Optimizing Eastern Gamagrass Forage Harvests Using Growing Degree Days

Tripsacum dactyloides (L.) L., commonly known as eastern gamagrass, is useful for grazing, stored forage, soil amelioration and conservation, and as a biofuel feedstock. Our goal was to calculate accumulated growing degree days (GDD) from existing datasets collected for eastern gamagrass forage production experiments in northwestern Oklahoma, and discuss the use of GDD, instead of calendar harvest dates, in the production of eastern gamagrass forage. Growing degree days were calculated from 1 January each year using the “optimum day method”. For 10 harvest years, the first eastern gamagrass harvest required 690 ± 26 cumulative GDD. Based on long-term weather data from Woodward, Oklahoma, this would place the first harvest on or near 1 June. The second harvest required 635 ± 27 cumulative GDD which would place the second harvest on or near 15 July and the third harvest required 690 ± 23 cumulative GDD placing it on or near 30 August. Each of the 30 harvest required an average of 670 ± 15 cumulative GDD. Using GDD to predict harvest events is a useful tool that forage producer can use in the production of eastern gamagrass forage in the USA and possibly elsewhere.

Introducing Degree Days to Building Thermal Climatic Zoning in China

Building thermal climatic zoning is a key issue in building energy efficiency.Heating degree days(HDD) and cooling degree days(CDD) are often employed as indexes to represent the heating and cooling energy demand in climatic zoning.However,only using degree days may oversimplify the climatic zoning in regions with complex climatic conditions.In the present study,the application of degree days to current building thermal climatic zoning in China was assessed based on performance simulations.To investigate the key indexes for thermal climatic zoning,the climate characteristics of typical cities were analyzed and the relationships between the climate indexes and heating/cooling demand were obtained.The results reveal that the annual cumulative heating load had a linear correlation with HDD 18 only in regions with small differences in altitude.Therefore,HDD is unsuitable for representing the heating demand in regions with large differences in altitude.A comprehensive index(winter climatic severity index) should be employed instead of HDD,or complementary indexes(daily global solar radiation or altitude) could be used to further divide climate zones.In the current official climatic zoning,the base temperature of 26℃ for CDD is excessively high.The appropriate base temperature range is 18℃ to 22℃.This study provides a reference for selecting indexes to improve thermal climatic zoning in regions with similar climates.

Improving Quantitative and Qualitative Characteristics of Wheat (Triticum aestivum L.) through Nitrogen Application under Semiarid Conditions

Nitrogen(N),the building block of plant proteins and enzymes,is an essential macronutrient for plant functions.A field experiment was conducted to investigate the impact of different N application rates(28,57,85,114,142,171,and 200 kg ha^(−1))on the performance of spring wheat(cv.Ujala-2016)during the 2017–2018 and 2018–2019 growing seasons.A control without N application was kept for comparison.Two years mean data showed optimum seed yield(5,461.3 kg ha^(−1))for N-application at 142 kg ha^(−1) whereas application of lower and higher rates of N did not result in significant and economically higher seed yield.A higher seed yield was obtained in the 2017–2018(5,595 kg ha^(−1))than in the 2018–2019(5,328 kg ha^(−1))growing seasons under an N application of 142 kg ha^(−1).It was attributed to the greater number of growing degree days in the first(1,942.35°C days)than in the second year(1,813.75°C).Higher rates of N(171 and 200 kg ha^(−1))than 142 kg ha^(−1) produced more number of tillers(i.e.,948,300 and 666,650 ha^(−1),respectively).However,this increase did not contribute in achieving higher yields.Application of 142,171,and 200 kg ha^(−1) resulted in 14.15%,15.0%and 15.35%grain protein concentrations in comparison to 13.15%with the application of 114 kg ha^(−1).It is concluded that the application of N at 142 kg ha^(−1) could be beneficial for attaining higher grain yields and protein concentrations of wheat cultivar Ujala-2016.

