Meteorological and lysimetric data for a period of nine years were used to develop crop coefficients for rice grown under lowland conditions in a sub-humid tropical climate in India. The estimated crop coefficients were found to be higher than those values recommended by FAO. A crop coefficient model with basal coefficient, moisture availability coefficient and surface wetness coefficient terms has been proposed and found suitable. On most counts, the moisture availability coefficient was found to be near unity and the wetness coefficient was found to be significant. The basal crop coefficients for lowland rice have also been presented for practical use with the proposed models. © 1994 Kluwer Academic Publishers.

S. Mohan

Department Of Civil Engineering

CROP COEFFICIENT

evapotranspiration

Irrigation

IRRIGATION SCHEDULING

LOWLAND RICE

Rice

water management

India

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Irrigation crop coefficients for lowland rice

Irrigation and Drainage Systems

The quantity of water available for irrigation is getting scarce in many countries and it assumes great importance for assured crop production, especially in view of the erratic behavior of the monsoon. Thus, there is a pressing need to improve the water efficiency of irrigation systems. One-way of improving the efficiency of the irrigation system is reusing the return flow from the irrigation system. This task requires quantification of return flow, which still remains as a grey area in irrigation water management. The estimation of return flow from the irrigation system is usually obtained using thumb rules depending upon the site-specific conditions like command area conditions and soil properties. In this paper, a hierarchical modeling technique, namely, regression tree is developed for return flow estimation. Regression tree is built through binary recursive partitioning. The effective rainfall, inflow, consumptive water demand, and percolation loss are taken as predictor variables and return flow is treated as the target variable. The applicability of the hierarchical model is demonstrated through a case study of Periyar-Vaigai Irrigation System in Tamil Nadu, India. The model performance shows a good match between the simulated and the field measured return flow values. Results of statistical analysis indicated that the correlation coefficients are high for both single as well as double crop seasons. © 2008 Elsevier B.V. All rights reserved.

Agricultural Water Management

Prediction of irrigation return flows through a hierarchical modeling approach

Five methods of computing reference evapotranspiration from a reference crop (Penman, radiation, Blaney-Criddle, Hargreaves and pan evaporation) have been studied for their applicability under different climatic conditions. The Penman method was taken as the standard and the other four methods were compared against this method. Good correlation was obtained between the values estimated by the four methods and the Penman method although differences in magnitude were found. Regression equations were developed to correct those differences in magnitude. The method suitable for the estimation of reference evapotranspiration for each climatic condition is also suggested. © 1991 Taylor and Francis Group, LLC.

Fulltext

Hydrological Sciences Journal

Intercomparison of evapotranspiration estimates [La comparaison des estimations de l’évapotranspiration]

Journal	Irrigation and Drainage Systems
Publisher	Kluwer Academic Publishers
ISSN	01686291
Open Access	No