Accumulation Of Growing Degree Days Research Articles

Monitoring for Changes in Spring Phenology at Both Temporal and Spatial Scales Based on MODIS LST Data in South Korea

This study aims to monitor spatiotemporal changes of spring phenology using the green-up start dates based on the accumulated growing degree days (AGDD) and the enhanced vegetation index (EVI), which were deducted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) data. The green-up start dates were extracted from the MODIS-derived AGDD and EVI for 30 Mongolian oak (Quercus mongolica Fisch.) stands throughout South Korea. The relationship between green-up day of year needed to reach the AGDD threshold (DoYAGDD) and air temperature was closely maintained in data in both MODIS image interpretation and from 93 meteorological stations. Leaf green-up dates of Mongolian oak based on the AGDD threshold obtained from the records measured at five meteorological stations during the last century showed the same trend as the result of cherry observed visibly. Extrapolating the results, the spring onset of Mongolian oak and cherry has become earlier (14.5 ± 4.3 and 10.7 ± 3.6 days, respectively) with the rise of air temperature over the last century. The temperature in urban areas was consistently higher than that in the forest and the rural areas and the result was reflected on the vegetation phenology. Our study expanded the scale of the study on spring vegetation phenology spatiotemporally by combining satellite images with meteorological data. We expect our findings could be used to predict long-term changes in ecosystems due to climate change.

INTER-ANNUAL DIFFERENCE OF LAND SURFACE PHENOLOGY (LSP) TRANSACTION DATES USING TIME SERIES LANDSAT IMAGES

Abstract. Land surface phenology (LSP) is a kind of vital information for land cover classification and vegetation growth monitoring. Time series Landsat images, with the advantages of long observations and high spatial resolution, have been widely used in LSP identification. However, LSP transaction dates, such as start of season (SOS) and end of season (EOS), are highly influenced by the coarse temporal resolution. In this study, we compare the inter-annual difference of LSP SOS from 5 years interval, 10 years interval and all years interval Landsat images, and improve the SOS estimated model by considering the accumulated growing degree-days (AGDD) of soil temperature and soil moisture. Results indicate that LSP SOS can serve as a good proxy for reflecting ground vegetation phenology, especially using 5 years interval Landsat images. Soil temperature and soil moisture have certain influence on SOS estimation, and the R-squared value reached 0.9 after model adjustment. This study can provide guidance for estimating suitable inter-annual LSP transaction dates under different sceneries in the future.

Performance of wheat (Triticum aestivum) varieties under different thermal regimes and N-levels

Present study was carried out during rabi 2015-16 and 2016-17 at SKUAST-Jammu to evaluate the performance of different varieties of wheat (Triticum aestivum L.) under thermal regimes and N-levels in terms of agrometerological indices like accumulated growing degree days (AGDD), heliothermal units (AHTU), photothermal units (APTU), heat use efficiencies (HUE) of grain and biological yields. The treatments comprised of three wheat varieties (HD 2967, RSP 561 and WH 1105) planted in three sowing environments 25th October (early), 14th November and 4th December with three levels of nitrogen (100, 125 and 150 kg/ha) in split split plot design. Among the varieties, WH 1105 recorded significantly higher AGDD, AHTU, APTU and HUE than HD 2967 and RSP 561. Early sowing (25th October) of wheat also recorded higher AGDD, HTU, PTU and HUE values and grain and biological yield HUE than normal (14th November) and late sowing (4th December) environments. Delayed planting resulted in the reduction of accumulation of thermal indices along with HUE. Fifty kg higher nitrogen (150 kg/ha) than recommended dose (100 kg/ha) exhibited higher agrometerological indices values along with higher HUEs. The relationship between grain and biological yield with AGDD and HUE was highly significant during both the years under experimentation.

