–1.36 Mg/m3). The microstructures of these soils are mostly granular with compound packing 10–1500 μm voids between aggregates. Water retention curves for all soils are similar with amounts of water retained at 33 kPa (field capacity) ranging from 22 to 43 (%weight) and 1500 kPa (permanent wilting point) ranging from 17 to 34%. Water available to plants is 3–12%. There is a strongly bimodal pore size distribution with the modal macropore and micropore diameters calculated from the soil moisture retention characteristic curves being about 100 μm and 0.02 μm. The pore size distribution determined by N2-BET method indicates a modal micropore of about 0.01 μm. The total volume of pores >5 μm estimated by image analysis of scanning electron micrographs ranges from 20 to 50% with most of the variation in porosity being due to large differences in macropore (>75 μm) volume.

No abstract

Effective soil erosion prediction models and proper conservation practices are important tools to mitigate soil erosion in hillside agricultural areas. The Water Nutrient and Light Capture in Agroforestry Systems (WaNuLCAS) and Water Erosion Prediction Project (WEPP) models are capable tools in soil erosion simulation in the conventional and conservation cropping systems in hillslopes. We calibrated both the models in maize monocropping and simultaneously validated them in maize-chili intercropping with Leucaena hedgerow for nine rainfall events in 2010, with the aim to evaluate their performances in runoff and sediment prediction on a skeleton soil in a hillslope, Western Thailand. The results showed that the calibrated WaNuLCAS model poorly predicts runoff prediction in the validation. In contrast, the calibrated WEPP model had a better performance in runoff prediction in the validation. For sediment prediction, the calibrated WaNuLCAS model predicted sediment yield better than the calibrated WEPP model in the validation because the WEPP model shows more variability of the sediment yield in the calibration (5.84 kg m –2 ) than the WaNuLCAS (5.18 kg m –2 ). Thus, the WEPP model was more suitable for runoff prediction than sediment prediction in the monocropping system, whereas the WaNuLCAS model was better suited for sediment yield prediction than runoff prediction, especially in complex intercropping systems.