Report of the special session of "Chesapeake Bay Ecological Forecasting: Moving Ecosystem Modeling from Research to Operation" of the 2010 Chesapeake Modeling Symposium

Moving ecosystem modeling from research to applications and operations has direct management relevance and will be integral to achieving the water quality and living resource goals of the 2010 Chesapeake Bay Executive Order. Yet despite decades of ecosystem modeling efforts of linking climate to water quality, plankton and fish, ecological models are rarely taken to the operational phase. In an effort to promote operational ecosystem modeling and ecological forecasting in Chesapeake Bay, a meeting was convened on this topic at the 2010 Chesapeake Modeling Symposium (May, 10-11). These presentations show that tremendous progress has been made over the last five years toward the development of operational ecological forecasting models, and that efforts in Chesapeake Bay are leading the way nationally. Ecological forecasts predict the impacts of chemical, biological, and physical changes on ecosystems, ecosystem components, and people. They have great potential to educate and inform not only ecosystem management, but also the outlook and opinion of the general public, for whom we manage coastal ecosystems. In the context of the Chesapeake Bay Executive Order, ecological forecasting can be used to identify favorable restoration sites, predict which sites and species will be viable under various climate scenarios, and predict the impact of a restoration project on water quality.

Farmer-scientist research partnerships for integrated agriculture-aquaculture

A realistic alternative to traditional technology development and transfer has been utilized by the International Center for Living Aquatic Resources Management (ICLARM) to integrate pond fish culture into low-input farming systems in Malawi. Resource mapping was used to assess farm resources and constraints and introduce the concept of integrated resource management (IRM), the synergistic movement of resources between and among farm and household enterprises. Farmer-led IRM research projects are conducted on-farm and monitored by researchers through direct observation and on-station simulation of constraints and management practices. Technology-adoption rates by farmers involved in a pilot activity was 65% compared to 0% by farmers exposed only to top-down extension approaches. Within two years of adoption, every participating farmer had transferred the technology to an average of four other farmers without the involvement of the extension services.

Integrated aquaculture-agriculture adoption and impact in rainfed farming systems in Sakata, Malawi

ICLARM introduced integrated aquaculture-agriculture (IAA) in Sakata, Malawi three years ago. Since that time, and without extension support, the number of farmers with ponds increased from 4 in 1993/94 to 12 in 1995/96. To learn why and how IAA is spreading, a study of impact and adoption was conducted in the 1995/96 production season. Interviews were conducted with farmers to discuss lAA and collect data on farm function through the use of bioresource flow diagrams. Motivations given by farmers as to why they adopted IAA were to improve household nutrition and income. Constraints to adoption identified by farmers were availability of labor and capital to purchase inputs

Agriculture, irrigation and poverty reduction in Cambodia: Policy narratives and ground realities compared

This report is a contribution to an assessment of the current status of agriculture in Cambodia, focusing on the linkages between agriculture and water, mainly in the form of irrigation. It seeks to view current government policies on agriculture and irrigation in the context of experiences on the ground, as communicated through the many field studies that cover varied aspects of performance in the agriculture sector and irrigation schemes. In an effort to identify future research areas, this review examines the status quo, and connects or disconnects with stated policy through a broad lens to capture strengths and challenges across crop production, irrigation management and post-harvest contexts. It places irrigation under scrutiny in terms of its value as a major area of government expenditure in recent years, and asks whether it presents the best potential for future gains in productivity, when compared with the prospects offered by investments in other aspects of agriculture. The fieldwork and review of current literature that form the basis of this report were undertaken at the request of, and partly funded by, the Australian Centre for International Agricultural Research (ACIAR). It is also intended to contribute knowledge to the CGIAR Research Program on Aquatic Agricultural Systems (AAS) led by WorldFish, who co-funded the activities.

Report on Uganda Census of Agriculture vol. 1

The Census has been undertaken by the Department of Agriculture in conjunction with the Statistics Division of the Ministry of Planning and Community Development with technical assistance, in the form of experts and equipment, from the Food and Agriculture Organisation of the United Nations.

Forecasting high-frequency futures returns using online langevin dynamics

Forecasting the returns of assets at high frequency is the key challenge for high-frequency algorithmic trading strategies. In this paper, we propose a jump-diffusion model for asset price movements that models price and its trend and allows a momentum strategy to be developed. Conditional on jump times, we derive closed-form transition densities for this model. We show how this allows us to extract a trend from high-frequency finance data by using a Rao-Blackwellized variable rate particle filter to filter incoming price data. Our results show that even in the presence of transaction costs our algorithm can achieve a Sharpe ratio above 1 when applied across a portfolio of 75 futures contracts at high frequency. © 2011 IEEE.

Forecasting Daily Chlorophyll a Concentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network

A recurrent artificial neural network was used for 0-and 7-days-ahead forecasting of daily spring phytoplankton bloom dynamics in Xiangxi Bay of Three-Gorges Reservoir with meteorological, hydrological, and limnological parameters as input variables. Daily data from the depth of 0.5 m was used to train the model, and data from the depth of 2.0 m was used to validate the calibrated model. The trained model achieved reasonable accuracy in predicting the daily dynamics of chlorophyll a both in 0-and 7-days-ahead forecasting. In 0-day-ahead forecasting, the R-2 values of observed and predicted data were 0.85 for training and 0.89 for validating. In 7-days-ahead forecasting, the R-2 values of training and validating were 0.68 and 0.66, respectively. Sensitivity analysis indicated that most ecological relationships between chlorophyll a and input environmental variables in 0-and 7-days-ahead models were reasonable. In the 0-day model, Secchi depth, water temperature, and dissolved silicate were the most important factors influencing the daily dynamics of chlorophyll a. And in 7-days-ahead predicting model, chlorophyll a was sensitive to most environmental variables except water level, DO, and NH3N.