Afghanistan frequently faces drought and other climate change-related challenges due to rising temperatures and decreased precipitation in many areas of the country. Therefore, acquiring a thorough comprehension of the implications of climate change on the cultivation of key cereal crops is of the utmost importance. This is particularly significant in the context of Afghanistan, where the agricultural sector plays a pivotal role, contributing close to a quarter of the country’s national gross domestic product and serving as the primary source of employment for 70% of the rural workforce. In this paper, we employ a panel regression model to evaluate the relationship between climate change and cereal productivity in Afghanistan’s agro-climatic zones. Temperature had a significant negative impact, implying that a mean temperature increase of 1 °C decreased wheat and barley yields by 271 and 221 kg/ha, respectively. Future medium- and high-emission scenarios (RCP4.5 and RCP8.5, respectively) for the period 2021–2050 were considered for future yield predictions. To project future climate change impacts, the estimated panel data regression coefficients were used to compute the projected changes in cereal yields. During the period 2021–2050, the mean yield of wheat is projected to decrease by 21 or 28%, the rice yield will decrease by 4.92 or 6.10%, and the barley yield will decrease by 387 or 535 kg/ha in the RCP4.5 and RCP8.5 emission scenarios, respectively, further emphasizing the need for targeted actions to tackle the effects of climate change on agriculture in Afghanistan in alignment with SDG 2 (Zero Hunger) and SDG 13 (Climate Action).

Afghanistan is frequently affected by drought and other climate change related hazards due to increased temperature and reduced precipitation over most parts of the country. The present study was undertaken to analyze the climate change preparedness measures for adaptation and willingness to pay for crop insurance to mitigate the climate change impacts. This study was conducted in central agro-climatic zone of Afghanistan to collect the primary data through the random sampling technique with a total sample of 105 respondents from the seven provinces located in this zone. Likert type responses were used to assess the climate change adaptation and binary log it regression model was used to identify the factors affecting the willingness to pay for crop insurance to mitigate the climate change impacts on agriculture. The results of the study indicated that changing cropping pattern (74%) followed by receiving technical information from experts (70%) and keeping the land as fallow (60%) were rated as very high to high preparedness measures to mitigate the climate change impacts. Logit regression model revealed that the education and technical information received from experts were positively significant and their marginal effects implied that the probabilities of willing to pay for crop insurance were increased by 1.3 and 15.9 percent, respectively. The establishment of crop insurance agencies at province level may help reducing the impact of climate change and increasing the income and livelihood of the farmers.

Climate variability and extremes adversely affect the agricultural production system, food security, livestock sector, and water resources. With the cumulative effects of climate variability, there is a need to anticipate and develop appropriate adaptation strategies to cope with changing climatic conditions. It is necessary to study the adaptation strategies that are to be followed for climate change to examine the ability of vulnerable communities and people, frequently affected by drought and other climate-related risks, to adapt to climate change impacts. Hence, the present study examined the determinants of various climate change adaptation strategies followed by farmers as a measure to face climate variability, which will be ultimately beneficial and enlightening to policymakers to gain knowledge about the measures to be taken to mitigate the impact of climate change. The study was undertaken using data collected from 105 farm households with an organized pre-tested interview schedule in the central agro-climatic zone of Afghanistan. The multivariate probit econometric model was used to analyze the factors responsible for major adaptation strategies to mitigate the impact of climate change. The key findings of the model indicated that the probability of educated farmers migrating to the non-agricultural sector for employment has increased by 1.3 percent, and those who have more land area have adopted a reduction in irrigation by 5.2 percent as an adaptive mechanism. The study also found that having access to technical guidance from extension officials increased the likelihood of farmers changing their cropping pattern by 18.6 percent and of diversifying their farms by 19.2 percent. On the other hand, expert guidance reduced the likelihood of drilling new bore wells by 20.5 percentage points and decreased the probability of selling livestock by 10.8 percentage points. The results of the study provide policy insights to improve the ability of farmers to modify their practices through improvement in extension services, irrigation infrastructure facilities, watershed development, and climate-resilient agricultural systems.

The changes in frequency and intensity of rainfall, variation in temperature, increasing extreme weather events, and rising greenhouse gas emissions can together have a varying impact on food grain production, which then leads to significant impacts on food security in the future. The purpose of this study is to quantify how maize productivity might be affected due to climate change in Southern India. The present study examines how the projected changes to the northeast monsoon will affect maize yield in Tamil Nadu during the rabi season, which spans from September to December, by using a three-step methodology. Firstly, global climate models that accurately represent the large-scale features of the mean monsoon were chosen. Secondly, baseline and future climate data were extracted from the selected global models and the baseline data were compared with observations. Thirdly, the panel data regression model was fitted with the India Meteorological Department’s (IMD) observed climate data to generate the baseline coefficients and projected the maize production using future climate data generated from the global climate model. The Representative Concentration Pathways (RCPs) of RCP4.5 and RCP8.5 were used from two global climate model outputs, namely GFDL_CM3 and HadGEM2_CC, to predict the climate change variability on maize yields during the middle (2021–2050) and the end (2071–2100) of this century. The maize yield is predicted to increase by 3 to 5.47 per cent during the mid-century period and it varies from 7.25 to 14.53 per cent during the end of the century for the medium- (RCP4.5) and high-emission (RCP8.5) climate change scenarios. The maize grain yield increasing during the future periods indicated that the increase in rainfall and temperature during winter in Southern India reduced the possibility of a negative impact of temperature on the maize yield.

Tamil Nadu is one of the largest state with an area of 130.33 lakh haandwith the population of 6 percent of nation. Agriculture sector contributes 13 percent of state income. Nearly 56 percent of people depends on agriculture for their livelihood. In this scenario, the present study has been undertaken to analyse the agriculture growth as it determines the trend in terms of production, area and productivity. In this study, compound growth rate is used to measure the agricultural growth. The study was conducted based on secondary data.The data is analysed for the period of 50 years (1967-68 to 2017-18) for the important crops such as Rice, Maize, Cotton, Banana, Groundnut, Sorghum, Sugarcane, Oilseeds, Pulses, Vegetables, Fruits and Spices.The growth rates of the production of the major crops revealed that rice, maize, banana, pulses, vegetables and fruits were showing a positive growth rate. In contrast, crops such as cotton, groundnut, sorghum, oilseeds, and spices were showing negative trend. In recent decades, there is a vast change in the climate and other environmental factors which makes farmers to shift in their cropping pattern. Hence to analyse the changes crop diversification is calculated using the indices such as Herfindal Index and Modified Entropy Index (MEI). The estimates of indexes revealed that crop diversification is increased in over years.