Nitrogen Fertilization and Harvest Timing Affect Switchgrass Quality

Early season switchgrass (Panicum virgatum L.) can be used as animal feed and mature late-season biomass as a biofuel feedstock. However, nitrogen (N) application and harvest timing effects on the quality of both end-use need further evaluation. This study evaluated the changes in nutritive quality for animal feed and biofuel feedstock, under different N application rates (0 to 235 kg N ha−1 yr−1) and different harvest times at a fixed N rate. Plant N removal increased with increasing N application rate (P < 0.05). The largest single difference (27%) was found between 0 and 33.6 kg N ha−1 application rates. Nitrogen removal decreased during subsequent harvests at a fixed N rate (P ≤ 0.0001). Forage quality was affected by N rates, although it was especially impacted by harvesting time. Fibers and most minerals in the biomass increased as accumulated growing degree days (AGDD) increased (P ≤ 0.0001), but N and total digestible nutrients (TDN) decreased as AGDD increased (P ≤ 0.0001). High crude protein and minerals with low fiber are desired forage qualities and the opposite is true for biofuel feedstock. Earlier harvests are beneficial for hay production or livestock grazing, and late-season harvests are better for biofuel production.

Effect of Precipitation and Temperature on Larval Survival of Cephus cinctus (Hymenoptera: Cephidae) in Barley Cultivars.

Host plant traits strongly affect survivorship of insect herbivores, and host suitability is especially important for the wheat stem sawfly, Cephus cinctus Norton, which spends its entire egg, larval, and pupal periods in a single stem. Measuring larval survival inside stems from egg hatch through diapause-mediated dormancy is a potential measure of population size for the next year but is also useful in assessing effects of growing season precipitation and temperature. Larval growth is synchronized with host plant growth, and the larva cannot switch hosts. Thus, incorporating plant physiological time, as growing degree days (GDD), may yield a better prediction of larval survival. Therefore, we assessed wheat stem sawfly survival from early larval growth to the beginning of autumnal diapause in barley cultivars selected from across feed, forage, and two- or six-row malt groups. Field experiments were conducted in Gallatin and Chouteau counties, Montana, in 2016 and 2017. We used Kaplan-Meier estimation to assess larval survival among cultivars. We found that the survival of pre-diapause larvae was greatest in 'Hockett' (36.5%) and lowest in 'Celebration' (15.4%). Precipitation and temperature during the growing season affected temporal patterns for larval survival across study sites. Adjusting survivorship curves using site-specific GDD accumulation allowed cultivar-specific survivorship to be estimated more precisely for each site, despite differing environmental influences. Our findings suggest that measuring wheat stem sawfly survival across barley cultivars and standardizing by site-specific GDDs may provide better recommendations on barley cultivars that impede wheat stem sawfly population growth and reduce economic losses.

Analyzing the leafing phenology of Quercus crispula Blume using the growing degree days model

ABSTRACT In the growing degree days (GDD) model for plants, 0°C or 5°C have been empirically used as base temperature (BT) for the GDD model, without experimental determination. In this study, developmental periods for bud break and leaf opening of mizunara oak, Quercus crispula were determined by using samples from locations separated by 900 km, cut branches from central Japan and potted saplings from northern Japan. The samples from each location were then grown in growth chambers under the four constant temperatures in different years. The relationships between the developmental rate and temperature were determined, BTs were calculated by regression analyses, and the required GDDs were determined. Multiple regression was applied to compare model fittings for bud break and leaf opening. Observational data were fitted with a logistic curve to empirically determine the BT and GDD for bud break. Better fitting was obtained for bud break than for leaf opening, in the growth chamber experiment as well as in the multiple regression, probably because bud break was controlled by the GDD accumulation, while the rate of leaf opening was directly and exponentially influenced by temperature. The BT for bud break obtained by the growth chamber experiments varied from 0.09°C–4.88°C between the two locations, and also within one location by year. A large variation in the GDD accumulation was recognized in the field observation at each location. These results indicate additional environmental factors not included in the GDD calculation likely influenced the leafing phenology of Q. crispula.

