Agricultural Loss Caused by 2007 Sidoharjo’s Flood and its House-hold Impact - Leo Agung Widiarto; Nanette Kingma (Faculty of Geography, Universitas Gadjah Mada, 2014)
Flood is an undeniable reality that threaten Sidoharjo Village, as part of Sragen District. It is affected by the presence of Mungkung River, the tributary of Solo River, which crosses in the area. It is certainly going to be one of the factors inhibiting the development and economic growth in the region, given the agricultural sector is one of the backbones of the economy potentially disturbed by the flood. The information about the flood and its impacts specifically related to agriculture are needed to determine the precise policies. The research focuses on 2007-flood mapping, agricultural production loss assessment, and farmer resilience, as expressed in their ability to continue the next cropping after being hitten by the 2007-flood. The flood map was built by integrating the local knowledge and the Digital Terrain Model (DTM). The information about 2007 flood was collected by interviewing the local people. The DTM was built by interpolating the detailed spot height directly measured in the field. As the result of the integration, the depth of the flood immersing the paddy fields reaches approximately 3 meters. Beside the flood depth, the growth stage of rice also determines the paddy vulnerability. It refers to the plant height and the sensitivity to the water immersion. There are three stages i.e. vegetative, generative, and graining phases. The vulnerabilities were constructed based on the synthetic data obtained via Focus Group Discussion (FGD). The production loss of paddy of the research area was counted based on the vulnerability. A grid-based GIS method is used in the loss calculation which produces a value of Rp. 1,137,350,000.00 (about USD 100,000.00). The losses influence the farmer ability to continue the cultivation in the next season, which in this study is defined as farmer resilience. To investigate the resilience level, 32 respondents were proportionally randomized to each flood zone. There are three zones created based on the flood depth. The influencing factors and their weights and scores were determined by the farmer representatives via FGD. Meanwhile, the socioeconomic data were collected by using the questionnaires. The results show that most of the farmers in the area (56.3%) are categorized in moderate resilience level.