Water saving rice cultivation using sheet-pipe subsurface irrigation.

Water saving in rice cultivation has assumed paramount importance, especially in the context of climate change. The introduction of sheet-pipe technology in Indonesia heralded as an innovative subsurface irrigation and drainage system, is poised to revolutionize how to manage this vital resource. Our study was designed with two primary objectives: first, to investigate how rice plants respond when water levels are deliberately reduced using the sheet-pipe technology; and second, to comprehensively analyze water productivity and water use efficiency in comparison to conventional flooded rice cultivation systems. We conducted two distinct experiments: one employing sheet-pipe subsurface irrigation (SSI) and the other utilizing conventional flooded irrigation (CFI). In the SSI setup, the water level was maintained at a depth of 5-10 cm below the soil surface 20 days after transplanting to harvesting. With this setting, the soil moisture was maintained at around 85-95 degrees of saturation. On the other hand, the CFI approach involved water flowing directly over the soil surface, with the water level consistently maintained at a mere 2-3 cm above it. Interestingly, while the SSI method did lead to a reduction in yield, it has significant benefits. Our results showed that a reduction in yield was observed for the SSI 15.5-18.6 % lower compared to the conventional method (CFI). However, the SSI is environmentally benefit compared to the conventional method by reducing 37.5-50.5 % in water irrigation, increasing water use efficiency (WUE) up to 70.8 %, and improving 3.2-10.4 % in water productivity. Our findings reveal that optimizing water conservation may have a disadvantageous effect on rice yield, indicating the importance of optimal water level. Future research to find the optimal water level that balances yield production and environment is required, especially to adapt to dry and warming climate change in the future.

Dataset of physical properties of histosols topsoils effected by wildfire in Indonesia.

Physical properties of peat are widely applied to detect the quality of peatland ecosystem. A comprehensive dataset on the peat properties is the foundation for the development tool and model of peat ecosystem, especially in region with frequent wildfire. Here we established a tabular dataset for physical properties of lowland tropical peatland in Indonesia. The data were obtained in dry season 2019 and 2023, respectively, at Jambi and Central Kalimantan peatlands. The dataset comprises of 66 peat samples from two land-uses namely secondary forest and ex-burned lowly vegetation. The physical properties are bulk density, porosity, water retention at four pressures (-1, -10, -25, and -1500 kPa), and water holding capacity. In addition, a set parameter of van Genuchten for water retention curve is available. The field-observed dataset provides a solid base for a better understanding of physical peat properties and can be used as a first step to develop peat water retention database in lowland tropical peatlands.

Peat moisture dataset of Sumatra peatlands.

Peatland is a unique ecosystem that is key in regulating global carbon cycle, climate, hydrology, and biodiversity. Peat moisture content is a key variable in ecohydrological and biogeochemical cycles known to control peatland's greenhouse gas emissions and fire vulnerability. Peat moisture is also an indicator of the success of peat restoration projects. Here we present datasets of peat moisture dynamic and retention capacity of degraded tropical peatlands. The data were collected from automatic daily monitoring and field campaigns. The peat moisture content data consists of daily data from 21 stations across three peatland provinces in Sumatra Island, Indonesia, from 2018 to 2019. In addition, peat water retention data were collected from field campaigns in Riau province. This dataset represents human modified peatlands which can be used as a benchmark for hydrological and biogeochemical models.

Pixel color clustering of multi-temporally acquired digital photographs of a rice canopy by luminosity-normalization and pseudo-red-green-blue color imaging.

Red-green-blue (RGB) channels of RGB digital photographs were loaded with luminosity-adjusted R, G, and completely white grayscale images, respectively (RGwhtB method), or R, G, and R + G (RGB yellow) grayscale images, respectively (RGrgbyB method), to adjust the brightness of the entire area of multi-temporally acquired color digital photographs of a rice canopy. From the RGwhtB or RGrgbyB pseudocolor image, cyan, magenta, CMYK yellow, black, L*, a*, and b* grayscale images were prepared. Using these grayscale images and R, G, and RGB yellow grayscale images, the luminosity-adjusted pixels of the canopy photographs were statistically clustered. With the RGrgbyB and the RGwhtB methods, seven and five major color clusters were given, respectively. The RGrgbyB method showed clear differences among three rice growth stages, and the vegetative stage was further divided into two substages. The RGwhtB method could not clearly discriminate between the second vegetative and midseason stages. The relative advantages of the RGrgbyB method were attributed to the R, G, B, magenta, yellow, L*, and a* grayscale images that contained richer information to show the colorimetrical differences among objects than those of the RGwhtB method. The comparison of rice canopy colors at different time points was enabled by the pseudocolor imaging method.