Detection of phenology using an improved shape model on time-series vegetation index in wheat

Accurate information about the growth period can guide us to fertilize, irrigate and harvest. Much progress has been achieved in detecting the phenology with the unique features of the time-series vegetation index (VI). However, these features only reflect information about specific stages (e.g., tillering, heading, and maturity stages), ignoring the information from other important stages (e.g., jointing, booting, and filling stages). In this study, a new approach for the phenology detection of winter wheat at the whole phenological stages is described, whereby the integrated accumulated growing degree days (AGDD) combined with the shape model (SM) method (SM-AGDD) is used to detect important phenology stages of winter wheat using five classic time-series VIs derived from three sensors at the field scale. Two proximal sensors (ASD FieldSpec Pro spectrometer and a Greenseeker RT 100) and a digital camera mounted on an unmanned aerial vehicle (UAV-DC) are used to acquire the above time-series VI. The results show that the newly developed SM-AGDD with the normalized difference vegetation index (NDVI) from ASD is the best predictor of crop phenology with an average RMSE ranging from 1.0 day at maturity to 10.3 days at tillering, followed by CI, EVI, and VARI, respectively. Among the three different spectral sensors, ASD has the best performance for detecting the whole targeted stages, while UAV-DC was the worst. In particular, the accuracy of EVI has the highest improvement on all growth stages. Compared with the previous SM constructed with the days after sowing (DAS) produced by Sakamoto et al. (2010), the newly developed SM-AGDD improves the accuracy of detecting the critical stages for winter wheat phenology for all VIs. We also find that SM-AGDD has a higher accuracy to the SM constructed with accumulated photothermal time (APTT) by Zeng et al. (2016). While, it also greatly simplified the calculation. This study shows that the accuracy of the shape model method is affected by the form and characteristics of the constructed shape, which could provide the theoretical basis for accurate detection of critical phenology dates for crops.

Sowing Date and Hybrid Choice Matters Production of Maize–Maize System

Maize can be sown in spring and fall seasons in Pakistan under maize–maize cropping system. Due to seasonal variability in meteorological parameters, optimization of planting time for maize hybrids is vital to harvest improved productivity in maize-maize system. This study was designed to explore the effect of diverse sowing dates on phenology, growing degree days (GDDs), photo-thermal-units (PTUs) and helio-thermal-units (HTUs), and its impact on radiation-use-efficiency (RUE) and grain yield (GY) in different maize hybrids under maize-maize cropping system. Two-year experiments were conducted to optimize planting dates for hybrids during 2016 and 2017. In spring, three hybrids were sown on Jan 15, Feb 5, Feb 25, Mar 15 and Apr 05. During fall, three hybrids were planted on Jun 15, Jul 05, Jul 25, Aug 15 and Sept 05. Results showed that spring early (Jan 15), while fall late (Jul 25) sowing took more days to complete 50% tasseling, silking and maturity. However, maize sown on Feb 05 and Jul 25 accumulated more GDDs to attain 50% tasseling, silking and maturity. Maize sown on Feb 05 and Jul 25 had more leaf area index (LAI), crop growth rate (CGR), RUE and GY, which was linked with higher accrual of GDDs, PTUs and HTUs. Likewise, hybrids P-33M15 and P-30R50, during spring and fall observed higher values of all above cited traits. Moreover, positive-correlation was witnessed among days taken to complete different phenophases, LAI, CGR and 1000-grain weight, total biomass, GY and RUE. However, higher GY and RUE was obtained in spring than fall. In conclusion, maize sown on Feb 05 and Jul 25 using hybrids P-33M15 and P-30R50, during spring and fall crops, respectively produced more GY and higher RUE due to more accumulation of GDDs, PTUs and HTUs. Thus the spring season seemed more productive than fall season under maize-maize cropping system.

