Abstract: The Northeast Farming Region (NFR) is the most important and the largest rain-fed maize production region in China, accounting for 30% of China’s maize. We investigated the effects of climatic factors, drought risk and irrigation requirement on maize yield in different maize growth phases during 1961 to 2010 using a statistical analysis of observed yield from 44 stations in NFR. We divided the maize growing season into four growth phases, comprising seeding, vegetative, flowering and maturity. The dual crop coefficient was used to calculate crop evapotranspiration and soil water balance during the maize growing season. The effects of mean temperature, radiation, effective rainfall, water deficit, drought stress days, actual crop evapotranspiration (ETa) and irrigation requirement in different growth phases were included in the statistical model to predict maize yield. During the period 1961 to 2010, mean temperature increased significantly in all growth phases in NFR, while radiation decreased significantly in southern NFR in the seeding, vegetative and flowering phases. Effective rainfall increased in the seeding and vegetative phases leading to less water deficit, whereas decreased effective rainfall in the flowering and maturity phases enhanced water deficit. More days with drought stress were concentrated in western NFR where larger volumes of irrigation were needed. Our results indicate that the increase of mean temperature in the seeding and maturity phases was beneficial for maize yield, higher ETa in each growth phase would lead to yield increase, but too high rainfall would damage maize yield. The results also show that water deficit and drought stress days had significant negative effects on maize yield, and the absence of irrigation would manifest such effects on maize production in NFR. Therefore, the development of irrigation and drainage systems is highly needed for ensuring the stability of maize production in NFR. In addition, other adaptation measures like introducing new cultivars and optimizing soil and crop management to better conserve soil water would be beneficial for future maize production. No Label

Abstract: In this study at regional scale, the model DSSAT was applied in order to simulate the cultivation of winter durum wheat and tomato to estimate the green water and the blue water through a dual-step approach (with and without supplemental irrigation). The model simulation covered a period of 30 years in three scenario including a reference period and two future scenarios based on forecasted global average temperature increase of 2 and 5°C. In this paper GW e BW contribution for evapotranspiration requirement is presented and analyzed on a distributed scale related to Puglia region (Southern Italy) characterized by high evaporative demand of the atmosphere. For winter durum wheat the GW component was predominant compared to BW, covering almost 90% of the ETc of WW. Under Baseline scenario the weight of BW was of 11%, slightly increasing in the future scenarios. After considering the probability the climate change determine an increase of irrigation practice for WW from climatic point of view we carried out an example of analysis in order to verify the economical convenience of supplemental irrigation for WW cultivation. The probability that irrigation has a negative or zero income ranged between 55 and 60% and the climate change did not impact the profitability of irrigation for WW as simulated for the economic and agro-pedoclimatic conditions of Puglia region considered in this study.For tomato, in the baseline and future scenarios affected by global warming, the analysis of ET components showed with strong evidence the importance of irrigation that is confirmed as irreplaceable practice for obtaining sustainable yield from productive and economical point of view.GW and BW, both in the case of wheat and tomato, appeared dependent on the spatial and temporal distribution of rainfall during the crop cycle, but also on the hydraulic characteristics of soils corresponding to each calculation unit. No Label

Abstract: In the recently finished EU-funded project Carbo-Extreme, we developed a simple probabilistic method for quantifying vulnerabilities and risks to ecosystems (http://iopscience.iop.org/1748-9326/8/1/015032). The method defines risk as expected loss due to environmental hazards, and shows how such risk can be calculated as the product of ecosystem vulnerability and hazard probability. The method was used with six different vegetation models to estimate current and future drought risks for crops, grasslands and forests across Europe (http://www.biogeosciences.net/11/6357/2014/bg-11-6357-2014.html).In the still ongoing UK-funded project GREENHOUSE, the focus is on spatial upscaling of local measurements and model predictions of greenhouse gas emissions to wider regions. As part of this work, we are comparing different model upscaling methods – ranging from naive input aggregation to geostatistics – and quantify the uncertainties associated with the upscaling. This work builds on an earlier inventory of model upscaling methods that was produced in a collaboration of CEH-Edinburgh and the University of Bonn (https://www.stat.aau.at/Tagungen/statgis/2009/StatGIS2009Van%20Oijen1.pdf). Here we show a comparison of the methods using model predictions for the border region of England and Scotland. No Label