Development of Spatial Decision Support System for Village Based National Rice Production (Phase 1: Developing Baseline of Spatially-Explicit Dynamic Model)
Impron, Y. Setiawan, H. Imantho, O. A. Endiviana, S. W. Sugiarto, T. Yuliawan

Source: SEAMEO BIOTROP's Research Grant | 2020


According to the Food and Agriculture Organization (FAO), global demand for primary food (staple foods) will grow by 60% in 2050 as a result of demographic growth and changes in welfare and income levels. This increasing global demand is confronted with the uncertainty of sufficient food supply mainly due to global climate change which also correlates with the development/change of biological enemies such as plant pest and diseases.  In response to this problem, there is a need to increase agricultural production, efficiency in farming inputs, and proper use of technology and agricultural management systems that are designed towards sustainable agriculture. Hence, the adoption of information technology and mechanization in agriculture in the form of climate smart agriculture is a mandatory since it allows cultivation activities and agricultural inputs to be adequately managed following the needs of plants, soil conditions and the environment. Smart agricultural technology combined with data- based precision agriculture will elevate more productive and resilient agriculture.

Integration of GIS Technology, Remote Sensing and Information Technology can be used to monitor agricultural activities in a landscape through spatial-temporal computing models. With this model, monitoring of sub-unit scale of field activities can be carried out and can provide appropriate recommendations at each location depicted on the map/image. As the evolution of remote sensing, many models have been developed and used in agriculture. Several models have shown their capability to map and monitor spatial distribution of crop yields based on spectral information and topographic characteristics, soil characteristics, and meteorological data. Real-time information on the status of rice production is one of important factors in the formulation of strategic decisions by farmers (producers), private sector, and government. For instance, timely information and accurate estimation of the distribution and development phases of rice plants, yield potential and harvest area are very crucial in the management of agricultural inputs such as fertilizer and irrigation, supply chain strategies, including import and export. In addition, spatial planting lagis influenced by differences in paddy field types, geographical factors, and weather conditions. Those factors will cause variation in harvesting time and harvested area, which ultimately determines the dynamics of food supply and food sufficiency in certain districts, cities and throughout the country. 

The research entitled “Development of Spatial Decision Support System for Village Based National Rice Production (Phase 1)” has succeeded address several important information on the physiological responses of rice to different fertilizer doses, environmental effects (weather) and cropping patterns on the three selected varieties. The dynamics of LAI are generally uniform, experiencing an increase in LAI until it reaches a maximum vegetative growth stage at 47 days after planting, and then decreases towards harvest time. The similar and consistent response is shown by NDVI which is derived from the Sentinel-2 imagery. NDVI has significant response to the fertilizer dosage treatment. A higher fertilizer dosage gives a higher NDVI value, especially in the early stages until maximum vegetative stage. However, cropping patterns and varieties cannot be distinguished through this NDVI value.

Drone imagery demonstrated its potential to differentiate cropping patterns and varieties. The index values generated using the Red and Green channels show the similar pattern as the NDVI generated using the Sentinel-2 image. Further processing is required to produce an empirical formula that represents the spatio-temporal relationship between signature reflectance and plant characteristics-based drone imagery.

The interaction of soil plants and the environment, including weather is a dynamic process and may not be explained by simple regression analysis, and its proven by this research. Understanding plant physiological responses and interaction of soil plants and the environment can be address by integrating remoted sensed data with spatially explicit crop dynamics model. This is the ultimate goal of this research which is expected to be achieved at phase 3 of research activities.

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