IMPACT OF CLIMATE ON SUMMER RESIDENTIAL ELECTRICITY CONSUMPTION IN HONG KONG

The influence of climatic variables and cooling degree days (CDD) on summer residential electricity consumption for the period 1980 through 1994 in Hong Kong was investigated. The association between Clo, a measure of amount of Clothing insulation to maintain comfort, and residential electricity consumption was also examined. Utilizing monthly data and multiple regression analyses, it is discovered vapor pressure was not significantly related to electricity consumption while Cloud cover was negatively associated with electricity use. Climatic variables, CDD and Clo provided highly comparable results in modeling summer residential electricity consumption. Mean temperature and Cloud gave the best result. Clo yielded a slightly higher R2 value (0.867) than that of CDD (0.865) in the models. These results indicated that Clo could replace the weather variables and CDD to model electricity consumption.

Deduction of a meteorological phenology indicator from reconstructed MODIS LST imagery

Phenology is a valuable attribute of vegetation to assess the biological impacts from climate change.A challenge of phenological research is to obtain information on both high temporal resolution and fine spatial scale observations.Here,we constructed an air temperature map based on temporal merging and spatial interpolation algorithms to overcome the cloud-related problem from the MODIS LST product.Then,we derived the accumulated growing degree days(AGDD)from the constructed mean air temperature map to use as a meteorological indicator.Further,we verified the indicator with the seasonal mean air temperature and the green-up date of a Quercus mongolica forest determined from the field-based measurements.The AGDD threshold for each Q.mongolica forest when the first leaf has unfolded was detected from the EXG trajectory extracted from digital camera images.A comparison between meteorological and MODIS-derived AGDD showed good agreement between them.There was also high consistency between DoYs extracted from AGDD and EVI based on curvature K for Q.mongolica forests of 30 sampling sites throughout South Korea.The results prove that microclimatic factors such as elevation,waterbody,and land-use intensity were faithfully reflected in the reconstructed images.Therefore,the results of this study could be applied effectively in areas where microclimatic variation is very severe and for monitoring phenology of undergrowth,which is difficult to detect from reflectance imaging.

Impact of temperature on yield and related traits in cotton genotypes

Cotton growth and development is influenced by various uncontrollable environmental conditions. Temperature variations in the field can be created by planting at different dates. The objective of the present study was to evaluate the effect of planting dates and thermal temperatures（growing degree days） on yield of 4 cotton genotypes, viz., CIM-598, CIM-599, CIM-602 and Ali Akbar-703. Plants were subjected to 6 planting dates during 2013 and 2014 in a trial conducted in randomized complete block design with four replications. For boll number, boll weight and seed cotton yield, cotton genotypes exhibited significant differences, CIM-599 produced the highest seed cotton yield of 2 062 kg ha^（–1） on account of maximum boll number and boll weight. The highest seed cotton yield was recorded in planting dates from 15 th April to 1st May whereas early and delayed planting reduced the yield due to less accumulation of heat units. Regression analysis revealed that increase of one unit（15 days） from early to optimum date（15th March to 15 th April） increased yield by 93.58 kg ha^–1. Delay in planting also decreased the seed cotton yield with the same ratio. Thus it is concluded that cotton must be sown from 15 th April to 1st May to have good productivity in this kind of environment.

Alpine tundra species phenology is mostly driven by climate-related variables rather than by photoperiod

