Adaptation strategies for maize cultivation under climate change in Iran: irrigation and planting date management

Climate change is affecting the productivity of crops and their regional distribution. Strategies to enhance local adaptation capacity are needed to mitigate climate change impacts and to maintain regional stability of food production. The objectives of this study were to simulate the climate change effects on phenological stages, Leaf Area Index (LAI), biomass and grain yield of maize (Zea mays L.) in the future and to explore the possibilities of employing irrigation water and planting dates as adaptation strategies to decrease the climate change impacts on maize production in Khorasan Razavi province, Iran. For this purpose, we employed two types of General Circulation Models ((United Kingdom Met. Office Hadley Center: HadCM3) and (Institute Pierre Simon Laplace: IPCM4)) and three scenarios (A1B, A2 and B1). Long Ashton Research Station-Weather Generator (LARS-WG) was used to produce daily climatic parameters as one stochastic growing season for each projection period. Also, crop growth under projected climate conditions was simulated based on the Cropping System Model (CSM)-CERES-Maize. The results of model evaluation showed that LARS-WG had appropriate prediction for climatic parameters. Time period from cultivation until anthesis and maturity were reduced in majority of scenarios as affected by climate change. The results indicated that the grain yield of maize may be reduced (11 % to 38 %) as affected by climate change based on common planting date in baseline and changed (?61 % to 48 %) in response to different irrigation regimes in the future climate change, under all scenarios and times. In general, earlier planting date (1 May) and decreasing irrigation intervals in the anthesis stage (11 applications) caused higher yield compared with other planting dates due to adaption to high temperature. Based on our findings, it seems that management of irrigation water and planting dates can be beneficial for adaptation of maize to climate change in this region.     

Future production of rainfed wheat in Iran (Khorasan province): climate change scenario analysis

Projecting staple crop production including wheat under future climate plays a fundamental role in planning the required adaptation and mitigation strategies for climate change effects especially in developing countries. The main aim of this study was to investigate the direction and magnitude of climate change impacts on grain yield of rainfed wheat (Triticum aestivum L.) production and precipitation within growing season. This study was performed for various regions in Khorasan province which is located in northeast of Iran. Climate projections of two General Circulation Models (GCM) for four locations under three climate change scenarios were employed in this study for different future time periods. A stochastic weather generator (LARS-WG5) was used for downscaling to generate daily climate parameters from GCMs output. The Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.5 was employed to evaluate rainfed wheat performance under future climate. Grain yield of rainfed wheat and precipitation during growth period considerably decreased under different scenarios in various time periods in contrast to baseline. Highest grain yield and precipitation during growth period was obtained under B1 scenario but A1B and A2 scenarios resulted in sharp decrease (by ?57 %) of grain yield. Climate change did not have marked effects on evapotranspiration during the rainfed wheat growth. A significant correlation was detected between grain yield, precipitation and evapotranspiration under climate change for both GCMs and under all study scenarios. It was concluded, that rainfed wheat production may decline during the next 80 years especially under A2 scenario. Therefore, planning the comprehensive adaptation and mitigation program is necessary for avoiding climate change negative impact on rainfed wheat production.     

Impacts of climate change on savannah woodland biomass carbon density and water-use: a modelling study of the Sudanese gum belt region

This paper analyzes potential impacts of climate change on biomass carbon (C) density and water-use (actual evapotranspiration, AET) of savannah woodlands in Sudan. Climate change scenarios were developed from five General Circulation Models (GCMs; CGCM2, CSIRO2, ECHam4, HadCM3 and PCM) under two IPCC (Intergovernmental Panel on Climate Change) emission scenarios (A1FI and B1). Baseline (1961-90) climate and climate change scenarios for 2080s for eight map sheet grids (1° latitude x 1.5° longitude) were constructed. Compared to baseline values, mean annual precipitation (MAP) showed both increases (+112 to +221 mm) and decreases (?13 to ?188 mm) but mean annual temperature (MAT) only showed increases (+1.2 to +8.3 °C). Baseline biomass C densities showed an exponential relationship with MAP (y?=?6.798 e ^0.0054x, R²?=?0.70). Depending on climate change MAP, biomass C densities increased (+14 to +241 g C m^?2) or decreased (?1 to ?148 g?C m^?2). However, because of uncertainty in biomass C density estimates, the changes were only significant (P <0.05) for some of the climate change scenarios and for grids with MAP >260 mm. Under A1FI emission scenarios, only HadCM3 did not have a significant effect while under B1 emission scenarios, only CGCM2 and ECHam4 had a significant effect on biomass C density. AET also showed both increases (+100 to +145 mm for vertisols and +82 to +197 mm for arenosols) and decreases (?12 to ?178 mm for vertisols and ?12 to ?132 mm for arenosols). The largest relative changes in AET (up to 31 %) were associated with grids receiving the lowest rainfall. Thus, even if MAP increases across the study region, the increase will have little impact on biomass levels in the driest areas of the region, emphasizing the need for improved management and use of savannah woodlands.     

