This study applied livelihood vulnerability index (LVI) and livelihood effect index (LEI) to assess vulnerability from climate variability and change of three agricultural and natural resources dependent commune in northwest Vietnam, a country that is expected to bear some of the most severe impacts of climate change. Based on a survey of 335 farm household data, complemented with secondary data on climate factors, a composite index was calculated and differential vulnerabilities were compared. The results of the analysis suggest that one of the communities, “Pa Vay Su,” was more vulnerable than the others, particularly in relation to housing, knowledge and skills, socio-demographics, health and water security, social networks, and livelihood strategy. “Hien Luong” commune, on the other hand, was more vulnerable in relation to other LVI indicators with the exception of food security, climate variability, and natural disasters. “Moc Chau” community was more vulnerable in relation to water security, social demographic than Hien Luong commune. Overall, the article shows that three different vulnerability assessment indices can be broadly applied in comparable setting in other areas of country and they could usefully establish the basis for a nationally applicable index to identify and prioritize adaptation and mitigation needs.

Purpose: This study aims to examine the extent to which farmers are aware of climate change and how they have modified their growing practices in response to perceived climate changes.Design/methodology/approach: A logit model was used to explore farmers’ awareness and a binary logistic model was used to analyze their adaptive responses. Data from 335 farm households were collected from three provinces of Northwest Vietnam with different climate change vulnerability.Findings: Farmers’ awareness of climate change was related significantly to household and farm characteristics. Farm experience, education level, location, tenancy status, soil fertility, access to credit, climate information, agricultural extension services, farmer groups, non-agriculture income, distance to market and house and climate change experience influence adaptation measure choices.Research limitations/implications: These findings suggest that investment strategies must promote adaptation to climate change by supporting technological and institutional methods, such as education, markets, credit and information.Originality/value: This study is the first study that uses econometric models to analyze farmers’ perception effect and adaptation to climate change aspect in Northwest Vietnam

Since the reform and opening up, regional imbalances in the development of market economy and urbanization have significantly changed the spatial agglomeration pattern of grain production (GP) in China. To characterize GP by the yield, we used the gravity center (GC) and standard deviation ellipse (SDE) to investigate the evolution characteristics of the spatial agglomeration pattern of GP and its three major crops with wheat, maize and rice in China, then establish a spatial econometric model to explore the natural and socioeconomic drivers of GP at the national level, at different crops and at different regions. The research results indicate that the GC of GP gradually shifts and expands to the northeast and presents a spatial distribution pattern in a “northeast–southwest” direction. The GC of wheat production mainly expands upwards in the east–west direction. The GC of maize production shifts in a direction that is more consistent with grain production, and there is an expansion trend in the east–west direction, while the GC of rice production has the largest north–south span, and continues to expand upward to the north by the east. The status of grain production in Northeast China and Northwest China is rising and the importance of grain production in the Southeast coastal areas are decreasing. The importance of wheat and corn production in North China continues to strengthen, and Northeast China is becoming more important for rice production, but the middle and lower reaches of the Yangtze River are still important rice producing regions. Changes in GP with significant spatial dependence are jointly affected by many factors, such as natural and socio-economic factors, and there are obvious differences among different food crops and different division regions, with the most prominent positive effects being the multiple crop index (MCI), arable land per capita (AL) and agricultural mechanization (MECH), while economic growth and urbanization are significantly negative.

The present study applies a bootstrapped data envelopment analysis (DEA) procedure to compute bias-corrected measures of agricultural total factor productivity (TFP) change and its components (technical change and technical efficiency change) using a panel data of 19 regions of Bangladesh covering a 23-year period (1987–2009), thereby overcoming the limitation of the lack of statistical inference of the conventional non-parametric DEA. Results revealed that overall productivity grew at a modest rate of 0.03%, mainly powered by technological progress at 0.03% and a negligible decline in technical efficiency at 0.004% with large disparities amongst regions. Six regions in the middle order shifted ranks with regard to TFP change following bias correction. The estimated confidence intervals demonstrated that many regions underwent either progress or regress in productivity performance over time. Investments in research and development (R&D), agricultural extension, and crop diversification are suggested to improve regional inequality and declining technical efficiency.