The study of plant phenology has frequently been used to link phenological events to various factors,such as temperature or photoperiod.In the high-alpine environment,proper timing of the phenological cycle has always been crucial to overcome harsh conditions and potential extreme events(i.e.spring frosts)but little is known about the response dynamics of the vegetation,which could shape the alpine landscape in a future of changing climate.Alpine tundra vegetation is composed by an array of species belonging to different phytosociological optima and with various survival strategies,and snowbed communities are a relevant expression of such an extreme-climate adapted flora.We set eight permanent plots with each one in a snowbed located on the Cimalegna plateau in Northwestern Italy and then we selected 10 most recurring species among our plots,all typical of the alpine tundra environment and classified in 3different pools:snowbed specialists,grassland species and rocky debris species.For 3 years we registered the phenophases of each species during the whole growing season using an adaptation of the BBCH scale.We later focused on the three most biologically relevant phenophases,i.e.,flower buds visible,full flowering,and beginning of seed dispersion.Three important season-related variables were chosen to investigate their relationship with the phenological cycle of the studied species:(i)the Day Of Year(DOY),the progressive number of days starting from the 1 st of January,used as a proxy of photoperiod,(ii)Days From Snow Melt(DFSM),selected to include the relevance of the snow dynamics,and(iii)Growing Degree Days(GDD),computed as a thermal sum.Our analysis highlighted that phenological development correlated better with DFSM and GDD than with DOY.Indeed,models showed that DOY was always a worse predictor since it failed to overcome interannual variations,while DFSM and marginally GDD were better suited to predict the phenological development of most of the species,despite differences intemperature and snowmelt date among the three years.Even if the response pattern to the three variables was mainly consistent for all the species,the timing of their phenological response was different.Indeed,species such as Salix herbacea and Ranunculus glacialis were always earlier in the achievement of the phenophases,while Agrostis rupestris and Euphrasia minima developed later and the remaining species showed an intermediate behavior.However,we did not detect significant differences among the three functional pools of species.

Does heat accumulation alter crop phenology, fibre yield and fibre properties of sunnhemp(Crotalaria juncea L.) genotypes with changing seasons?

Field experiments were carried out in split plot design during the dry and wet seasons for two years(two seasons each in 2016–2017 and 2017–2018) with two genotypes(SH4 and SUIN053), two plant geometry(30×15 cm and 45×15 cm main plots) and three levels of NPK(20 kg N ha^(–1), 40 kg P ha^(–1) and 40 kg K ha^(–1);20 kg N ha^(–1), 60 kg P ha^(–1) and 60 kg K ha^(–1);20 kg N ha^(–1), 80 kg P ha^(–1) and 80 kg K ha^(–1)) with an objective to study the relationship between fibre yield of sunhmep and thermal indices. The results indicated that the thermal units such as cumulative heat unit(CHU), photo thermal unit(PTU) and helio thermal unit(HTU) were the highest during dry seasons, while relative temperature disparity(RTD) was the highest during wet seasons irrespective of the genotypes, plant geometry and fertilizer levels. The combined analysis of variance showed that the suitability of sunnhemp genotypes for obtaining fibre and seed yields varied with season. The results further indicated that sunnhemp grew during dry seasons with longer photoperiod and higher values of growing degree days(GDD), HTU and PTU resulted in a higher fibre yield, while a higher seed yield and relatively longer, finer and stronger fibres were obtained during wet seasons with higher RTD values. Regression analysis indicated that CHU was positively related to fibre yield, while RTD was positively related to seed yield. CHU beyond 2 000 °C d reduced seed yield and favoured fibre production. In contrary to CHU, RTD values were positively related to seed yield and negatively related to fibre yield. Similarly, HTU had an inverse relationship with fibre yield while PTU had a positive relationship with fibre yield. The genotype SH4 produced a seed yield of 1 361 kg ha^(–1) during wet seasons, which was significantly higher than SUIN053, while a fibre yield of 990 kg ha^(–1)(significantly higher than that of SH4) was obtained for SUIN053 that required less CHU to attain the phenological events during dry seasons. The per unit area yields of seed and fibre with the closer spacing(30 cm×15 cm) by virtue of higher plant density were 17.0 and 14.9% higher than those with the spacing of 45 cm×15 cm, respectively. Higher doses of P and K resulted in higher seed and fibre yields.

