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This study is the first effort to assess the long-term effects of change in climate on crop production in South Asian countries from 1991 to 2016 by employing the second-generation methods robust to cross-sectional dependence and slope heterogeneity. Using panel data with the panel dynamic least squares (PD-LS) method and several co-integration approaches, this study confirmed a long-term co-integration among considered variables. The long-run estimates revealed that climatic variables, including temperature and CO2 emissions, negatively affected crop production, suggesting that a 1% increase in temperature and CO2 emissions reduces crop production by 1.93% and 0.32%, respectively. Conversely, precipitation positively affects crop production in the long-run, indicating that a 1% surge in precipitation increases crop production by 0.52%. Moreover, non-climatic variables including cultivated area, income level, and financial development positively affected crop production in the long-run. Furthermore, a 1% surge in cultivated area, income level, and financial development enhances crop production by 0.29%, 0.13%, and 0.04%, respectively. The outcomes of the Dumitrescu-Hurlin causality test confirmed that the causal link among all variables is significant, and outcomes verify the previous findings. This study suggests that urgent attention should be given to various adaptation strategies, such as credit supply, cropped area expansion, irrigation infrastructure enhancement, and the introduction of improved kinds of major food crops to enhance agricultural productivity. Besides, agricultural extension authorities should provide information about climate change to farming communities to deal with the adverse impacts of sudden climate changes on productivity. [ABSTRACT FROM AUTHOR]

This study aims to examine the impact of financial development on agricultural production in the case of China. The study used country level time‐series data for the period 1989 to 2016. We applied the autoregressive distributed lag (ARDL) approach to investigate the long‐term cointegration relationship between underlying variables and the fully modified ordinary least squares (FMOLS) for robustness check. Results of this study confirm the existence of a long‐term relationship among the variables. The ARDL estimation results reveal that financial development has a significantly positive impact on agricultural production in both long‐run and short‐run. The fully modified OLS confirm the robustness of the findings. This study suggests that the Chinese government should pay more attention on long‐run policies to enhance agricultural growth through improving banking sectors, efficient rural credit markets, and increasing township banking infrastructures in the country. [ABSTRACT FROM AUTHOR]

Water and land both are limited resources. Current management strategies are facing multiple challenges to meet food security of an increasing population in numerous South Asian countries, including Pakistan. The study of land cover/land use changes (LCLUC) and land surface temperature (LST) is important as both provide critical information for policymaking of natural resources. We spatially examined LCLU and LST changes in district Multan, Pakistan, and its impacts on vegetation cover and water during 1988 to 2017. The LCLUC indicate that rice and sugarcane had less volatility of change in comparison with both cotton and wheat. Producer's accuracy (PA) is the map accuracy (the producer of map), but user's accuracy (UA) is the accuracy from the point of view of a map user, not the map maker. Average overall producer's and user's accuracy for the region was 85.7% and 87.7% for Rabi (winter) and Kharif (summer) seasons, respectively. The results of this study showed that 'built-up area' increased with 7.2% of all the classes during 1988 to 2017 in the Multan district. Anthropogenic activities decreased the vegetation, leading to an increase in LST in study area. Changes on LCLU and LST during the last 30 years have shown that vegetation pattern has changed and temperature has increased in the Multan district. [ABSTRACT FROM AUTHOR]

Unbalanced climate during the last decades has created spatially alarming and destructive situations in the world. Anomalies in temperature and precipitation enhance the risks for crop production in large agricultural region (especially the Southern Punjab) of Pakistan. Detailed analysis of historic weather data (1980-2011) record helped in creating baseline data to compare with model projection (SimCLIM) for regional level. Ensemble of 40 GCMs used for climatic projections with greenhouse gas (GHG) representative concentration pathways (RCP-4.5, 6.0, 8.5) was selected on the baseline comparison and used for 2025 and 2050 climate projection. Precipitation projected by ensemble and regional weather observatory at baseline showed highly unpredictable nature while both temperature extremes showed 95 % confidence level on a monthly projection. Percentage change in precipitation projected by model with RCP-4.5, RCP-6.0, and RCP-8.5 showed uncertainty 3.3 to 5.6 %, 2.9 to 5.2 %, and 3.6 to 7.9 % for 2025 and 2050, respectively. Percentage change of minimum temperature from base temperature showed that 5.1, 4.7, and 5.8 % for 2025 and 9.0, 8.1, and 12.0 % increase for projection year 2050 with RCP-4.5, 6.0, and 8.5 and maximum temperature 2.7, 2.5, and 3.0 % for 2025 and 4.7, 4.4, and 6.4 % for 2050 will be increased with RCP-4.5, 6.0, and 8.5, respectively. Uneven increase in precipitation and asymmetric increase in temperature extremes in future would also increase the risk associated with management of climatic uncertainties. Future climate projection will enable us for better risk management decisions. [ABSTRACT FROM AUTHOR]