Grasshopper phenological responses to climate gradients, variability, and change

AbstractSpecies have responded to recent climate change via pronounced, but often variable, shifts in seasonal timing (phenology). Can this variability be accounted for by comparing responses along an elevational gradient and among species differing in seasonal timing and traits? We resurveyed montane grasshopper communities along a 1300‐m elevational gradient to examine phenological responses to ~50 yr of climate warming. Phenological responses were elevation dependent, influenced by relative warming (which was more pronounced at higher elevations) and by substantial interannual variability in available growing degree‐days (GDDs). The total number of GDDs available for development within a season declines with elevation, and correspondingly high‐elevation populations required fewer GDDs to reach adulthood. Although phenological advancements were particularly pronounced at higher elevations, consistent with a greater magnitude of climate warming, species as a whole initiated and completed development earlier in warmer years. During warm years, phenological advances were most pronounced early in development, and the timing of adulthood tended to occur earlier (at a similar or greater accumulation of GDDs). Phenological advancement was also more pronounced in early season species, which exhibit developmental plasticity to capitalize on warming. Interactions with elevation were also strongest for the early season species. Our study shows the importance of incorporating developmental information to understand the environmental and organismal mechanisms that drive variable phenological responses and suggests that the phenological impacts of warming will be concentrated early in development, among early season species, and at high elevations. Considering these drivers of phenological variability can aid understanding and predicting climate‐induced changes to community and ecosystem structure.

Optimal Timing Assessment for Crop Separation Using Multispectral Unmanned Aerial Vehicle (UAV) Data and Textural Features

The separation of crop types is essential for many agricultural applications, particularly when within-season information is required. Generally, remote sensing may provide timely information with varying accuracy over the growing season, but in small structured agricultural areas, a very high spatial resolution may be needed that exceeds current satellite capabilities. This paper presents an experiment using spectral and textural features of NIR-red-green-blue (NIR-RGB) bands data sets acquired with an unmanned aerial vehicle (UAV). The study area is located in the Swiss Plateau, which has highly fragmented and small structured agricultural fields. The observations took place between May 5 and September 29, 2015 over 11 days. The analyses are based on a random forest (RF) approach, predicting crop separation metrics of all analyzed crops. Three temporal windows of observations based on accumulated growing degree days (AGDD) were identified: an early temporal window (515–1232 AGDD, 5 May–17 June 2015) with an average accuracy (AA) of 70–75%; a mid-season window (1362–2016 AGDD, 25 June–22 July 2015) with an AA of around 80%; and a late window (2626–3238 AGDD, 21 August–29 September 2015) with an AA of <65%. Therefore, crop separation is most promising in the mid-season window, and an additional NIR band increases the accuracy significantly. However, discrimination of winter crops is most effective in the early window, adding further observational requirements to the first window.

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.

Characterizing Land Use/Land Cover Using Multi-Sensor Time Series from the Perspective of Land Surface Phenology

Due to a rapid increase in accessible Earth observation data coupled with high computing and storage capabilities, multiple efforts over the past few years have aimed to map land use/land cover using image time series with promising outcomes. Here, we evaluate the comparative performance of alternative land cover classifications generated by using only (1) phenological metrics derived from either of two land surface phenology models, or (2) a suite of spectral band percentiles and normalized ratios (spectral variables), or (3) a combination of phenological metrics and spectral variables. First, several annual time series of remotely sensed data were assembled: Accumulated growing degree-days (AGDD) from the MODerate resolution Imaging Spectroradiometer (MODIS) 8-day land surface temperature products, 2-band Enhanced Vegetation Index (EVI2), and the spectral variables from the Harmonized Landsat Sentinel-2, as well as from the U.S. Landsat Analysis Ready Data surface reflectance products. Then, at each pixel, EVI2 time series were fitted using two different land surface phenology models: The Convex Quadratic model (CxQ), in which EVI2 = f(AGDD) and the Hybrid Piecewise Logistic Model (HPLM), in which EVI2 = f(day of year). Phenometrics and spectral variables were submitted separately and together to Random Forest Classifiers (RFC) to depict land use/land cover in Roberts County, South Dakota. HPLM RFC models showed slightly better accuracy than CxQ RFC models (about 1% relative higher in overall accuracy). Compared to phenometrically-based RFC models, spectrally-based RFC models yielded more accurate land cover maps, especially for non-crop cover types. However, the RFC models built from spectral variables could not accurately classify the wheat class, which contained mostly spring wheat with some fields in durum or winter varieties. The most accurate RFC models were obtained when using both phenometrics and spectral variables as inputs. The combined-variable RFC models overcame weaknesses of both phenometrically-based classification (low accuracy for non-vegetated covers) and spectrally-based classification (low accuracy for wheat). The analysis of important variables indicated that land cover classification for this study area was strongly driven by variables related to the initial green-up phase of seasonal growth and maximum fitted EVI2. For a deeper evaluation of RFC performance, RFC classifications were also executed with several alternative sampling scenarios, including different spatiotemporal filters to improve accuracy of sample pools and different sample sizes. Results indicated that a sample pool with less filtering yielded the most accurate predicted land cover map and a stratified random sample dataset covering approximately 0.25% or more of the study area were required to achieve an accurate land cover map. In case of data scarcity, a smaller dataset might be acceptable, but should not smaller than 0.05% of the study area.

