Considerations for determining research priorities: learning cycles and impact pathways

Agricultural researchers identify and apply new science, novel approaches and innovations that could generate research breakthroughs and improve impacts to support the development of the agricultural sector. During the past few decades, there has been an expansion of the research agenda along the entire research-fordevelopment continuum, with farm- and policy-level implications. The goals and objectives of research have broadened from primarily food production to include sustainable resource management, equity, gender, health, and environmental concern

CHANGES IN THE CROP PRODUCTION AND THEIR SOURCES IN BANGLADESH AGRICULTURE

The magnitude of changes in average crop production and the sources of changes were measured for six crops- Aus rice, Aman rice, Boro rice , jute, wheat and sugarcane between two time periods pre-modern technology adoption period and modern technology (MT) adoption period. Except for wheat, these periods were defined as 1947/48 -1967/68 and 1968/69- 1986/87 respectively. For wheat, these periods were defined as 1947/48-1971/72 and 1972/73 -1986/87 respectively. The analysis shows that production, area and yield increased at the national level for all the crops except jute. Although area under jute increased, its production and yield declined in the second period. Change in mean area was the main source of mean production change in sugarcane, Aus rice and Boro rice. But yield was the main source of increase in Aman rice production and decrease in jute production. For wheat, interaction between changes in mean area and mean yield was the principal source of change in production. And yield of all these crops except jute increased due to the adoption of modern technology (MT). Finally, some policy conclusions are drawn with respect to an improvement of production levels of the major crops

A Distance Metric for Evolutionary Many-Objective Optimization Algorithms Using User-Preferences

Abstract. In this paper we propose to use a distance metric based on user-preferences to efficiently find solutions for many-objective problems. In a user-preference based algorithm a decision maker indicates regions of the objective-space of interest, the algorithm then concentrates only on those regions to find solutions. Existing user-preference based evolutionary many-objective algorithms rely on the use of dominance comparisons to explore the search-space. Unfortunately, this is ineffective and computationally expensive for many-objective problems. The proposed distance metric allows an evolutionary many-objective algorithm’s search to be focused on the preferred regions, saving substantial computational cost. We demonstrate how to incorporate the proposed distance metric with a user-preference based genetic algorithm, which implements the reference point and light beam search methods. Experimental results suggest that the distance metric based algorithm is effective and efficient, especially for difficult many-objective problems. Keywords: Distance metric, User-preference, Many-objective optimization, Multi-objective optimization, Reference point, Light beam search

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Technical, Allocative, Cost and Scale Efficiencies in Bangladesh Rice Cultivation: A Non-parametric Approach

Applying programming techniques to detailed data for 406 rice farms in 21 villages, for 1997, produces inefficiency measures, which differ substantially from the results of simple yield and unit cost measures. For the Boro (dry) season, mean technical efficiency was efficiency was 56.2 per cent and 69.4 per cent, allocative efficiency was 81.3 per cent, cost efficiency was 56.2 per cent and scale efficiency 94.9 per cent. The Aman (wet) season results are similar, but a few points lower. Allocative inefficiency is due to overuse of labour, suggesting population pressure, and of fertiliser, where recommended rates may warrant revision. Second-stage regressions show that large families are more inefficient, whereas farmers with better access to input markets, and those who do less off-farm work, tend to be more efficient. The information on the sources of inter-farm performance differentials could be used by the extension agents to help inefficient farmers. There is little excuse for such sub-optimal use of survey data, which are often collected at substantial costs