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Changing climate has increasingly become a challenge for smallholder farmers. Identification of technical, institutional and policy interventions as coping and adaptation strategies and exploring risks of their adoption for smallholder farms are the important areas to consider. The aim of the present study was to carry out an in-depth analysis of adaptation strategies followed and the associated risk premium in technology adoption.

The study was carried out in the dryland systems of three Indian states – Andhra Pradesh, Karnataka and Rajasthan – and was based on a survey of 1,019 households in 2013. The flexible moment-based approach was used for estimating the stochastic production function, which allowed estimation of the relative risk premium that farmers are willing to pay while adopting the technologies to avoid crop production risks.

In all the three states, the risk premium (INR ha⁻¹) was higher for farm mechanization compared to supplemental irrigation, except in the case of Andhra Pradesh. The higher the level of technology adoption, the higher the risk premium that households have to pay. This can be estimated by the higher investment needed to build infrastructure for farm mechanization and supplemental irrigation in the regions. The key determinants of technology adoption in the context of smallholder farmers were climatic shocks, investment in farm infrastructure, location of the farm, farm size, household health status, level of education, married years, expected profit and livestock ownership.

Quantification of the risk premium in technology adoption and conducting associated awareness programs for farmers and decision-makers are important to strengthen evidence-based adoption decisions in the dryland systems of India.

The main purposes of this paper were to assess effects of smallholder farmers access to livelihood capital (e.g. land, livestock and water) on livestock water productivity (LWP) and to evaluate impacts of selected interventions in reducing livestock water demand (per unit of livestock product) and therefore increasing LWP.

A total of 203 sample farm households were selected in intensive and semi‐intensive crop‐livestock systems of Indo‐Ganga basin of India. A household survey was undertaken to capture data on land, water and livestock management. For the analysis, sample farms were clustered into poor, medium, better‐off. LWP is estimated as a ratio of livestock beneficial‐outputs (e.g. milk) to depleted‐water (i.e. evapotranspired water to produce livestock feed). Impacts of selected interventions, on LWP, were analyzed using scenarios developed on a spread sheet model.

The results showed different LWP values among farm‐clusters and levels of intensification. The intensive systems showed higher LWP than the semi‐intensive. In the baseline, dairy water demand to produce a liter of milk was higher than the world average: ranging between 1,000 and 29,000 L. Among the farm‐clusters, variation of LWP was system specific and affected by farmers' access to virtual water trading (i.e. milk and feed). Improving milk productivity, feed quality and feed water productivity reduced livestock water demand per liter of milk substantially and, therefore, the saved water can be used to augment ecosystem services that can mitigate the impacts of climate change.

This paper revealed that in the study systems LWP, in the business as usual scenario, is low. But by improving animal productivity, quality feed supply and water conservation substantial volume of water can be saved.

The purpose of this paper is to assess the adoption of climate-smart agriculture (CSA) and its implication on improving the farming household food security status, their resilience and livelihood risk management of farmers.

This systematic review has followed procedures to accomplish the review, including literature searches, screening studies, data extraction, synthesis and presentation of the data.

Based on the result of the review, the determinants of CSA adoption can be categorized into five categories, including demographic factors (age, sex, family size, dependency ratio, education), economic factors (land size, household income, livestock ownership), institutional factors (extension services, training access, credit services, farm input, market distance), environmental factors (agroecology, change in precipitation, slope of land) and social factors (cooperatives membership, farmers perception). The result also shows that applying CSA practices has an indispensable role on increasing productivity, food security, income, building resilient livelihoods, minimizing production risk and alleviating poverty. This concluded CSA practice has a multidimensional role in the livelihood of agrarian population like Ethiopia, yet its adoption was constrained by several factors.

This review mainly emphasizes on the most commonly practiced CSA strategies that are examined by different scholars.

This study aims to analyze the temperature variability and change for the past 30 years (1990–2019) and the future 60 years (2030s, 2050s and 2070s) in Wolaita Zone and the surroundings, in Southern Ethiopia.

The temperature (maximum and minimum) data of the past 30 years (1990–2019) of ten meteorological stations and the future (2021–2080) data of regional climate models (RCMs) under two representative concentration pathways (RCP4.5 and RCP8.5) were used in this study. The accuracy of RCMs in representing observed temperature data was evaluated against mean absolute error, root-mean-square error, percent bias, Nash–Sutcliffe measure of efficiency, index of agreement (d) and coefficient of determination (R²). The temperature variability was analyzed using the coefficient of variation, and the trend was determined using the Mann–Kendall trend and Sen’s slope tests.

The results indicate that the past maximum (Tmax) and minimum (Tmin) temperatures showed low variability (CV = 4.3%) with consistently increasing trends. Similarly, Tmax and Tmin are projected to have low variability in the future years, with upward trends. The Tmax and Tmin are projected to deviate by 0.7°C–1.2°C, 1.3°C–2.2°C and 1.5°C–3.2°C by 2030s, 2050s and 2070s, respectively, under RCP4.5 and RCP8.5, from the baseline. Thus, it can be concluded that temperature has low variability in all periods, with consistently increasing trends. The increasing temperature could have been affecting agricultural production systems in Southern Ethiopia.

This research did not remove the uncertainties of models (inherited errors of models) in future temperature projections. However, this study did not have any limitation. Therefore, individuals or organizations working on agricultural productivity, food security and sustainable development can use the results and recommendations.

The globe has been warming due to the increasing temperature; as a result, many adaptation and mitigation measures have been suggested globally and nationally (IPCC, 2021). FAO (2017) indicates that the level of vulnerability to the impacts of climate change varies with geographic location, economy and demography; the adaptation measures need to be local. The detailed information on temperature variability and change in the past and future helps to understand the associated negative impacts on agriculture, hydrology, biodiversity, environment and human well-being, among others.

The projected future climate pattern helps the country devise proactive adaptation and mitigation measures for the associated damages at different levels (from local to national). This could improve the resilience of farmers and the country to climate change impacts. This contributes to achieving sustainable development goals (e.g. no poverty, zero hunger and climate action). This is because the agriculture sector in Ethiopia accounts for 80% of employment, 33% of the gross domestic product and 76% of exports (EPRSS, 2023).

Temperature is one of the major climate elements affecting agricultural production in rain-fed production systems. Despite this, past studies in Southern Ethiopia considered only the past temperature but not the future climate. Thus, generating detailed information about past and future temperatures is very important to take proactive adaptation measures for reducing climate-associated damages in the agriculture sector in Ethiopia.