Climate change impact and potential adaptation strategies under alternate climate scenarios for yam production in the sub-humid savannah zone of West Africa

Globally, yam (Dioscorea spp.) is the fifth most important root crop after sweet potatoes (Ipomoea batatas L.) and the second most important crop in Africa in terms of production after cassava (Manihot esculenta L.) and has long been vital to food security in sub-Saharan Africa (SSA). Climate change is expected to have its most severe impact on crops in food insecure regions, yet very little is known about impact of climate change on yam productivity. Therefore, we try estimating the effect of climate change on the yam (variety: Florido) yield and evaluating different adaptation strategies to mitigate its effect. Three regional climate models REgional MOdel (REMO), Swedish Meteorological and Hydrological Institute Regional Climate Model (SMHIRCA), and Hadley Regional Model (HADRM3P) were coupled to a crop growth simulation model namely Environmental Policy Integrated Climate (EPIC) version 3060 to simulate current and future yam yields in the Upper Ouémé basin (Benin Republic). For the future, substantial yield decreases were estimated varying according to the climate scenario. We explored the advantages of specific adaptation strategies suggesting that changing sowing date may be ineffective in counteracting adverse climatic effects. Late maturing cultivars could be effective in offsetting the adverse impacts. Whereas, by coupling irrigation and fertilizer application with late maturing cultivars, highest increase in the yam productivity could be realized which accounted up to 49 % depending upon the projection of the scenarios analyzed.     

Impact of Climate Change on Maize Yield in Central and Northern Greece: A Simulation Study with Ceres-Maize

The potential impacts of climate change on the phenology and yield of two maize varieties in Greece were studied. Three sites representing the central and northern agricultural regions were selected: Karditsa, Naoussa and Xanthi. The CERES-Maize model, embedded in the Decision Support System for Agrotechnology Transfer (DSSAT 3.0), was used for the crop simulations, with current and possible future management practices. Equilibrium doubled CO₂ climate change scenarios were derived from the GISS, GFDL, and UKMO general circulation models (GCMs); a transient scenario was developed from the GISS GCM transient run A. These scenarios predict consistent increases in air temperature, small increases in solar radiation and precipitation changes that vary considerably over the study regions in Greece. Physiological effects of CO₂ on crop growth and yield were simulated. Under present management practices, the climate change scenarios generally resulted in decreases in maize yield due to reduced duration of the growing period at all sites. Adaptation analyses showed that mitigation of climate change effects may be achieved through earlier sowing dates and the use of new maize varieties. Varieties with higher kernel-filling rates, currently restricted to the central regions, could be extended to the northern regions of Greece. In the central regions, new maize varieties with longer grain-filling periods might be needed.     

Groundwater-dependent irrigation costs and benefits for adaptation to global change

