Abstract: The Northeast Farming Region of China (NFR) is a very important crop growing area, comprising seven sub-regions: Xing’anling (XA), Sanjiang (SJ), Northwest Songliao (NSL), Central Songliao (CSL), Southwest Songliao (SSL), Changbaishan (CB) and Liaodong (LD), which has been severely affected by extreme climate events and climatic change. Therefore, a set of expert survey has been done to identify current and project future climate limitations to crop production and explore appropriate adaptation measures in NFR. Droughts have been the largest limitation for maize (Zea mays L.) in NSL and SSL, and for soybean (Glycine max L Merr.) in SSL. Chilling damage has been the largest limitation for rice (Oryza sativa L) production in XA, SJ and CB. Projected climate change is expected to be beneficial for expanding the crop growing season, and to provide more suitable conditions for sowing and harvest. Autumn frost will occur later in most parts of NFR, and chilling damage will also decrease, particularly for rice production in XA and SJ. Drought and heat stress are expected to become more severe for maize and soybean production in most parts of NFR. Also, plant diseases, pests and weeds are considered to become more severe for crop production under climate change. Adaptation measures that have already been implemented in recent decades to cope with current climatic limitations include changes in timing of cultivation, variety choice, soil tillage practices, crop protection, irrigation and use of plastic film for soil cover. With the projected climate change and increasing risk of climatic extremes, additional adaptation measures will become relevant for sustaining and improving productivity of crops in NFR to ensure food security in China. (C) 2016 Elsevier B.V. All rights reserved.

Abstract: The Mediterranean region has always shown a marked inter-annual variability in seasonal weather, creating uncertainty in decisional processes of cultivation and livestock breeding that should not be neglected when modeling farmers’ adaptive responses. This is especially relevant when assessing the impact of climate change (CC), which modifies the atmospheric variability and generates new uncertainty conditions, and the possibility of adaptation of agriculture. Our analysis examines this aspect reconstructing the effects of inter-annual climate variability in a diversified farming district that well represents a wide range of rainfed and irrigated agricultural systems in the Mediterranean area. We used a Regional Atmospheric Modelling System and a weather generator to generate 150 stochastic years of the present and near future climate. Then, we implemented calibrated crop and livestock models to estimate the corresponding productive responses in the form of probability distribution functions (PDFs) under the two climatic conditions. We assumed these PDFs able to represent the expectations of farmers in a discrete stochastic programming (DSP) model that reproduced their economic behaviour under uncertainty conditions. The comparison of the results in the two scenarios provided an assessment of the impact of CC, also taking into account the possibility of adjustment allowed by present technologies and price regimes. The DSP model is built in blocks that represent the farm typologies operating in the study area, each one with its own resource endowment, decisional constraints and economic response. Under this latter aspect, major differences emerged among farm typologies and sub-zones of the study area. A crucial element of differentiation was water availability, since only irrigated C3 crops took full advantage from the fertilization effect of increasing atmospheric CO2 concentration. Rainfed crop production was depressed by the expected reduction of spring rainfall associated to the higher temperatures. So, a dualism emerges between the smaller impact on crop production in the irrigated plain sub-zone, equipped with collective water networks and abundant irrigation resources, and the major negative impact in the hilly area, where these facilities and resources are absent. However intensive dairy farming was also negatively affected in terms of milk production and quality, and cattle mortality because of the increasing summer temperatures. This provides explicit guidance for addressing strategic adaptation policies and for framing farmers’ perception of CC, in order to help them to develop an awareness of the phenomena that are already in progress, which is a prerequisite for effective adaptation responses.

Abstract: AimTo derive location-specific parameters that reflect the geographic differences among cultivars in vernalization requirements, sensitivity to day length (photoperiod) and temperature, which can be used to simulate the phenological development of wheat and maize at the global scale. LocationGlobal. Methods Based on crop calendar observations and literature describing the large-scale patterns of phenological characteristics of cultivars, we developed algorithms to compute location-specific parameters to represent this large-scale pattern. Vernalization requirements were related to the duration and coldness of winter, sensitivity to day length was assumed to be represented by the minimum and maximum day lengths occurring at a location, and sensitivity to temperature was related to temperature conditions during the vegetative development phase of the crop. Results Application of the derived location-specific parameters resulted in high agreement between simulated and observed lengths of the cropping period. Agreement was especially high for wheat, with mean absolute errors of less than 3 weeks. In the main maize cropping regions, cropping periods were over- and underestimated by 0.5-1.5 months. We also found that interannual variability in simulated wheat harvest dates was more realistic when accounting for photoperiod effects. Main conclusions The methodology presented here provides a good basis for modelling the phenological characteristics of cultivars at the global scale. We show that current global patterns of growing season length as described in cropping calendars can be largely reproduced by phenology models if location-specific parameters are derived from temperature and day length indicators. Growing seasons can be modelled more accurately for wheat than for maize, especially in warm regions. Our method for computing parameters for phenology models from temperature and day length offers opportunities to improve the simulation of crop productivity by crop simulation models developed for large spatial areas and for long-term climate impact projections that account for adaptation in the selection of varieties