Pengaruh Tipe Petani dan Ekspektasi Harga dalam Sertifikasi Kelapa Sawit

Certification is a compliance in implementing Best Management Practice (BMP) in oil palm plantation businesses. Participation in certification is influenced by several factors, including socio-economic, demographic, environmental, political and other factors. This study to see the effect of farmer types (independent and scheme farmers) and higher price expectations in implementing oil palm certification. Other determinants also studied included income, education, age, pesticide use, farming experience, and location. Through OLS regression analysis, it is known that the farmer types, higher price expectations, income, pesticide use, farming experience, and location are factors that influence the area of certified oil palm plantations.

Best Management Practices (BMPs): Perimeter Borders

This publication addresses perimeter borders as a best management practice. Written by Vivek Sharma, Kati W. Migliaccio, Brian Boman, Jemy Hinton, and Kevin Hancock, and published by the UF/IFAS Department of Agricultural and Biological Engineering, revised May 2021.

Developing a framework for sacred grove management using stakeholder analysis: evidence from sacred groves in Gujarat, India

PurposeThe purpose of this study is to use the framework of stakeholder analysis in a participatory democracy, used in forest management planning, for arriving at the best management option for selected sacred groves of Kachchh. This is achieved by accounting for economic, cultural and ecological values and the resulting outcomes in the complex institutional mechanism. Additionally, this study provides a framework for complex decision-making that characterizes the management of sacred groves involving multiple criteria and options accounting for multiple stakeholders that involve conflicting interests.Design/methodology/approachThe analytical hierarchy process was used to calculate the global priorities of management options using the relative importance of stakeholders, weights of different decision criteria to arrive at the best management practice for selected groves of Kachchh. The global priorities of management options rank management practices based on stakeholders' values and their effects on the choice of management strategy as well as on the potential to attain a compromise between competing interests. For this purpose, survey responses of 141 individuals belonging to seven different stakeholder categories were analyzed. Along with focus group discussions, and personal interviews, a stratified random sampling technique was used to survey respondents.FindingsBased on the global priority weights of the alternatives, it is determined that the restoration management option (guggal is restored by planting new guggal sapplings, cattle grazing is prohibited and high levels of ecosystem goods and services are provided) had the highest score, followed by the preservation management option (grazing is allowed on the periphery, juvenile guggal is preserved and moderate ecosystem goods and services are provided). Therefore, restoration of sacred groves is the best management practice of sacred groves in West Kachchh, offering a compromise between maximizing provision of ecosystem services and economic benefits in terms of allowing cattle grazing.Originality/valueThough there are several studies on best management practices for community-owned forests, irrigation systems, and pasture lands, and the role of local institutions in sustaining these common-pool resources; such studies for sacred groves are absent, despite sacred groves being one of the longest surviving common-pool resources that has sustained it over several decades. This is the first study that uses the framework of stakeholder analysis to arrive at the best management practice for sacred groves. The uniqueness of the study lies in a comprehensive evaluation of ecological–economic–cultural interests of multiple stakeholders toward management of sacred groves.

Spatial optimization of watershed best management practice scenarios based on boundary-adaptive configuration units

Spatial optimization of watershed best management practice (BMP) scenarios based on watershed modeling is an effective decision support tool for watershed management. During such optimization, existing types of BMP configuration units for configuring BMPs (or BMP configuration units, e.g. subbasins, hydrologic response units, farms) remain fixed boundaries once they have been created through spatial discretization prior to BMP scenario optimization. This sort of “boundary-fixed” method does not allow for adjustments to the spatial characteristics of BMP configuration units. Hence, it runs the risk of missing superior BMP scenarios that could have been obtained by adjusting unit boundaries and may produce less effective spatial optimization. In this article, we propose a new approach to the spatial optimization of BMP scenarios based on boundary-adaptive configuration units. The proposed optimization approach adopts slope positions (basic landform units along hillslopes inherently related to physical hillslope processes) as BMP configuration units and dynamically adjusts their boundaries by using quantitative information about their spatial gradation (i.e. fuzzy slope positions) during the optimization. A case study conducted in the Youwuzhen watershed in Fujian, China, showed that the proposed optimization approach can significantly enlarge the search space and obtain optimal BMP scenarios with better cost-effectiveness and higher optimization efficiency than with boundary-fixed units. The proposed optimization approach provides a new alternative framework for spatial optimization of BMP scenarios, in which other watershed models, intelligent optimization algorithms, and BMP configuration units available for boundary adjustment can be applied to BMP scenario optimization in a boundary-adaptive manner. This study also exemplifies the potential for transforming qualitative, vague, and empirical geographical knowledge about slope position units related to physical hillslope processes and BMPs into quantitative, explicit, and automated geospatial algorithms for effectively resolving environmental management problems in a more geographically meaningful way.