The effects of a 1.5 °C global change on irrigation costs and carbon emissions in a groundwater-dependent irrigation system were assessed in the northwestern region of Bangladesh and examined at the global scale to determine possible global impacts and propose necessary adaptation measures. Downscaled climate projections were obtained from an ensemble of eight general circulation models (GCMs) for three representative concentration pathways (RCPs), RCP2.6, RCP4.5, and RCP8.5 and were used to generate the 1.5 °C warming scenarios. A water balance model was used to estimate irrigation demand, a support vector machine (SVM) model was used to simulate groundwater levels, an energy-use model was used to estimate carbon emissions from the irrigation pump, and a multiple linear regression (MLR) model was used to simulate the irrigation costs. The results showed that groundwater levels would likely drop by only 0.03 to 0.4 m under a 1.5 °C temperature increase, which would result in an increase in irrigation costs and carbon emissions ranging from 11.14 to 148.4 Bangladesh taka (BDT) and 0.3 to 4% CO₂ emissions/ha, respectively, in northwestern Bangladesh. The results indicate that the impacts of climate change on irrigation costs for groundwater-dependent irrigation would be negligible if warming is limited to 1.5 °C; however, increased emissions, up to 4%, from irrigation pumps can have a significant impact on the total emissions from agriculture. This study revealed that similar impacts from irrigation pumps worldwide would result in an increase in carbon emissions by 4.65 to 65.06 thousand tons, based only on emissions from groundwater-dependent rice fields. Restricting groundwater-based irrigation in regions where the groundwater is already vulnerable, improving irrigation efficiency by educating farmers and enhancing pump efficiency by following optimum pumping guidelines can mitigate the impacts of climate change on groundwater resources, increase farmers’ profits, and reduce carbon emissions in regions with groundwater-dependent irrigation.     

Vulnerability of Indian mustard (<Emphasis Type="Italic">Brassica juncea</Emphasis> (L.) Czernj. Cosson) to climate variability and future adaptation strategies

A simulation study has been carried out using the InfoCrop mustard model to assess the impact of climate change and adaptation gains and to delineate the vulnerable regions for mustard (Brassica juncea (L.) Czernj. Cosson) production in India. On an all India basis, climate change is projected to reduce mustard grain yield by ～2 % in 2020 (2010–2039), ～7.9 % in 2050 (2040–2069) and ～15 % in 2080 (2070–2099) climate scenarios of MIROC3.2.HI (a global climate model) and Providing Regional Climates for Impact Studies (PRECIS, a regional climate model) models, if no adaptation is followed. However, spatiotemporal variations exist for the magnitude of impacts. Yield is projected to reduce in regions with current mean seasonal temperature regimes above 25/10 °C during crop growth. Adapting to climate change through a combination of improved input efficiency, additional fertilizers and adjusting the sowing time of current varieties can increase yield by ～17 %. With improved varieties, yield can be enhanced by ～25 % in 2020 climate scenario. But, projected benefits may reduce thereafter. Development of short-duration varieties and improved crop husbandry becomes essential for sustaining mustard yield in future climates. As climatically suitable period for mustard cultivation may reduce in future, short-duration (<130 days) cultivars with 63 % pod filling period will become more adaptable. There is a need to look beyond the suggested adaptation strategy to minimize the yield reduction in net vulnerable regions.     

北京农业气候资源变化特征及其对不同种植模式玉米各生育期的影响

分析1960—2008年北京山区和平原的春玉米、平原夏玉米不同生育期的热量、水、光等农业气候资源的变化趋势和突变特征,研究农业气候资源变化对玉米可能产生的影响。利用统计回归分析方法研究不同种植模式玉米在关键生育期内主要农业气候资源历史时间序列特征,同时以突变理论中的Mann-Kendall方法和Pettitt方法分析主要气候资源的趋势变化特征并检测其突变特征;在数据分析的基础上,结合国内和国际相关研究成果讨论北京市气候资源变化对玉米生长的可能性影响并给出相应对策。结果显示:近50 a来北京地区大于10℃的年活动积温和玉米全生育期有效积温呈现明显增长趋势,变化过程中无明显的突变特征;玉米播种—拔节期降水量呈增加的趋势,平原夏玉米在拔节—抽雄期和全生育期、平原春玉米在吐丝—成熟期降水量减少趋势均达显著水平,其它生育期间降水呈现不明显的下降趋势,但无明显突变点;除了抽雄—吐丝期外,玉米各生育期光照资源呈现明显的下降趋势,不同生育期的突变点均出现在20世纪80年代中期到90年代中期。北京地区农业气候资源变化趋势对玉米生产有利有弊:对积温增长的响应应以灌浆期延长为主要方式,对降水减少的响应应以种植抗旱品种和加大抗旱技术研究为主,对光照资源显著减少的对策应以调节田间通风透光和培育耐阴性品种为主。     

Mitigation of climate change impacts on maize productivity in northeast of Iran: a simulation study

