Harnessing Wisdom for Managing Watersheds: Honey Bee Perspective on Innovations, Institutions and Policies for Marginal Environments

Participatory approaches for watershed management are now considered essential for sustainable natural resources management and yet there is very little opportunity for intellectual participation by the people. This requires understanding of the local knowledge systems and their institutional context. In this paper, we provide an overview of the conceptual framework which can facilitate such participation. The full report being published separately includes case studies of farmers’ innovations in natural resources management.

Improved weed management for transplanted aman rice

Rainfed transplanted rice grown in the monsoon aman season accounts for more than 50% of the total area planted to rice in Bangladesh. Because of rising input costs, including labor, farmers are searching for ways to maintain income, by either increasing yields or reducing costs or both. On-farm trials in the High Barind Tract indicated that one-third of the farmers would be able to gain 0.5 t ha–1 or more additional grain by undertaking more intensive or timelier weeding than is usual under current management practices. Higher yields were observed on-farm from a preemergence application of butachlor(1.25 kg a.i. ha–1) compared with hand weeding twice. In Comilla District, trials of a range of weed management practices demonstrated that the yield advantage over the farmers’ practice, either one or two hand weedings, was on average 355 ± 18 kg ha–1 for Rifit (pretilachlor), 281 ± 39 kg ha–1 for Machete (butachlor), and 210 ± 34 kg ha–1 for Ronstar (oxadiazon), each followed by one hand weeding in aman 2003. Partial budgets calculated for inputs and returns showed that hand weeding was less profitable than herbicides in rainfed rice,incurring US$72 ha–1 lower return. Use of a push weeder plus one hand weeding was less profitable than herbicides, incurring$49 ha–1 lower return. To date, herbicides have been largely promoted for irrigated rice in Bangladesh. The trial results demonstrate that under rainfed conditions early in the aman season, water levels are adequate for herbicides to work effectively. The use of herbicides allows timely weed control when there is a shortage of labor and avoids transaction costs, such as the provision of meals and time needed to source laborers. Herbicides are likely to be adopted by growers experiencing labor shortages, particularly on large farms and for farmers seeking to reduce input costs. Sharecroppers and tenant farmers who pay rent are primarily concerned about obtaining a high aman yield, so innovations that raise aman yields (such as herbicides that will have a similar effect as a timely first weeding) are also likely to be adopted on sharecropped plots, even when costs are not shared between the landlord and tenant

Learning Agent for a Service-Oriented Context-Aware Recommender System in Heterogeneous Environment

Traditional recommender systems provide users with customized recommendations of products or services. They employ various technologies and algorithms in order to search and select the best options available while taking into account the user's context. Increasingly often, such systems run on devices in heterogeneous environments (including mobile devices) making use of their functionalities: various sensors (e.g. movement, light), wireless data transmission technologies and positioning systems (e.g. GPS) among others. In this paper, we propose an innovative recommender system that determines the best service (including photo and movie conversion) and simultaneously accommodates the context of the device in a heterogeneous environment. The system allows the choice between various service providers that make their resources available using cloud computing as well as having the services performed locally. In order to determine the best possible recommendation for users, we employ the concept of learning agents, which has not been thoroughly researched in connection with recommender systems so far

Developing a diagnostic heuristic for integrated sugarcane supply and processing systems.

