AbstractsPhysics

Water consumption and greenhouse gas emissions have emerged as major issues in rice production. Conventional rice farming with application of continuous flooding is not essential to achieve good yield and is known as a major source of greenhouse gas emissions from paddy fields. Hence, system of rice intensification (SRI) is proposed as an alternative of rice farming with more efficient water use for producing more rice and reducing greenhouse gas emissions. The main challenge in the application of SRI is finding the optimal water management to raise yield and water productivity and to reduce greenhouse gas emissions simultaneously. To support this purpose, improving the technology for collecting precise field data is important through continuous measurements of related variables by a field monitoring system (FMS). Therefore, this study was conducted to achieve this purpose based on the FMS data. Nine chapters were presented in this study. In chapter 1, the introduction presented the originality and the main objectives of this study. The main objectives were to develop and evaluate the FMS for SRI paddy field with different irrigation regimes, to identify effects of irrigation regime on yield and water productivity and greenhouse gas emissions, and then to find the optimal irrigation regime for maximizing yield and water productivity and reducing greenhouse gas emissions. In chapter 2, a method of data acquisition was presented. Here, we used the FMS consisting of a FieldRouter equipped with a surveillance camera and connected to meteorological and soil data loggers. The meteorological data consisted of solar radiation, air temperature, relative humidity, wind speed, and precipitation, while soil data consisted of soil moisture and soil temperature. The FieldRouter was set to automatically work from 12:00 to 12:30 PM (local time) regulated by a timer to collect the data, and then to send the data as well as a plant image to the data server through the GSM connection. The FMS was installed in Nusantara Organic SRI Center (NOSC), Nagrak, Sukabumi, West Java, Indonesia. Four SRI paddy plots under different irrigation regimes were monitored by the FMS. The FMS was demonstrated to be effective, efficient and reliable in monitoring the plots during 2010-2012. The actual field conditions were monitored well in terms of image, numeric and graphic data. The data were then used for further analyses to find the optimal SRI water management. In chapter 3, neural network (NN) models were proposed to estimate soil moisture based on meteorological data. Sometimes during the above monitoring period, some soil moisture data were lost by unexpected problems in the field, where the sensor was broken, the cable was unplugged or the data logger battery was depleted. Therefore, the motivation of this chapter was to solve the problems. We developed two NN models; the first model was developed to estimate reference evapotranspiration (ETo) according to maximum, average, and minimum values of air temperature and solar radiation; the second…