Development and evaluation of mitigation strategies are fundamental to manage climate change risks. This study was built on (1) quantifying the response of maize (Zea mays L.) grain yield to potential impacts of climate change and (2) investigating the effectiveness of changing sowing date of maize as a mitigation option for Khorasan Province which is located in northeast of Iran. Two types of General Circulation Models (GCM: (United Kingdom Met Office Hadley Center :HadCM3) and (Institute Pierre Simon Laplace: IPCM4)) and three scenarios (A1B, A2 and B1) at four locations (Mashhad, Birjand, Bojnourd and Sabzevar) employed in this study. Long Ashton Research Station-Weather Generator (LARS-WG) was employed for generating the future climate. The Cropping System Model (CSM)-CERES-Maize was used for crop growth simulation under projected climate conditions. The results showed the simulated grain yields of maize gradually would decrease (from −1% to −39%) during future 100 years compared to baseline under different scenarios and two GCM at all study locations. The simulation results suggested that delayed sowing date from May to June at all study locations, except Sabzevar location is the most effective mitigation option for avoiding thermal stress at end of growth period. In addition, shifting in sowing date to March or April will be beneficial in terms of obtaining higher yields in Sabzevar. Grain yield did not show special trend from north to south of Khorasan Province in the future climate. In general, change of sowing date may be quite beneficial to mitigate climate change impacts on grain yield of maize in northeast of Iran.     

Assessing high-impact spots of climate change: spatial yield simulations with Decision Support System for Agrotechnology Transfer (DSSAT) model

Drybeans (Phaseolus vulgaris L.) are an important subsistence crop in Central America. Future climate change may threaten drybean production and jeopardize smallholder farmers’ food security. We estimated yield changes in drybeans due to changing climate in these countries using downscaled data from global circulation models (GCMs) in El Salvador, Guatemala, Honduras, and Nicaragua. We generated daily weather data, which we used in the Decision Support System for Agrotechnology Transfer (DSSAT) drybean submodel. We compared different cultivars, soils, and fertilizer options in three planting seasons. We analyzed the simulated yields to spatially classify high-impact spots of climate change across the four countries. The results show a corridor of reduced yields from Lake Nicaragua to central Honduras (10–38 % decrease). Yields increased in the Guatemalan highlands, towards the Atlantic coast, and in southern Nicaragua (10–41 % increase). Some farmers will be able to adapt to climate change, but others will have to change crops, which will require external support. Research institutions will need to devise technologies that allow farmers to adapt and provide policy makers with feasible strategies to implement them.     

Adaptation for crop agriculture to climate change in Cameroon: Turning on the heat

The Cameroonian agricultural sector, a critical part of the local ecosystem, is potentially vulnerable to climate change raising concerns about food security in the country’s future. Adaptations policies may be able to mitigate some of this vulnerability. This article investigates and addresses the issue of selected adaptation options within the context of Cameroonian food production. A methodology is applied where transient diagnostics of two atmosphere–ocean general circulation models, the NASA/Goddard Institute GISS and the British HadCM3, are coupled to a cropping system simulation model (CropSyst) to simulate current and future (2020, 2080) crop yields for selected key crops (bambara nut, groundnut, maize, sorghum, and soybean) in eight agricultural regions of Cameroon. Our results show that for the future, substantial yield increases are estimated for bambara groundnut, soybean and groundnut, while little or no change or even decreases for maize and sorghum yields, varying according to the climate scenario and the agricultural region investigated. Taking the “no regrets” principle into consideration, we explore the advantages of specific adaptation strategies specifically for three crops viz. maize, sorghum and bambara groundnut, under GISS A2 and B2 marker scenarios only. Changing sowing dates may be ineffective in counteracting adverse climatic effects because of the narrow rainfall band that strictly determines the timing of farm operations in Cameroon. In contrast, the possibility of developing later maturing new cultivars proved to be extremely effective in offsetting adverse impacts, giving the highest increases in productivity under different scenario projections without management changes. For example, under climate change scenario GISS A2 2080, a 14.6% reduction in maize yield was converted to a 32.1% increase; a 39.9% decrease in sorghum yield was converted to a 17.6% increase, and for bambara groundnut (an under-researched and underutilised African legume), yields were almost trebled (37.1% increase above that for sowing date alone (12.9%)) due to increase length of growing period and the positive effects of higher CO₂ concentrations. These results may better inform wider studies and development strategies on sustainable agriculture in the area by providing an indication as to the potential direction in shifts in production capabilities. Our approach highlights the benefit of using models as tools to investigate potential climate change impacts, where results can supplement existing knowledge. The results provide useful guidance and motivation to public authorities and development agencies interested in food security issues in Cameroon and elsewhere.     