The Influence of Urbanization on Cooling Energy Consumption in Xi＇an

Based on data of daily air temperature during 1951-2013,long-term variation characteristics of cooling degree days（ CDD） in Xi＇an and Chang＇an in summer were analyzed by using CDD to evaluate cooling energy consumption and 26 ℃ as the basic temperature of CDD. The results indicated that the changing trends of CDD in Xi＇an and Chang＇an were basically identical within a year,and the demand for cooling refrigeration was large mainly from June to August,especially in July. The maximum of urban-rural difference of CDD between Xi＇an and Chang＇an appeared in June.In order to achieve the same temperature,energy needed by the urban area was 5-7 ℃·d more than the suburb from June to August. Temperature and the cooling energy consumption were closely related,and the correlation degree increased with the rise of temperature. The effects of temperature increase of 1 ℃ on cooling energy consumption rate in Xi＇an were more obvious than that in Chang＇an. In both Xi＇an and Chang＇an,the effects of temperature increase of 1 ℃ on cooling energy consumption rate in July and August were greater than that in May,June and September.Evaluation models of cooling energy consumption in summer in Xi＇an and Chang＇an were built using temperature anomaly and CDD variability and can be applied to business systems.

Growing Season and Phenological Stages of Small Grain Crops in Response to Climate Change in Alaska

The climate change in Alaska has caused earlier spring snowmelt and the growing season expanded. However, the effect of climate change on crop phenological stages, heading (BBCH 55) and maturity (BBCH 85), is unknown. In this study, the trends of growing-season length (GSL), phenological stages of crops and climatic parameters, and the correlations between climatic parameters and the phenological stages were analyzed using the climate data and crop data over the period of 1978 to 2016. The longer GSL was found in Fairbanks (64.83˚N, 147.77˚W) and in Delta Junction (64.05˚N, 145.60˚W) but not in Palmer (61.60˚N, 149.11˚W). Sowing dates did not change significantly in three locations. The decreasing trends of heading and maturity of crops were observed but varied with location. Heading of barley and oat significantly advanced 3 and 3.1 d decade-1, respectively from 1989 to 2016 in Fairbanks while no change of heading was observed in Delta Junction and Palmer. Maturity of barley, oat and wheat significantly advanced 2.6, 3.8 and 3.9 d decade-1, respectively from 1978 to 2016 in Fairbanks (P < 0.05);maturity of oat and wheat significantly advanced 4.4 and 3.4 d decade-1 from 1978 to 2015, respectively in Delta Junction (P < 0.05). The increasing temperature trends and decreasing precipitation trends were found in Fairbanks and Delta Junction but varied with phenological stages of crops. Sowing was more important for heading than for maturity of crops. The effect of climate change on heading was less important than that on maturity. Earlier maturity of crops in Fairbanks may be attributed to increased temperatures, that in Delta Junction to both increased minimum temperature and decreased precipitation and that in Palmer to temperature and precipitation.

An Approach to Quantify the Heat Wave Strength and Price a Heat Derivative for Risk Hedging

Mitigating the heat stress via a derivative policy is a vital financial option for agricultural producers and other business sectors to strategically adapt to the climate change scenario. This study has provided an approach to identifying heat stress events and pricing the heat stress weather derivative due to persistent days of high surface air temperature （SAT）. Cooling degree days （CDD） are used as the weather index for trade. In this study, a call-option model was used as an example for calculating the price of the index. Two heat stress indices were developed to describe the severity and physical impact of heat waves. The daily Global Historical Climatology Network （GHCN-D） SAT data from 1901 to 2007 from the southern California, USA, were used. A major California heat wave that occurred 20-25 October 1965 was studied. The derivative price was calculated based on the call-option model for both long-term station data and the interpolated grid point data at a regular 0.1~ x0.1~ latitude-longitude grid. The resulting comparison indicates that （a） the interpolated data can be used as reliable proxy to price the CDD and （b） a normal distribution model cannot always be used to reliably calculate the CDD price. In conclusion, the data, models, and procedures described in this study have potential application in hedging agricultural and other risks.