VEGETATIVE DEVELOPMENT AND GROWING DEGREE-DAYS OF TROPICAL AND WINTER FORAGES

ABSTRACT One of the limitations to producing irrigated forages is setting suitable biometric parameters to establish the entrance of animals to grazing areas. Such parameters can be measured or even estimated. Estimates are advantageous for being practical and able to be used for grazing optimization. This study aimed to evaluate the correlation among growing degree-days (GDD), canopy height (CH), leaf area index (LAI), and number of cycle days (NCD) of Guinea grass (Panicum maximum cv. Mombaca) and Bermuda grass (Cynodon spp. Tifton 85) single cropped for one year and overseeded with black oat and ryegrass in the autumn-winter season. The study was carried out from February 2016 to February 2017, and forage crops were sprinkle irrigated. LAI and CH were measured twice a week, at intervals of 3 and 4 days. These parameters were correlated to GDD accumulation at each cutting cycle (CC). All the correlations showed high coefficients in linear fits. For spring-summer cycles, Guinea grass in exclusive cultivation showed the highest averages for CH and LAI, being 102.3 cm and 5.93, respectively. For autumn- winter cycles, when this grass was overseeded, it showed an LAI value similar to that when single cropped, which was of about 5.6. Yet single Bermuda grass presented lower values of LAI (one unit lower) and CH (16.4 cm lower) in the autumn-winter season when compared to that overseeded. The spring-summer cycles of Guinea grass had a duration of 24 days, after which no increase in leaf production was registered.

Influence of Temperature on the Development of Peach Fruit in a Subtropical Climate Region

Understanding the growing process and fruit size differences among peach cultivars is extremely important in the technological domain of the crop and can provide information to improve the proper crop management (thinning and harvesting seasons) and the crop breeding of fruits with a larger caliber. However, this information is still incipient in subtropical regions and requires further research, especially in Brazil. The aim of this study was to determine the influence of temperature on the growing of four peach cultivars (Tropical, Aurora-2, Ouro Mel-4, and Biuti) under subtropical conditions of field cultivation. Fruit development was determined every two weeks throughout the cycle with 30 fruits from previously identified branches from six different plants of each cultivar. Regarding the thermal accumulation in growing degree-days (GDD), the cultivar ‘Tropical’ showed the lowest agronomic fruit properties (size and mass) and required a lower GDD accumulation during the development stages of the fruits, whereas the cultivar ‘Biuti’ showed higher thermal requirements and higher agronomic properties. The number of cells had greater influence on the final fruit size than the cell area.

Black Medic (Medicago lupulina) Germination Response to Temperature and Osmotic Potential, and a Novel Growing Degree-Day Accounting Restriction for Heat-Limited Germination

