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.

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.

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.

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.

Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data

The accumulation of thermal time usually represents the local heat resources to drive crop growth.Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity.To solve the critical problems of estimating air temperature(T a) and filling in missing pixels due to cloudy and low-quality images in growing degree days(GDDs) calculation from remotely sensed data,a novel spatio-temporal algorithm for T a estimation from Terra and Aqua moderate resolution imaging spectroradiometer(MODIS) data was proposed.This is a preliminary study to calculate heat accumulation,expressed in accumulative growing degree days(AGDDs) above 10 ℃,from reconstructed T a based on MODIS land surface temperature(LST) data.The verification results of maximum T a,minimum T a,GDD,and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels.Overall,MODIS-derived AGDD was slightly underestimated with almost 10% relative error.However,the feasibility of employing AGDD anomaly maps to characterize the 2001-2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper.Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring,agricultural climatic regionalization,and agro-meteorological disaster detection at the regional scale.

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.

The relative controls of temperature and soil moisture on the start of carbon flux phenology and net ecosystem production in two alpine meadows on the Qinghai-Tibetan Plateau

Aims Variations in vegetation spring phenology are widely attributed to temperature in temperate and cold regions.However,temperature effect on phenology remains elusive in cold and arid/semiarid ecosystems because soil water condition also plays an important role in mediating phenology.Methods We used growing degree day(GDD)model and growing season index(GSI)model,coupling minimum temperature(T_(min))with soil moisture(SM)to explore the influence of heat requirement and hydroclimatic interaction on the start of carbon uptake period(SCUP)and net ecosystem productivity(NEP)in two alpine meadows with different precipitation regimes on the Qinghai-Tibet Plateau(QTP).One is the water-limited alpine steppe-meadow,and the other is the temperature-limited alpine shrub-meadow.Important Findings We observed two clear patterns linking GDD and GSI to SCUP:SCUP was similarly sensitive to variations in preseason GDD and GSI in the humid alpine shrub-meadow,while SCUP was more sensitive to the variability in preseason GSI than GDD in the semiarid alpine steppe-meadow.The divergent patterns indicated a balance of the limiting climatic factors between temperature and water availability.In the humid meadow,higher temperature sensitivity of SCUP could maximize thermal benefit without drought stress,as evidenced by the stronger linear correlation coefficient(R2)and Akaike’s information criterion(AIC)between observed SCUPs and those of simulated by GDD model.However,greater water sensitivity of SCUP could maximize the benefit of water in semiarid steppe-meadow,which is indicated by the stronger R2 and AIC between observed SCUPs and those of simulated by GSI model.Additionally,although SCUPs were determined by GDD in the alpine shrub-meadow ecosystem,NEP was both controlled by accumulative GSI in two alpine meadows.Our study highlights the impacts of hydroclimatic interaction on spring carbon flux phenology and vegetation productivity in the humid and semiarid alpine ecosystems.The results also suggest that water,together with temperature should be included in the models of phenology and carbon budget for alpine ecosystems in semiarid regions.These fi ndings have important implications for improving vegetation phenology models,thus advancing our understanding of the interplay between vegetation phenology,productivity and climate change in future.

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.