Multi-model assessment of glacio-hydrological changes in central Karakoram, Pakistan

The multi-model assessment of glacio-hydrological regimes can enhance our understanding of glacier response to climate change.This improved knowledge can uplift our computing abilities to estimate the contributing components of the river discharge.This study examined and compared the hydrological responses in the glacier-dominated Shigar River basin(SRB)under various climatic scenarios using a semi-distributed Modified Positive Degree Day Model(MPDDM)and a distributed Glacio-hydrological Degree-day Model(GDM).Both glacio-hydrological models were calibrated and validated against the observed hydro-meteorological data from 1988–1992 and 1993–1997.Temperature and precipitation data from Shigar and Skardu meteorological stations were used along with field estimated degree-day factor,temperature,and precipitation gradients.The results from both models indicate that the snow and ice melt are vital contributors to sustain river flow in the catchment.However,MPDDM estimated 68%of rain and baseflow contribution to annual river runoff despite low precipitation during the summer monsoon,while GDM estimated 14%rain and baseflow contribution.Likewise,MPDDM calculated 32%,and GDM generated 86%of the annual river runoff from snow and ice melt.MPDDM simulated river discharge with 0.86 and 0.78 NSE for calibration and validation,respectively.Similarly,GDM simulated river discharge with improved accuracy of 0.87 for calibration and 0.84 NSE for the validation period.The snow and ice melt is significant in sustaining river flow in the SRB,and substantial changes in melt characteristics of snow and ice are expected to have severe consequences on seasonal water availability.Based on the sensitivity analysis,both models’outputs are highly sensitive to the variation in temperature.Furthermore,compared to MPDDM,GDM simulated considerable variation in the river discharge in climate scenarios,RCP4.5 and 8.5,mainly due to the higher sensitivity of GDM model outputs to temperature change.The integration of an updated melt module and two reservoir baseflow module in GDM is anticipated to advance the representation of hydrological components,unlike one reservoir baseflow module used separately in MPDDM.The restructured melt and baseflow modules in GDM have fundamentally enriched our perception of glacio-hydrological dynamics in the catchment.

Summer maize LAI retrieval based on multi-source remote sensing data

Leaf Area of Index(LAI)refers to half of the total leaf area of all crops per unit area.It is an important index to represent the photosynthetic capacity and biomass of crops.To obtain LAI conditions of summer maize in different growth stages quickly and accurately,further guiding field fertilization and irrigation.The Unmanned aerial vehicles(UAV)multispectral data,growing degree days,and canopy height model of 2020-2021 summer maize were used to carry out LAI inversion.The vegetation index was constructed by the ground hyperspectral data and multispectral data of the same range of bands.The correlation analysis was conducted to verify the accuracy of the multispectral data.To include many bands as possible,four vegetation indices which included R,G,B,and NIR bands were selected in this study to test the spectral accuracy.There were nine vegetation indices calculated with UAV multispectral data which were based on the red band and the near-infrared band.Through correlation analysis of LAI and the vegetation index,vegetation indices with a higher correlation to LAI were selected to construct the LAI inversion model.In addition,the Canopy Height Model(CHM)and Growing degree days(GDD)of summer maize were also calculated to build the LAI inversion model.The LAI inversion of summer maize was carried out based on multi-growth stages by using the general linear regression model(GLR),Multivariate nonlinear regression model(MNR),and the partial least squares regression(PLSR)models.R²and RMSE were used to assess the accuracy of the model.The results show that the correlation between UAV multispectral data and hyperspectral data was greater than 0.64,which was significant.The Wide Dynamic Range Vegetation Index(WDRVI),Normalized Difference Vegetation Index(NDVI),Ratio Vegetation Index(RVI),Plant Biochemical Index(PBI),Optimized Soil-Adjusted Vegetation Index(OSAVI),CHM and GDD have a higher correlation with LAI.By comparing the models constructed by the three methods,it was found that the PLSR has the best inversion effect.It was based on OSAVI,GDD,RVI,PBI,CHM,NDVI,and WDRVI,with the training model’s R²being 0.8663,the testing model’s R²being 0.7102,RMSE was 1.1755.This study showed that the LAI inversion model based on UAV multispectral vegetation index,GDD,and CHM improves the accuracy of LAI inversion effectively.That means the growing degree days and crop population structure change have influenced the change of maize LAI certainly,and this method can provide decision support for maize growth monitoring and field fertilization.