AbstractBlack medic (Medicago lupulinaL.) is a problem weed species in Florida strawberry [Fragaria×ananassa(Weston) Duchesne ex Rozier (pro sp.) [chiloensis×virginiana]] production. It competes with the crop and hinders harvest efficiency. A reductionist approach is being undertaken to predictM. lupulinafield emergence to coordinate control tactics. Germination is the first model component to be developed. The objectives were to study the effect of osmotic potential and temperature on seed germination and to develop the germination component for reductionist emergence modeling. Trials were initiated using petri dishes in incubators to testM. lupulinagermination in response to osmotic potential (0 to −1 MPa) and constant (5 to 40 C) and fluctuating temperatures (35/25, 35/20, 25/15, and 25/10 C, 12/12-h duration).Medicago lupulinagerminated between 5 and 35 C. Optimal germination was between 10 and 20 C. Germination was negatively impacted by temperatures above the optimum. Fluctuating temperatures did not influence germination compared with constant temperatures. A reduction in osmotic potential from 0 to −0.25 MPa reduced germination from 43% to 14%. Three temperature-mediated germination trends were identified: standard increases and plateau up to 20 C, reduced germination between 20 and 35 C, and no germination at 40 C. A novel restriction to daily growing degree-day (GDD) accounting was developed for heat-limited germination. The germination restriction accounted for the optimum and maximum temperature, diminishing returns of exposure to higher temperatures, and the negative impact of higher temperatures above the optimum range. Determination logic and the new daily GDD accounting formula aligned GDD accumulation across all temperatures to be described by a Weibull formula (R2= 0.5199). Results establish the germination component for reductionist emergence modeling, but further study is required to account for dormancy and PRE growth.

Characterizing land cover/land use from multiple years of Landsat and MODIS time series: A novel approach using land surface phenology modeling and random forest classifier

Over the last 20 years, substantial amounts of grassland have been converted to other land uses in the Northern Great Plains. Most of land cover/land use (LCLU) assessments in this region have been based on the U.S. Department of Agriculture - Cropland Data Layer (USDA - CDL), which may be inconsistent. Here, we demonstrate an approach to map land cover utilizing multi-temporal Earth Observation data from Landsat and MODIS. We first built an annual time series of accumulated growing degree-days (AGDD) from MODIS 8-day composites of land surface temperatures. Using the Enhanced Vegetation Index (EVI) derived from Landsat Collection 1's surface reflectance, we then fit at each pixel a downward convex quadratic model to each year's progression of AGDD (i.e., EVI = α + β × AGDD − γ × AGDD2). Phenological metrics derived from fitted model and the goodness of fit then are submitted to a random forest classifier (RFC) to characterize LCLU for four sample counties in South Dakota in three years (2006, 2012, 2014) when reference point datasets are available for training and validation. To examine the sensitivity of the RFC to sample size and design, we performed classifications under different sample selection scenarios. The results indicate that our proposed method accurately mapped major crops in the study area but showed limited accuracy for non-vegetated land covers. Although all RFC models exhibit high accuracy, estimated land cover areas from alternative models could vary widely, suggesting the need for a careful examination of model stability in any future land cover supervised classification study. Among all sampling designs, the “same distribution” models (proportional distribution of the sample is like proportional distribution of the population) tend to yield best land cover prediction. RFC used only the most eight important variables (e.g., three fitted parameter coefficients [α, β, and γ]; maximum modeled EVI; AGDD at maximum modeled EVI; the number of observations used to fit CxQ model; and the number of valid observations) have slightly higher accuracy compared to those using all variables. By summarizing annual image time series through land surface phenology modeling, LCLU classification can embrace both seasonality and interannual variability, thereby increasing the accuracy of LCLU change detection.

Comparison of Data Mining and GDD-Based Models in Discrimination of Maize Phenology

Data mining approaches are designed for classification problems in which each observation is a member of one and only one class. In this study, a non-deterministic approach based on C5.0 data mining algorithm has been employed for discriminating the phenological stages of maize from emergence to dough, in a field located in Karaj, Iran. Two readily-available predictors i.e. accumulated growing degree days (AGDD) and multi-temporal LANDSAT7-extracted normalized difference vegetation index (NDVI) was used to build the decision tree. The AGDD was calculated based on three cardinal thresholds of temperature i.e. effective minimum, optimum, effective maximum. The NDVI was compared with two recently developed indices namely, enhanced vegetation index2 (EVI2) and optimized soil adjusted vegetation index (OSAVI) using the signal to noise ratio (SNR) criterion. Findings confirmed that these three remotely sensed indices do not have significant differences, therefore, the smoothed time series of NDVI was used in the C5.0 algorithm. The precisions of classification by C5.0 data mining algorithm in partitioning of training and testing data were approximately 90.51 and 81.77%, respectively. The mean absolute error (MAE) values of the onset of maize phenological stages were estimated about 2.6–5.3 days for various stages by C5.0 model. While corresponding values for the classical AGDD model were 3.9–10.7 days. This confirms the skill of data mining approach in comparison with commonly-used the classical AGDD model in applications of real time monitoring.

