Leaf Area Index Development of Local Rice Varieties as a Response to Different Irrigation Management
Rizki Maftukhah, Andi Surahman Suli, Hertiyana Nur Annisa, Bayu D. A. Nugroho
Departement of Agricultural and Biosystem Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada, Jl. Flora No. 1, Bulaksumur, Yogyakarta 55281, Indonesia
Abstract
Rice is a semi aquatic plant and grown under anaerobic condition as long as water is available. Nowadays, water scarcity and climate change issues need to be address with new technology to increase water use efficiency in rice production. In the other hand, rice varieties must be able to adapt climate change in the future, especially drought even during rice growth periods. A shallow water depth irrigation, as a strategy to reduce water use might influence the rice growth development. The aim of this study was to characterize the leaf area index development of three different rice varieties grown in continuous flooding and shallow water depth irrigation. Pot experiments were conducted in Yogyakarta, Indonesia with three different rice varieties, i.e Mutiara, IR 64, and Hitam, and cultivated with two different irrigation system namely shallow water depth (SWD) and continuous flooding (CF). Leaf Area Index (LAI) was measured every 10 days and polynomial equation was used to describe LAI development during plant growth. Analysis of variance (ANOVA) was performed using Ms. Excel to determine significant differences between treatments. Pearson correlation coefficient (R) was used to evaluate the performance of mathematical model. Leaf Area Index (LAI) under shallow water depth irrigation in different rice varieties were not significantly different compare to continuous flooding irrigation. LAI development in different treatment were described by polynomial equation, with various correlation value, ranged between 0.71 to 0.97. IR64 variety under control irrigation resulted lowest R (0.71), indicated that prediction value from observation data was not strongly correlated. However, other treatments showed strong relationship between prediction and observation data.
Keywords: water scarcity, rice, irrigation, leaf area index
Topic: Biophysics engineering