基于Google Earth Engine(GEE)和作物模型快速评估低温冷害对大豆生产的影响

Frequent chilling injury has serious impacts on national food security and in northeastern China heavily affects grain yields.Timely and accurate measures are desirable for assessing associated large-scale impacts and are prerequisites to disaster reduction.Therefore,we propose a novel means to efficiently assess the impacts of chilling injury on soybean.Specific chilling injury events were diagnosed in 1989,1995,2003,2009,and 2018 in Oroqen community.In total,512 combinations scenarios were established using the localized CROPGRO-Soybean model.Furthermore,we determined the maximum wide dynamic vegetation index(WDRVI)and corresponding date of critical windows of the early and late growing seasons using the GEE(Google Earth Engine)platform,then constructed 1600 cold vulnerability models on CDD(Cold Degree Days),the simulated LAI(Leaf Area Index)and yields from the CROPGRO-Soybean model.Finally,we calculated pixel yields losses according to the corresponding vulnerability models.The findings show that simulated historical yield losses in 1989,1995,2003 and 2009 were measured at 9.6%,29.8%,50.5%,and 15.7%,respectively,closely(all errors are within one standard deviation)reflecting actual losses(6.4%,39.2%,47.7%,and 13.2%,respectively).The above proposed method was applied to evaluate the yield loss for 2018 at the pixel scale.Specifically,a sentinel-2A image was used for 10-m high precision yield mapping,and the estimated losses were found to characterize the actual yield losses from 2018 cold events.The results highlight that the proposed method can efficiently and accurately assess the effects of chilling injury on soybean crops.

Effect of low temperature in the development cycle of Lucilia sericata (Meigen) (Diptera, Calliphoridae): implications for the minimum postmortem interval estimation

Knowledge of necrophagous insects' developmental data is necessary for the forensic entomologist to estimate a reliable minimum postmortem interval (PMImin).Among the most represented necrophagous species,Lucilia sericata (Diptera,Calliphoridae) is particularly interesting.It is regularly identified in samples,with a predominance in summer,and is commonly used by analysts of our entomology department (Institut de Recherche Criminelle de la Gendarmerie Nationale) to estimate the PMImin with the accumulated degree days (ADD) method.This method requires the mathematical lower thermal threshold to be known.This value dictates the quality of the applied ADD method but cannot be considered as fixed,especially when insect development occurs at temperatures close to the biological threshold.In such conditions,it is necessary to study the influence of such temperatures on development rate,as well as the consequences of estimating the period of first oviposition on cadavers,when using the ADD method.Seven replicate rearings were conducted at six different temperatures: 30 ℃,24 ℃,18 ℃,15 ℃,12 ℃ and 10 ℃.Time of development and time of emergence were recorded.The effect of low temperature on the development cycle and the reliability of the ADD method under this entire temperature spectrum were studied using different linear regression models.Calculated durations of total insect time development and experimental rearing duration were then compared.A global linear model cannot be used on the whole temperature spectrum experienced by L.sericata without resulting in an overestimation at some temperatures.We found a combination of two linear regression models to be suitable for the estimation of the total development time,depending on the temperature experienced by L.sericata.This approach allowed us to obtain a variation lower than 2% at 12 ℃ and 10 ℃ between the calculated duration and experimental duration of development.In comparison,the results obtained with a global model show a variation higher than 3% at 12 ℃ and 10%at 10℃.