Potentials and Limitations of a Growing Degree Day Approach to Predict the Phenology of Cereal Leaf Beetles.

Cereal leaf beetles (CLBs) are described as an invasive pest of small grain cereals in many regions worldwide. Prediction models aimed to prevent yield losses caused by these feeding insects have been developed by researchers all over the world. As a foundation for many of these prediction models, it is known that a specific number of heat units, or growing degree days (GDDs), is required for an insect to complete a certain physiological process. In this paper, we overview the existing GDD models for CLBs. Furthermore, we used our Belgian input data to compare model predictions with our own observations. Though, the existing models were not able to predict the seasonal trends present in our data: the occurrence of various life stages were monitored earlier then the model predicted. Hence, a weighted GDD model was tested on the data as well: the accumulated GDDs during certain periods were balanced according to the significance of this period for the insect. Rainfall and/or relative humidity were included as well. Based on these selected variables, multiple linear regression models, ridge regression models, and regression trees were fitted. This approach performed considerably better compared to the simple accumulation of GDD. However, based on cross-year cross-location validation method, to gain insight in the future performance of the models, the accuracy was still too low to serve as an accurate warning tool.

Growing Degree‐Day Models Predict the Performance of Paclobutrazol on Bentgrass Golf Putting Greens

The performance of plant growth regulators (PGRs) in turfgrass is closely related to air temperature. Growing degree‐days (GDD) have been used to predict the performance of a foliar‐absorbed PGR, trinexapac‐ethyl (TE), on creeping bentgrass (Agrostis stolonifera L.). Similar models have not been attempted for paclobutrazol, a root‐absorbed PGR. This research aimed to develop GDD models (base temperature = 0°C) for paclobutrazol applied alone or in combination with TE. Models were developed on creeping bentgrass putting greens in Madison, WI (2009) and near Mead, NE, (2014 and 2015). Paclobutrazol was applied alone or with TE at various rates each year. Clippings were collected several times each week and treatments were reapplied when the PGR's effects expired. Sinewave regression models fitted relative clipping yield to GDD accumulation following the most recent PGR application. Relative clipping yield from all treatments was described by symmetric sinewave regression (adjusted R2: 0.409–0.862). The intensity of clipping yield suppression, expressed by the model amplitude, ranged from 0.293 to 0.621 g g−1, depending on the application rate or the addition of TE. The application rate had less effect on the duration of the growth response, represented by the model period. The estimated reapplication intervals ranged from 269 to 302 GDD. Tripling the application rate lengthened the estimated reapplication interval by <2 d. The addition of TE did not accelerate peak suppression and had a small, inconsistent impact on the model period. Golf course superintendents should use GDD models to schedule reapplications to sustain season‐long yield suppression.

Degree-Days: Growing, Heating, and Cooling

How much and when it rains, freezes, and thaws can make the difference between boom and bust for a year's crop. However, temperature can predict more than boom or bust. Atmospheric temperature can predict the growth rates of many plants. For this reason, growers use a concept called growing degree-days (GDD), sometimes called heat units. This 5-page document discusses growing degree-days, use of the AgroClimate website to track and forecast GDD accumulation, heating and cooling degree-days, and methods for calculating HDD, CDD, and GDD. Written by Clyde W. Fraisse and Silvana V. Paula-Moraes, and published by the UF/IFAS Department of Agricultural and Biological Engineering, revised December 2010 and April 2018. http://edis.ifas.ufl.edu/ae428