Simulating impacts,potential adaptation and vulnerability of maize to climate change in India

Climate change associated global warming, rise in carbon dioxide concentration and uncertainties in precipitation has profound implications on Indian agriculture. Maize (Zea mays L.), the third most important cereal crop in India, has a major role to play in country’s food security. Thus, it is important to analyze the consequence of climate change on maize productivity in major maize producing regions in India and elucidate potential adaptive strategy to minimize the adverse effects. Calibrated and validated InfoCrop-MAIZE model was used for analyzing the impacts of increase in temperature, carbon dioxide (CO₂) and change in rainfall apart from HadCM3 A2a scenario for 2020, 2050 and 2080. The main insights from the analysis are threefold. First, maize yields in monsoon are projected to be adversely affected due to rise in atmospheric temperature; but increased rainfall can partly offset those loses. During winter, maize grain yield is projected to reduced with increase in temperature in two of the regions (Mid Indo-Gangetic Plains or MIGP, and Southern Plateau or SP), but in the Upper Indo-Gangetic Plain (UIGP), where relatively low temperatures prevail during winter, yield increased up to a 2.7°C rise in temperature. Variation in rainfall may not have a major impact on winter yields, as the crop is already well irrigated. Secondly, the spatio-temporal variations in projected changes in temperature and rainfall are likely to lead to differential impacts in the different regions. In particular, monsoon yield is reduced most in SP (up to 35%), winter yield is reduced most in MIGP (up to 55%), while UIGP yields are relatively unaffected. Third, developing new cultivars with growth pattern in changed climate scenarios similar to that of current varieties in present conditions could be an advantageous adaptation strategy for minimizing the vulnerability of maize production in India.     

Implications of climate change on mitigation potential estimates for forest sector in India

Climate change is projected to impact forest ecosystems, including biodiversity and Net Primary Productivity (NPP). National level carbon forest sector mitigation potential estimates are available for India; however impacts of projected climate change are not included in the mitigation potential estimates. Change in NPP (in gC/m²/yr) is taken to represent the impacts of climate change. Long term impacts of climate change (2085) on the NPP of Indian forests are available; however no such regional estimates are available for short and medium terms. The present study based on GCM climatology scenarios projects the short, medium and long term impacts of climate change on forest ecosystems especially on NPP using BIOME4 vegetation model. We estimate that under A2 scenario by the year 2030 the NPP changes by (−5) to 40% across different agro-ecological zones (AEZ). By 2050 it increases by 15% to 59% and by 2070 it increases by 34 to 84%. However, under B2 scenario it increases only by 3 to 25%, 3.5 to 34% and (−2.5) to 38% respectively, in the same time periods. The cumulative mitigation potential is estimated to increase by up to 21% (by nearly 1 GtC) under A2 scenario between the years 2008 and 2108, whereas, under B2 the mitigation potential increases only by 14% (646 MtC). However, cumulative mitigation potential estimates obtained from IBIS—a dynamic global vegetation model suggest much smaller gains, where mitigation potential increases by only 6% and 5% during the period 2008 to 2108.     

全球增温1.5℃和2.0℃对淮河中上游径流影响预估

论文应用第5次耦合模式比较计划(Coupled Model Intercomparison Project Phase 5,CMIP5)中5个全球气候模式(Global Climate Models,GCMs)和3种典型浓度路径(Representative Concentration Pathways,RCPs)在全球增温1.5℃和2.0℃下的预估结果,分析了淮河中上游地区未来的气候变化特征。进一步基于SWAT(Soil and Water Assessment Tool)水文模型定量预估了气候变化对该区域径流量的影响,并量化了预估结果的不确定性。结果表明:SWAT模型在淮河中上游对月径流量具有较好的模拟能力。在全球增温1.5℃和2.0℃下,淮河中上游年平均气温分别较基准期(1986—2005年)增加1.1℃和1.7℃;年降水量较基准期分别相应增加4%和7%;基于SWAT模型预估的年径流量较基准期分别增加5%和8%。未来气候变化不会改变月径流分布特征,年内径流仍集中在盛夏和初秋(6—9月)。预估的月丰水流量明显增加,尤其当全球增温达到2.0℃后,出现洪涝的风险明显增大。未来降水量和径流量预估都存在较大的不确定性,不确定性主要来源于GCMs,在全球增温2.0℃下预估的不确定性更大。     

