Documentation of my internship at NIDEC Precision Philippines

The paper is composed of the internship of the author during the 1st term of the academic year 2012-2013. This paper serves as the documentation of the author\u27s internship in NIDEC Precision Philippines, a leading manufacturer of the tiny electric motor that power hard disk drives on personal computers and other digital electronics in the world at Laguna Technopark, Special Economic Zone, Binan, Laguna. In this paper, included are tasks that were assigned to the author during the internship. Monitoring downtime and technicians\u27 performance was the main tasks assigned to the author as a Productivity Staff in NIDEC Precision Corporation. The tasks and activities, tools/software used, outputs, were summarized and expounded in this paper to provide a better view of the taken internship. Furthermore, the paper also includes principles and lesson experiences by the author throughout the whole internship. The lesson learned includes communication skills, interpersonal skills, flexibility / adaptability, detail-oriented, and core management courses. This will provide self-evaluation of the author\u27s experience in the internship. These lessons helped to develop the strengths of and eliminate the weaknesses of the author

Market opportunities to improve vegetable value chains and rural livelihoods in southern Philippines

Two thirds of the population in the southern Philippines are dependent on agriculture yet the country has one of the world’s lowest per capita consumption of fresh produce. Smallholders are poor and incomes are limited by poor integration with markets. In line with the Philippines Development Plan 2011-2016, there is a major opportunity to enhance the performance of vegetable value chains (VC) and to improve the livelihood of small farmers. A study was undertaken to assess how vegetable VC performance could be improved. The study was conducted at five sites in the southern Philippines. It focused on five vegetables – eggplant, tomatoes, sweet pepper, ampalaya and leafy vegetables. Rapid appraisals of vegetable VCs were conducted using observations and interviews with key chain members and associated stakeholders. Existing vegetable VCs were examined to highlight material flow, information flow and relationship along the chains. Key issues along the chain were explored. Three potential chain development models were proposed based on who in a chain takes the position of captain for leading development interventions – smallholders, wholesalers or retailers

Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project

Rice is the most important food security crop in Asia. Information on its seasonal extent forms part of the national accounting of many Asian countries. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland rice, especially in tropical and subtropical regions, where pervasive cloud cover in the rainy seasons precludes the use of optical imagery. Here, we present a simple, robust, rule-based classification for mapping rice area with regularly acquired, multi-temporal, X-band, HH-polarized SAR imagery and site-specific parameters for classification. The rules for rice detection are based on the well-studied temporal signature of rice from SAR backscatter and its relationship with crop stages. We also present a procedure for estimating the parameters based on “temporal feature descriptors” that concisely characterize the key information in the rice signatures in monitored field locations within each site. We demonstrate the robustness of the approach on a very large dataset. A total of 127 images across 13 footprints in six countries in Asia were obtained between October 2012, and April 2014, covering 4.78 m ha. More than 1900 in-season site visits were conducted across 228 monitoring locations in the footprints for classification purposes, and more than 1300 field observations were made for accuracy assessment. Some 1.6 m ha of rice were mapped with classification accuracies from 85% to 95% based on the parameters that were closely related to the observed temporal feature descriptors derived for each site. The 13 sites capture much of the diversity in water management, crop establishment and maturity in South and Southeast Asia. The study demonstrates the feasibility of rice detection at the national scale using multi-temporal SAR imagery with robust classification methods and parameters that are based on the knowledge of the temporal dynamics of the rice crop. We highlight the need for the development of an open-access library of temporal signatures, further investigation into temporal feature descriptors and better ancillary data to reduce the risk of misclassification with surfaces that have temporal backscatter dynamics similar to those of rice. We conclude with observations on the need to define appropriate SAR acquisition plans to support policies and decisions related to food security