Abstract: EU rural development policy (RDP) regulation 1305/2013 aims to protect farmers’ incomes from ongoing change of climate variability (CCV), and the increase in frequency of adverse climatic events. An income stabilization tool (IST) is provided to compensate drastic drops in income, including those caused by climatic events. The present study examines some aspect of its application focussing on Mediterranean irrigation area where frequent water shortages may generate significant income reductions in the current climate conditions, and may be further exacerbated by climate change. This enhanced loss of income in the future would occur due to a change in climate variability. This change would appreciably reduce the probability of weather conditions that are favourable for irrigation, but would not significantly increase either the probability of unfavourable weather conditions or the magnitude of their impact. As the IST and other insurance tools that protect against adversity and catastrophic events are only activated under extreme conditions, farmers may not consider them to be suitable in dealing with the new climate regime. This would leave a portion of the financial resources allocated by the RDP unused, resulting in less support for climate change adaptation.

Abstract: Maize (Zea mays L) is one of the most important food crops and very common in all parts of sub-Saharan Africa. In 2010 53 million tons of maize were produced in sub-Saharan Africa on about one third of the total harvested cropland area (similar to 33 million ha). Our aim is to identify the limiting agroclimatic variable for maize growth and development in sub-Saharan Africa by analyzing the separated and combined effects of temperature and precipitation. Under changing climate, both climate variables are projected to change severely, and their impacts on crop yields are frequently assessed using process-based crop models. However it is often unclear which agroclimatic variable will have the strongest influence on crop growth and development under climate change and previous studies disagree over this question. We create synthetic climate data in order to study the effect of large changes in the length of the wet season and the amount of precipitation during the wet season both separately and in combination with changes in temperature. The dynamic global vegetation model for managed land LPJmL is used to simulate maize yields under current and future climatic conditions for the two 10-year periods 2056-2065 and 2081-2090 for three climate scenarios for the A1b emission scenario but without considering the beneficial CO2 fertilization effect. The importance of temperature and precipitation effects on maize yields varies spatially and we identify four groups of crop yield changes: regions with strong negative effects resulting from climate change (<-33% yield change), regions with moderate (-33% to -10% yield change) or slight negative effects (-10% to +6% yield change), and regions with positive effects arising from climate change mainly in currently temperature-limited high altitudes (>+6% yield change). In the first three groups temperature increases lead to maize yield reductions of 3 to 20%, with the exception of mountainous and thus cooler regions in South and East Africa. A reduction of the wet season precipitation causes decreases in maize yield of at least 30% and prevails over the effect of increased temperatures in southern parts of Mozambique and Zambia, the Sahel and parts of eastern Africa in the two projection periods. This knowledge about the limiting abiotic stress factor in each region will help to prioritize future research needs in modeling of agricultural systems as well as in drought and heat stress breeding programs and to identify adaption options in agricultural development projects. On the other hand the study enhances the understanding of temperature and water stress effects on crop yields in a global vegetation model in order to identify future research and model development needs. (C) 2013 Elsevier B.V. All rights reserved.

Abstract: A better understanding of processes that shape farmers’ adaptation to climate change is critical to identify vulnerable entities and to develop well-targeted adaptation policies. However, it is currently poorly understood what determines farmers’ adaptation and how to measure it. In this study, we develop an activity-based adaptation index (AAI) and explore the relationship between socioeconomic variables and farmers’ adaptation behavior by means of an explanatory factor analysis and a multiple linear regression model using latent variables. The model was tested in six villages situated in two administrative wards in the Morogoro region of Tanzania. The Mlali ward represents a system of relatively high agricultural potential, whereas the Gairo ward represents a system of low agricultural potential. A household survey, a rapid rural appraisal and, a stakeholder workshop were used for data collection. The data were analyzed using factor analysis, multiple linear regression, descriptive statistical methods and qualitative content analysis. The empirical results are discussed in the context of theoretical concepts of adaptation and the sustainable livelihood approach. We found that public investment in rural infrastructure, in the availability and technically efficient use of inputs, in a good education system that provides equal chances for women, and in the strengthening of social capital, agricultural extension and, microcredit services are the best means of improving the adaptation of the farmers from the six villages in Gairo and Mlali. We conclude that the newly developed AAI is a simple but promising way to capture the complexity of adaptation processes that addresses a number of shortcomings of previous index studies.