Contribution of spatially explicit models to climate change adaptation and mitigation plans for a priority forest habitat

Climate change will impact forest ecosystems, their biodiversity and the livelihoods they sustain. Several adaptation and mitigation strategies to counteract climate change impacts have been proposed for these ecosystems. However, effective implementation of such strategies requires a clear understanding of how climate change will influence the future distribution of forest ecosystems. This study uses maximum entropy modelling (MaxEnt) to predict environmentally suitable areas for cork oak (Quercus suber) woodlands, a socio-economically important forest ecosystem protected by the European Union Habitats Directive. Specifically, we use two climate change scenarios to predict changes in environmental suitability across the entire geographical range of the cork oak and in areas where stands were recently established. Up to 40 % of current environmentally suitable areas for cork oak may be lost by 2070, mainly in northern Africa and southern Iberian Peninsula. Almost 90 % of new cork oak stands are predicted to lose suitability by the end of the century, but future plantations can take advantage of increasing suitability in northern Iberian Peninsula and France. The predicted impacts cross-country borders, showing that a multinational strategy, will be required for cork oak woodland adaptation to climate change. Such a strategy must be regionally adjusted, featuring the protection of refugia sites in southern areas and stimulating sustainable forest management in areas that will keep long-term suitability. Afforestation efforts should also be promoted but must consider environmental suitability and land competition issues.     

Climate change scenarios to facilitate stakeholder engagement in agricultural adaptation

To prepare agricultural systems for climate change, scientists need to be able to effectively engage with land managers and policy makers to explore potential solutions. An ongoing challenge in engagement is to distil the complexity of climate-change-management-change interactions in agro-ecological systems to identify responses that are most important for adaptation planning. This paper presents an approach for selecting climate change scenarios to provide a focal point for engaging with stakeholders to evaluate adaptation options and communicate assessment outcomes. We illustrate how scenarios selected with the approach can be used by evaluating climate change impacts and an adaptation option for livestock industries in the north-east Australian rangelands. Climate change impacts on forage production, animal liveweight gain and soil loss are found to track projected climate changes in four pasture communities; increasing by up to 50% and declining by up to 110% in response to doubled atmospheric carbon-dioxide (CO₂), 4°C warming, and +20% to ?40% changes in mean annual rainfall. The effectiveness of reducing grazing pressure as an adaptation option shows a similar response; resulting in higher forage production (up to 40%), animal liveweight gains (up to 59%) and gross margins (up to 40%), and reduced soil erosion (down by 91%) per hectare relative to the baseline management. The results show that a few key scenarios may be selected to represent the range of global climate model (GCM) projections for use in assessing and communicating impacts and adaptation; simplifying the assessments and allowing limits to the effectiveness of adaptation options to be explored. The approach provides a framework for capturing and communicating trends in climate change impacts and the utility of options, which are required for successful engagement of stakeholders in finding viable adaption responses.     

Artificial neural networks models for predicting effective drought index: Factoring effects of rainfall variability

Though most factors that trigger droughts cannot be prevented, accurate, relevant and timely forecasts can be used to mitigate their impacts. Drought forecasts must define the droughts severity, onset, cessation, duration and spatial distribution. Given the high probability of droughts occurrence in Kenya, her heavy reliance on rain-fed agriculture and lack of effective drought mitigation strategies, the country is highly vulnerable to impacts of droughts. Current drought forecasting approaches used in Kenya are not able to provide short and long term forecasts and they fall short of providing the severity of the drought. In this paper, a combination of Artificial Neural Networks and Effective Drought Index is presented as a potential candidate for addressing these drawbacks. This is demonstrated using forecasting models that were built using weather data for thirty years for four weather stations (representing 3 agro-ecological zones) in Kenya. Experiments varying various input/output combinations were carried out and drought forecasting network models were implemented in Matrix Laboratory’s (MATLAB) Neural Network Toolbox. The models incorporate forecasted rainfall values in order to mitigate for unexpected extreme climate variations. With accuracies as high as 98 %, the solution is a great enhancement to the solutions currently in use in Kenya.     