Doctoral Degrees. University of KwaZulu-Natal, Pietermaritzburg.Innovation is a valuable asset that gives supply chains a competitive edge. Moreover, the adoption of innovative research recommendations in agricultural value chains and integrated sugarcane supply and processing systems (ISSPS) in particular has been relatively slow when compared with other industries such as electronics and automotive. The slow adoption is attributed to the complex, multidimensional nature of ISSPS and the perceived lack of a holistic approach when dealing with certain issues. Most of the interventions into ISSPS often view the system as characterised by tame problems hence, the widespread application of traditional operations research approaches. Integrated sugarcane supply and processing systems are, nonetheless, also characterised by wicked problems. Interventions into such contexts should therefore, embrace tame and/or wicked issues. Systemic approaches are important and have in the past identified several system-scale opportunities within ISSPS. Such interventions are multidisciplinary and employ a range of methodologies spanning across paradigms. The large number of methodologies available, however, makes choosing the right method or a combination thereof difficult. In this context, a novel overarching diagnostic heuristic for ISSPS was developed in this research. The heuristic will be used todiagnose relatively small, but pertinent ISSPS constraints and opportunities. The heuristic includes a causal model that determines and ranks linkages between the many domains that govern integrated agricultural supply and processing systems (IASPS) viz. biophysical, collaboration, culture, economics, environment, future strategy, information sharing, political forces, and structures. Furthermore, a diagnostic toolkit based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was developed. The toolkit comprises a diagnostic criteria and a suite of systemic tools. The toolkit, in addition, determines thesuitability of each tool to diagnose any of the IASPS domains. Overall, the diagnostic criteria include accessibility, interactiveness, transparency, iterativeness, feedback, cause-and-effect logic, and time delays. The tools considered for the toolkit were current reality trees, fuzzy cognitive maps (FCMs), network analysis approaches, rich pictures (RP), stock and flow diagrams, cause and effect diagrams (CEDs), and causal loop diagrams (CLDs). Results from the causal model indicate that collaboration, structure and information sharing had a high direct leverage over the other domains as these were associated with a larger number of linkages. Collaboration and structure further provided dynamic leverage as these were also part of feedback loops. Political forces and the culture domain in contrast, provided lowleverage as these domains were only directly linked to collaboration. It was further revealed that each tool provides a different facet to complexity hence, the need for methodological pluralism. All the tools except RP could be applied, to a certain extent, across both appreciation and analysis criteria. Rich pictures do not have causal analysis capabilities viz. cause-and-effect logic, time delays and feedback. Stock and flow diagrams and CLDs conversely, met all criteria. All the diagnostic tools in the toolkit could be used across all the system domains except for FCMs. Fuzzy cognitive maps are explicitly subjective and their contribution lies outside the objective world. Caution should therefore be practiced when FCMs areapplied within the biophysical domain. The heuristic is only an aid to decision making. The decision to select a tool or a combination thereof remains with the user(s). Even though the heuristic was demonstrated at Mhlume sugarcane milling area, it is recommended that other areas be considered for future research. The heuristic itself should continuously be updated with criteria, tools and other domain dimensions

From "best practice" to "best fit": a framework for designing and analyzing pluralistic agricultural advisory services worldwide

"The paper develops a framework for the design and analysis of pluralistic agricultural advisory services and reviews research methods from different disciplines that can be used when applying the framework. Agricultural advisory services are defined in the paper as the entire set of organizations that support and facilitate people engaged in agricultural production to solve problems and to obtain information, skills and technologies to improve their livelihoods and well-being... To classify pluralistic agricultural advisory services, the paper distinguishes between organizations from the public, the private and the third sector that can be involved in (a) providing and (b) financing of agricultural advisory services. The framework for analyzing pluralistic agricultural advisory services presented in the paper addresses the need for analytical approaches that help policy-makers to identify those reform options that best fit country-specific frame conditions. Thus, the paper supports a shift from a “one-size-fits-all” to a “best fit” approach in the reform of public services... Based on a review of the literature, the paper presents a variety of quantitative and qualitative methodological approaches derived from different disciplines that can be applied when using the framework in empirical research projects. The disciplines include agricultural and institutional economics, communication theory, adult education, and public administration and management. The paper intends to inform researchers as well as practitioners, policy-makers and development partners who are interested in supporting evidence-based reform of agricultural advisory services. from Authors' AbstractAgricultural extension work, Pro-poor growth, Capacity strengthening,

Technology Adoption in Poorly Specified Environments

This article extends the characteristics-based choice framework of technology adoption to account for decisions taken by boundedly-rational individuals in environments where traits are not fully observed. It is applied to an agricultural setting and introduces the concept of ambiguity in the agricultural technology adoption literature by relaxing strict informational and cognition related assumptions that are implied by traditional Bayesian analysis. The main results confirm that ambiguity increases as local conditions become less homogeneous and as computational ability, own experience and nearby adoption rates decrease. Measurement biases associated with full rationality assumptions are found to increase when decision makers have low computational ability, low experience and when their farming conditions differ widely from average adopter ones. A complementary empirical paper (Useche 2006) finds that models assuming low confidence in observed data, ambiguity and pessimistic expectations about traits predict sample shares better than models which assume that farmers do not face ambiguity or are optimistic about the traits of new varieties.Research and Development/Tech Change/Emerging Technologies,