Managing the declining yield of non-food crops has opened new strategic challenges amidst global uncertainties. The COVID-19 scenario has increased awareness of natural lifestyle and eco-friendly products, largely dependent on non-food crop material. This strategic shift requires moving beyond traditional farm practices to improve agricultural production efficiency, and developing countries in particular have shown a consistent loss in their self-sufficiency of industrial crops despite being major exporters of non-food crop materials. However, existing studies analyze production efficiencies of non-food crops from general or theoretical aspects often by virtual estimates from breaking down the multiple factors of crop productivity. This study examined multiple factors of crop production to identify “which crop inputs have been inefficiently used overtime” by tracking efficiency changes and various input issues in overall cotton production from practical aspects, i.e., scaling non-constant returns of those multiple factors would allow for the violation of various situations. Accordingly, a stochastic frontier approach was employed to measure the production frontier and efficiency relationship using time-series data of Pakistan’s cotton production from 1971–2018—a specific non-food crop perspective from a top-ranked cotton-producing country that has recently been shifted towards being a non-exporter of cotton due to low yield. The coefficient of area, seed, and labor indicates the positive relationship with cotton production, while fertilizer, irrigation, electricity, and machinery are statistically negative. This implies that policymakers need priority-based strategies for the judicial use of synthetic fertilizers, irrigation, a subsidy policy, and technology adoption, which could significantly improve the efficiencies of cotton productivity from the same land resources. Being adaptable to other developing economies, the analysis would strategically facilitate designing and developing affordable technology-driven solutions and their customized extensions towards sustainable non-food crop production practices and Agri-Resources efficiencies.
Pakistan is the primary producer of cotton, which is an indispensable crop worldwide. The agriculture sector depends on the climate and may be susceptible to future climate changes, such as increasing temperature, heavy rainfall, droughts, and floods directly impacting cotton productivity. This study empirically investigates the relationship between climate change variables and non-climate change variables on cotton productivity in Pakistan. An econometric technique, the “autoregressive distributed lag model” (ARDL), was employed on time series data from 1970 to 2018 to explore the existence and nature of the relationship among variables. The findings indicated the presence of co-integration among variables which confirms the long-run relationship among the variables. At the same time, the empirical results revealed that increases in temperature and rainfall positively affected cotton productivity. However, CO2 harms cotton productivity. Moreover, infrastructural changes positively affect cotton productivity in both the long and short run, while labor is negatively related to productivity. The area, fertilizer, and seed consumption showed a significant positive effect on cotton productivity. We employed the dynamic ordinary least squares (OLS), co-integration regression estimation, and the series test to validate the robustness of the finding. The finding of this study urges policymakers to devise a comprehensive policy to mitigate the adverse effect of climate change and upsurge water conservation. Furthermore, it is imperative to adopt environmentally friendly production inputs and modern techniques, which helps to gain sustainable cotton productivity. To conclude, the cotton crop is significantly affected by climate change subject to the region. Although this study analyzed the Pakistan case, the model can be generalized to all the developing countries
Pastoral areas are the key difficulty in China’s pursuit of common prosperity and a key region for China to build the northern ecological safety barrier and to realize the Two Centenary Goals. It is of great significance to scientifically evaluate the quality of rural life (QRL), measure the relative poverty level (RPL), and identify the relatively poor areas, making it possible to dock poverty elimination with rural revitalization. Based on the socio-economic data of 18 pastoral areas in Inner Mongolia, this paper draws on spatial layout theory to evaluate QRL and measures RPL by the natural breakpoint method and then identifies the relatively poor areas in Inner Mongolia. The results show that (1) the QRLs of pastoral areas in Inner Mongolia were unbalanced and highly polarized. The mean score of QRLs was 0.2598. Eleven (61.11%) of the counties/banners had a QRL smaller than the mean score. On the spatial layout of QRLs, the western areas were stronger than the central areas. High QRL counties/banners are mainly concentrated in the western region. In the central region, the QRLs were very fragmented, falling onto all five levels. (2) The pastoral areas in Inner Mongolia differed significantly in RPL. The mean score of RPL stood at 0.3788. Nine counties/banners (50%) had an RPL greater than the mean. Contrary to the spatial layout features of QRLs, the central pastoral areas in Inner Mongolia had stronger RPLs than the eastern ones. High RPL counties/banners are mostly clustered in the central region. The spatial layout of RPLs is relatively reasonable in the central region: the RPLs decreased gradually from Dorbod Banner. (3) Nearly 45% of the pastoral areas in central and western Inner Mongolia face serious relative poverty and a high risk of returning to poverty. Eight counties/banners (45%) were identified as high composite relative poverty areas. From spatial layout, the composite relatively poor counties/banners clustered clearly, mainly in the western region. Finally, this paper establishes a warning mechanism against large-scale returning to poverty, aiming to lower composite RPL. The research results provide empirical reference and implementation path for consolidating the results of poverty eradication and facilitating rural revitalization.
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