Improvements to Rapfish: a rapid evaluation technique for fisheries integrating ecological and human dimensions^a

Abstract

This paper reports recent developments in Rapfish, a normative, scalable and flexible rapid appraisal technique that integrates both ecological and human dimensions to evaluate the status of fisheries in reference to a norm or goal. Appraisal status targets may be sustainability, compliance with a standard (such as the UN code of conduct for responsible fisheries) or the degree of progress in meeting some other goal or target. The method combines semi-quantitative (e.g. ecological) and qualitative (e.g. social) data via multiple evaluation fields, each of which is assessed through scores assigned to six to 12 attributes or indicators: the scoring method allows user flexibility to adopt a wide range of utility relationships. For assessing sustainability, six evaluation fields have been developed: ecological, technological, economic, social, ethical and institutional. Each field can be assessed directly with a set of scored attributes, or several of the fields can be dealt with in greater detail using nested subfields that themselves comprise multidimensional Rapfish assessments (e.g. the hierarchical institutional field encompasses both governance and management, including a detailed analysis of legality). The user has the choice of including all or only some of the available sustainability fields. For the attributes themselves, there will rarely be quantitative data, but scoring allows these items to be estimated. Indeed, within a normative framework, one important advantage with Rapfish is transparency of the rigour, quality and replicability of the scores. The Rapfish technique employs a constrained multidimensional ordination that is scaled to situate data points within evaluation space. Within each evaluation field, results may be presented as a two-dimensional plot or in a one-dimensional rank order. Uncertainty is expressed through the probability distribution of Monte-Carlo simulations that use the C.L. on each original observation. Overall results of the multidisciplinary analysis may be shown using kite diagrams that compare different locations, time periods (including future projections) and management scenarios, which make policy trade-offs explicit. These enhancements are now available in the R programming language and on an open website, where users can run Rapfish analyses by downloading the software or uploading their data to a user interface.

Appraisal Keberlanjutan Multidimensi Penggunaan Lahan Untuk Sawah Di Karawang - Jawa Barat

“…Dalam penerapan selanjutnya, aspek pembangunan berkelanjutan tidak hanya sebatas aspek ekologi, ekonomi dan sosial saja, namun dapat bertambah tergantung kebutuhan wilayah yang sedang dikaji. Sebagai contoh, Pitcher et al (2013) menambahkan dimensi teknologi, etika dan kelembagaan.…”

“…Kelompok responden pertama adalah responden petani, secara total ada 82 responden yang diambil secara acak, namun ada sampel dari semua kecamatan di Karawang. Jumlah responden ini tidak dimaksudkan untuk mewakili populasi (jumlah) petani secara statistik karena wawancara dengan petani lebih bersifat kualitatif untuk mendalami kriteria "baik" dan "buruk" (Pitcher et al, 2013). Data dari kelompok responden pertama ini digunakan untuk analisis indeks keberlanjutan.…”

“…Dalam metoda ini, dilakukan proses ordinasi menggunakan teknik ordinasi yang dalam penelitian ini di sebut Rapid Appraisalfor Paddy Field Land Use (Rap-LANDUSE), yang merupakan modifikasi dari RapFISH (Kavanagh, 2001;Pitcher et al, 2013). Teknik ordinasi menggunakan parameter yang diukur dengan teknik penskalaan (Fauzi dan Ana, 2005).…”

“…Teknik ordinasi menggunakan parameter yang diukur dengan teknik penskalaan (Fauzi dan Ana, 2005). Pendekatan MDS dipandang cukup stabil dibandingkan beberapa metoda multivariate lain (Pitcher dan Preikshot, 2001;Pitcher et al, 2013).…”

“…Beda nilai yang kecil juga menunjukkan bahwa pengaruh kesalahan terhadap pembuatan skor pada setiap atribut, kesalahan yang diakibatkan karena kurangnya pemahaman, perbedaan opini, atau penilaian dari peneliti yang saling berbeda, kesalahan pemasukan data atau data yang hilang, dan kesalahan posedur yang dapat mempengaruhi stabilitas proses analisis MDS, relatif kecil. Proses analisis data yang dilakukan secara berulang juga relatif stabil (Fauzi et al, 2005;Pitcher et al, 2013). (Kavanagh, 2001;Pitcher et al, 2013), nilai S-stress antara 0,14-0,16.…”