Toposequential effects on water balance and productivity in rainfed lowland rice ecosystem in Southern Laos

In the Mekong region, most paddies in rainfed lowland rice (Oryza sativa L.) lie in a sequence on gentle sloping land, and grain yield (GY) often depends on the toposequence position. There is, however, lack of information on toposequential effects on field water supply in rainfed lowland rice and how that influences GY. A total of eight field experiments were carried out on sandy, coarse-textured soils in Southern Laos (Champassak Province and Savannakhet Province) over three wet seasons (2000-2002). Components of the water balance, including downward and lateral water movement (D and L, respectively), were quantified at three different positions along toposequences (top, middle and bottom). GY, days-to-flower (DTF) and rainfall were measured, and the water productivity (WP) was determined. In most experiments, standing water disappeared first in the top position and gradually in lower positions. This was associated with the observation that when there was standing water in the field, the higher position had larger D in both the provinces and also larger L in Champassak Province. However, in one experiment, water loss appeared later in the higher position, as the result of lower L, apparently due to some water inputs other than rainfall occurring at this position. Despite larger D plus L at the top position, seasonal sum of D and L were not much affected by the toposequence position, as the daily rate of D plus L became minimal when the standing water was lost earlier in the top position. Lower GY was associated with earlier disappearance of standing water from the field. Relatively low GY was expected in the top toposequence position. This was clearly shown in the toposequence of Phonthong, Champassak Province, as the timing of standing water disappearance relative to flowering was earlier in the top position. Variation in GY across the toposequence positions was coupled with the WP variation, and both GY and WP tended to decline with increased DTF. Therefore, variation in productivity of rainfed lowland rice across toposequence positions depends mainly on the field water status around flowering time. (c) 2005 Elsevier B.V. All rights reserved.

Irrigation regime affected SOC content rather than plow layer thickness of rice paddies : A county level survey from a river basin in lower Yangtze valley, China

Acknowledgements This work was funded by Natural Science Foundation of China under grant numbers of 41071337 and 40830528 and jointly by the Priority Academic Program Development of Jiangsu Higher Education Institutions, China.

Study and comparison on freshwater snail fauna in the paddy fields and related river and lake (Babolroad & Parishan) water supply in Mazandaran & Frace provinces of Iran

As a result of this investigation, for the first time from Babolroud river in Iran is 15 species identified, which they belong to 8 genera, from 7 families. The most and least distribution, 13 and 5 species, belong to Miandasht and Rostaye Anarestane Babol stations respectively, which they are 50 kilometers away from each other. Also 20 species belong to 10 genera from 6 families were identified in Parishan lake. The most the distribution belong to stations 1 and 5 with 6 and 18 species respectively. The most commonly distributed family is Lymnaeidae with 6 species: Lyamnaea truncatula, L. auricularia, L. palustris, L. pereger, L. stagnalis and L. gedrosiana, which L. trancatula is identified as the most frequent and has medical importance. L. stagnalis is identified to be the most important to agriculture. Planorbis planorhis, Physa acuta, Lymnaea pereger, Bithynia tenculata and Vavata piscinalis are reported for the first time from this region.

PCA-ANFIS based prediction for water Injection effectiveness cycle in oil fields.

By proposing a numerical based method on PCA-ANFIS(Adaptive Neuro-Fuzzy Inference System), this paper is focusing on solving the problem of uncertain cycle of water injection in the oilfield. As the dimension of original data is reduced by PCA, ANFIS can be applied for training and testing the new data proposed by this paper. The correctness of PCA-ANFIS models are verified by the injection statistics data collected from 116 wells inside an oilfield, the average absolute error of testing is 1.80 months. With comparison by non-PCA based models which average error is 4.33 months largely ahead of PCA-ANFIS based models, it shows that the testing accuracy has been greatly enhanced by our approach. With the conclusion of the above testing, the PCA-ANFIS method is robust in predicting the effectiveness cycle of water injection which helps oilfield developers to design the water injection scheme.