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Farmers' adaptation strategies in agricultural production are required to minimise the negative impact of climate change on a nation's food production in developing countries. Based on the panel data of the provincial level in China from 2000 to 2017, this study aims to analyse the changing climate over recent years and farmers' adaptation strategy in terms of cropping in agricultural production.

This study uses Simpson's diversity index (SDI) to measure the degree of crop diversity planted by farmers and evaluate the influence of climate change on farmers' cropping strategy using the fixed-effect model. Further, the authors estimate the impact of farmers' cropping strategy on their economic performances in two aspects including yields and technical efficiency of crops.

The empirical results show that the overall climate appears a warming trend. Different from farmers in some other countries, Chinese farmers tend to adopt a more specialised cropping strategy which can significantly improve the technical efficiency and yields of crops in agriculture. In addition, as a moderating role, the specialised cropping can help farmers to alleviate the negative impact of climate change on technical efficiency of their crops.

First, previous studies showed that the changing climate influenced farmers' adaptation strategies, while most studies focussed on multiple adaptation strategies from the farm-level perspective rather than cropping strategy from the nation-level perspective. Second, the present study investigates how the cropping strategy affects the economic performance (in terms of the technical efficiency and crop yields) of agricultural production. Third, the stochastic frontier analysis method is used to estimate the technical efficiency. Fourth, this study explores the moderating effect between farmers' cropping strategy and technical efficiency by introducing an interaction item of SDI and accumulated temperature.

The purpose of this study is to develop a methodology which amalgamates quantitative and qualitative approaches to determine the best placement of mobile logistics hubs (MLH) to be established in different parts of Nepal as a part of real-life project, “Augmentation of National and Local-Level Emergency Logistics Preparedness in Nepal” (2017–2020), implemented by the World Food Programme in cooperation with the Government of Nepal.

The study develops a methodology using a combination of a modified version of the maximal covering location problem (MCLP) and focus group discussion. The MCLP model is used to determine the optimal number and spatial location of MLHs, and focus group discussion is used to identify the five first-priority strategic MLH locations using expert knowledge.

The authors identify the five first-priority locations for establishing MLHs using an amalgamation of quantitative approach (mathematical model) and qualitative approach (focus group discussion). By amalgamating mathematical model with expert knowledge, findings acceptable to a wide range of stakeholders are obtained. The focus group discussion helps to pinpoint the location of MLHs to city-level granularity which is otherwise impossible with data available on hand.

Although multiple experts’ judgements were obtained via focus group discussion, subjectivity and possible bias is inevitable. Overall, the quantitative results of the study are purely based on the data available during the study period; therefore, having updated data could possibly improve the quality of the results.

This study is the first of its kind that uses an amalgamation of mathematical model and expert knowledge to determine the strategic locations of MLHs and has been successful to an extent that the selected locations have been vetted by the government of Nepal for establishing MLHs and are undergoing implementation in real life. This study also considers multiple disaster scenarios and employs the concepts of human development, disaster risk and transportation accessibility to reflect Nepal's socioeconomic, geo-climatic and topographical features.