Regional crop yield, water consumption and water use efficiency and their responses to climate change in the North China Plain

The North China Plain (NCP) is one of the most important regions for food production in China, with its agricultural system being significantly affected by the undergoing climate change and vulnerable with water stress. In this study, the Vegetation Interface Processes (VIP) model is used to evaluate crop yield, water consumption (ET), and water use efficiency (WUE) of a winter wheat (Triticum aestivum L.)–summer maize (Zea mays L.) double cropping system in the NCP from 1951 to 2006. Their responses to future climate scenarios of 21st century projected by the GCM (HadCM3) with Intergovernmental Panel on Climate Change Special Report on Emission Scenario (IPCC SRES) A2 and B1 emissions are investigated. The results show a rapid enhancement of crop yield in the past 56 years, accompanying with slight increment of ET and noticeable improvement of WUE. There exist spatial patterns of crop yield stemmed mainly from soil quality and irrigation facilities. For climate change impacts, it is found that winter wheat yield will significantly increase with the maximum increment in A2 occurring in 2070s with a value of 19%, whereas the maximum in B1 being 13% in 2060s. Its ET is slightly intensified, which is less than 6%, under both A2 and B1 scenarios, giving rise to the improvement of WUE by 10% and 7% under A2 and B1 scenarios, respectively. Comparatively, summer maize yield will gently decline by 15% for A2 and 12% for B1 scenario, respectively. Its ET is obviously increasing since 2050s with over 10% relative change, leading to a lower WUE with more than 25% relative change under both scenarios in 2090s. Therefore, possible adaptation countermeasures should be developed to mitigate the negative effects of climate change for the sustainable development of agro-ecosystems in the NCP.     

Impact of climate change on regional irrigation water demand in Baojixia irrigation district of China

Water scarcity in China would possibly be aggravated by rapid increase in water demand for irrigation due to climate change. This paper focuses on the mechanism of climate change impact on regional irrigation water demand by considering the dynamic feedback relationships among climate change, irrigation water demand and adaptation measures. The model in implemented using system dynamics approach and employed in Baojixia irrigation district located in Shaanxi Province of China to analyses the changes in irrigation water demand under different climate change scenarios. Obtained results revealed that temperature will be the dominant factor to determine irrigation water demand in the area. An increase of temperature by 1 °C will result in net irrigation water demand to increase by about 12,050?×?10⁴ m³ and gross water demand by about 20,080?×?10⁴ m³ in the area. However, irrigation water demand will not increase at the same rate of temperature rise as the adaptation measures will eventually reduce the water demand increased by temperature rise. It is expected that the modeling approach presented in this study can be used in adopting policy responses to reduce climate change impacts on water resources.     

Climate impact potential of utilizing forest residues for bioenergy in Norway

The utilization of forest residues for bioenergy in Norway is foreseen to increase due to the government call to double bioenergy output by 2020 to thirty Tera-Watt hours. This study focuses on the climate impacts of bioenergy utilization where four forest residue extraction scenarios at clear-cut are considered: i) 75 % above ground residues (branches, (25 %) foliage, tops); ii) 75 % above and below ground residues (branches, tops, (25 %) foliage, stumps, coarse and small roots); iii) extracting 100 % of all available forest residue; and iv) leaving all residues in the forest. The Yasso07 soil-carbon model was utilized to quantify the carbon flux to the atmosphere due to the forest residues that are left in the forest in each scenario. The climate impact potential for each scenario was then calculated for the carbon-flux neutral Norway Spruce (Picea abies) forest system in five regions of Norway. The biogenic carbon dioxide emissions associated to decomposition upon forest floor, procurement losses and bioenergy conversion are included in these calculations. Results suggest that if such bioenergy can directly replace a fossil source of energy, the utilization of this biomass was found to be climatically beneficial in most fossil energy replacement cases and time horizons when compared to leaving the residues in the forest. Integrated global temperature change displacement factors have been developed which have been used to estimate the magnitude of this climate change mitigation over a particular time horizon.