Applicability of remote sensingbased surface temperature regimes in determining deciduous phenology over boreal forest

Aims The study of deciduous phenology over boreal forest is important for understanding forest ecology and better management.In this paper,our objective was to determine the phenological stages of deciduous leaf out(DLO)over the deciduous-dominant[i.e.trembling aspen(Populus tremuloides)]stands in the Canadian Province of Alberta.Methods During the period 2006–08,we used Moderate Resolution Imaging Spectroradiometer(MODIS)-based 8-day surface temperature(TS)images to calculate accumulated growing degree days(AGDD:a favourable temperature regime for plant growth).The temporal dynamics of AGDD in conjunction with in situ DLO observations were then analysed in determining the optimal threshold for DLO in 2006(i.e.80 degree days).Important Findings The implementation of the above-mentioned optimal threshold revealed reasonable agreements(i.e.on an average 91.9%of the DLO cases within ±2 periods or ±16 days of deviations during 2007–08)in comparison to the in situ observed data.The developments could be useful in various forestry-related applications,e.g.plant growth and its ability of exchanging atmospheric carbon dioxide,forest ecohydrology,risk of insect infestation,forest fire and impact of climate change,among others.

Regional growth model for summer maize based on a logistic model:Case study in China

The growing degree days(GDD)is an important factor for crop growth because it affects dry matter formation and crop yield.In this study,the universal logistic models were established employing GDD and the relative GDD(RGDD)as the main parameters to characterize summer maize growth indices such as plant height(H),leaf area index(LAI),and dry matter accumulation(DMA).The relationships were analyzed between the growth indices,harvest index(HI),water consumption,and yield in maize.By considering China as an example,the results showed that the logistic model performed well at simulating the changes in the summer maize growth indices in different regions and the universal model parameters were within specific ranges.Furthermore,the logistic model with RGDD as the independent variable was more suitable for modeling summer maize growth in large areas than GDD.The relationship between the maximum LAI and HI was described by a quadratic polynomial function.HI was optimal(0.53)when the maximum LAI was about 5.13.The maximum LAI,maximum H,and maximum DMA could be described by a quadratic polynomial function of water consumption during the growing season.The summer maize yield could be predicted with a binary quadratic equation using the maximum GDD and water consumption.This study confirmed that a logistic model can be used to establish a universal growth model for summer maize in large areas.Reasonable ranges of parameters were recommended for the logistic model,as well as the reasonable water consumption and each growth index value for summer maize.These results will be helpful for predicting the growth and yield of summer maize.

Effect of open-field experimental warming on the leaf phenology of oriental oak (Quercus variabilis) seedlings

Aims An open-field warming experiment enables us to test the effects of projected temperature increase on change in plant phenology with fewer confounding factors and to study phenological response to temperature ranges beyond natural variability.This study aims to(i)examine the effect of temperature increase on leaf unfold-ing and senescence of oriental oak(Quercus variabilis Blume)under experimental warming and(ii)measure temperature-related parameters used in estimating phenological response to tempera-ture elevation.Methods Using an open-field warming system with infrared heaters,we increased the air temperature by~3℃ in the warmed plots compared with that of the control plots consistently for 2 years.Leaf unfolding and senescence dates of Q.variabilis seedlings were recorded and temperature-related phenological parameters were analysed.Important Findings The timing of leaf unfolding was advanced by 3-8 days(1.1-3.0 days/℃)and the date of leaf senescence was delayed by 14-19 days(5.0-7.3 days/℃)under elevated air temperatures.However,the cumulative degree days(CDD)of leaf unfolding were not significantly changed by experimental warming,which suggest the applicability of a constant CDD value to estimate the change in spring leaf phe-nology under 3℃ warming.Consistent ranges of advancement and temperature sensitivity in spring phenology and delayed autumn phe-nology and proposed temperature parameters from this study might be applied to predict future phenological change.