経済学および管理科学の国際ジャーナル

Precision agriculture has revolutionized farming practices worldwide, offering advanced technologies and data-driven approaches to optimize crop production, minimize resource utilization, and enhance environmental sustainability. In China, a rapidly evolving agricultural landscape coupled with technological advancements has spurred the adoption of precision agriculture. This mini-review explores the development trends and management strategies of precision agriculture in China, with a focus on insights derived from data visualization techniques.

Production and productivity can be boosted either through increased use of inputs or by improving the efficiency of producers. The opportunities to increase farm production by bringing additional physical resource into cultivation have been diminishing. Then, reducing the existing inefficiency among farmers can be more effective. The serious reliance on obsolete farming techniques, poor complementary services such as extension, credit, marketing, infrastructure and poor and biased agricultural policies are among the major factors that have greatly constrained the development of Ethiopia's agriculture. This study tried to analyze the technical, allocative and economic efficiencies of common bean producer farmers in Burji district. It also identified the factors affecting the efficiency of producers in the study area. A multi-stage sampling technique was used to select 313 sample household farmers who were interviewed using a structured questionnaire to obtain data pertaining to common bean production during 2020. In the analysis, frontier 4.1 software was used to determine the levels of technical and economic efficiencies. Thus, the mean technical, allocative and economic efficiencies were 63.7, 77.2 and 50.0 percent respectively. Furthermore, descriptive statistics, stochastic frontier and two-limit Tobit regression models were employed. It was established from a stochastic frontier model that common bean yield estimated using Cobb- Douglas production function was positively influenced by seed, labor, oxen, land size, chemical and fertilizer (DAP). Similarly, a Tobit model revealed economic efficiencies was affected positively and significantly by family size, education, land size, TLU, access to credit, extension contact, training and off/none income. While variables such as crop pest affected negatively. Education, TLU, sex, access to credit, training, and off/none farm income influenced allocative efficiency positively and crop pest affected negatively. Finally, technical efficiency was affected age, family size, education, land size, sex, TLU, extension contact, training and off/none farm income affected technical efficiency positively while, distance from nearest market and crop pest affected it negatively. Thus, the study recommended policies targeting development of markets, roads and education of smallholder common bean producers that would promote economic efficiency of common bean producers.