Growing Degree Days Research Articles

Emergence timing of smallflower umbrella sedge (Cyperus difformis), barnyardgrass (Echinochloa crus-galli), and bearded sprangletop (Leptochloa fusca spp. fascicularis) in California water-seeded rice

Abstract Late-season weed emergence in California rice fields complicates decisions concerning the timing of control measures. The objective of this study was to predict the emergence of three problematic weed species in rice using thermal time models. Smallflower umbrella sedge, barnyardgrass, and bearded sprangletop seedlings were counted and removed daily at three locations across the Sacramento Valley rice-growing region in 2018. The accumulation of thermal time (growing degree days; GDD) commenced with the initial flooding of the fields at each location, utilizing the specific base temperatures corresponding to each species. The pattern of emergence as a function of GDD was modeled with a Weibull function. Root-mean-square values for comparing actual and model-predicted cumulative emergence values were 6% to 23%. Percent cumulative emergence initially increased rapidly for smallflower umbrella sedge and reached 90% emergence with accumulation of 13 GDD. Barnyardgrass emerged after smallflower umbrella sedge and reached 90% emergence with an accumulation of 124 GDD. Bearded sprangletop had a delay of 64 GDD compared to barnyardgrass to reach first emergence and reached 90% emergence at 215 GDD. The period of weed emergence at all field sites differed across the three species and led to a continuous spectrum of weed emergence over time. This study characterizes the emergence of three economically important rice weeds and provides useful information for the timing of weed management. Typical herbicide applications on the day of seeding may have less efficacy on the late-emerging weeds, causing reduced weed control. Delayed herbicide application, overlay of residual herbicides, or use of herbicides with longer residual activity are suggested to control late-emerging weeds.

Comparative analysis of growth cycles among three weedy Avena species: Insights from field observations.

Avena species, especially A. fatua, A. sterilis and A. ludoviciana, are among the most problematic weed species in many crops worldwide. The growth cycles of these three species could be helpful in understanding their growth cycle and their implications for agriculture and weed management. The growth cycles of these species were studied altogether or in combination with any single or double combinations of the other species in cereal fields in Türkiye, using two populations of each in a common garden experiment in Bornova district, Izmir, Türkiye. Germination and growth experiments were conducted in the laboratory and screen house, respectively. Various phenological parameters were recorded during the experiment and data were analyzed using R software. There were no significant differences in germination, emergence, SPAD values, leaf width, plant height, or plant dry weight among the species or populations. The SPAD values and width of the flag leaf and the leaf before the flag leaf were strongly correlated. Plant weight increased with increasing tiller number. The length of the ligule in a population of A. sterilis was significantly greater than that in populations of two other species, and it was concluded that the species is not A. fatua or A. ludoviciana if the length of the ligule is greater than 10 mm. The length of the spikelets of A. sterilis was greater than 65 mm with awn and greater than 35 mm without awn; these values were significantly greater than those of two other species that were shorter than 55 with awn for A. fatua and 30 mm without awn for A. ludoviciana, respectively. Avena ludoviciana had fewer tillers than the other two species. The plants emerged at 37.58 GDD at the soil surface temperature, which corresponds to 7 days after sowing. The growing cycles of the species differed: 196 days for A. sterilis, 201 days for A. fatua, and 209 days for A. ludoviciana after emergence, although there were no clear differences in earlier growth stages. This study provides initial basic information about the Avena spp., and it is concluded that even if a field has mixed Avena populations, herbicides can be applied simultaneously because the early development stages of the three species are very similar. In future prospects, there is a need for proper studies about the management of all Avena spp. on the basis of growth stages and growing degree days in regional context.

Influence of climatic variables on maize grain yield and its components by adjusting the sowing date.

Yield and its components are greatly affected by climate change. Adjusting the sowing date is an effective way to alleviate adverse effects and adapt to climate change. Aiming to determine the optimal sowing date of summer maize and clarify the contribution of climatic variables to grain yield and its components, a consecutive 4-year field experiment was conducted from 2016 to 2019 with four sowing dates at 10-day intervals from 5 June to 5 July. Analysis of historical meteorological data showed that more solar radiation (SR) was distributed from early June to mid-August, and the maximum temperature (Tmax) > 32°C appeared from early July to late August, which advanced and lasted longer in 1991-2020 relative to 1981-1990. Additionally, the precipitation was mainly distributed from early June to late July. The climate change in the growing season of summer maize resulted in optimal sowing dates ranging from 5 June to 15 June, with higher yields and yield stability, mainly because of the higher kernel number per ear and 1,000-grain weight. The average contribution of kernel number per ear to grain yield was 58.7%, higher than that of 1,000-grain weight (41.3%). Variance partitioning analysis showed that SR in 15 days pre-silking to 15 days post-silking (SS) and silking to harvest (SH) stages significantly contributed to grain yield by 63.1% and 86.4%. The extreme growing degree days (EDD) > 32°C, SR, precipitation, and diurnal temperature range (DTR) contributed 20.6%, 22.9%, 14.5%, and 42.0% to kernel number per ear in the SS stage, respectively. Therefore, we concluded that the early sowing dates could gain high yield and yield stability due to the higher SR in the growing season. Meanwhile, due to the decreasing trend in SR and increasing Tmax trend in this region, in the future, new maize varieties with high-temperature resistance, high light efficiency, shade tolerance, and medium-season traits need to be bred to adapt to climate change and increased grain yield.

Effect of sowing windows on yield, economics and growing degree days on varieties of green gram (Vigna radiata L.)

Effect of sowing windows on yield, economics and growing degree days on varieties of green gram (Vigna radiata L.)

Considerations regarding barley germination

Naturally, germination takes place under the soil surface hidden from the eye of the observer. Therefore, the main goal of the experiment is the understanding of the germination process in correlation with the growing degree days. The experiment is performed on samples of 100 untreated barley seeds placed in Petri dishes, subjected to four fixed temperatures, 5°C, 10°C, 15°C and 20°C, in four incubators, continuously over a period of 6 days. From the 3rd to the 6th day, a sample is taken from each incubator and the following data is collected: the number of germinated seeds, the number of stems, the length of the longest radicle, and the length of the stem. The data analysis shows that the barley seed germination rate reaches at least 50% after reaching 30 degree-days.

Relative and Unified Skill of Environmental, Edaphic, and Management Factors to Explain Crop Yield Variance Using Machine Learning

Highlights Random forests equipped with recursive feature elimination were used to predict crop yield variation using diverse predictors. The predictors belonged to either environmental, soils, or management domains. The best performing models combined predictors from all three domains and explained 75%-80% spatiotemporal yield variance. Extreme heat was most important among soils, management, and seasonal environment predictors in maize and soybean. Irrigation intensity, tile drainage, % silt, and July rain were most important at the sub-seasonal aggregation scale. Abstract. Crop yields are dictated by a complex interplay between environment, edaphic (soils), and management that are subject to change across space and time. However, to what extent each of these influences and their interactions have been important in explaining yield variance is limitedly understood. The convoluted nature of this question motivates the application of modern machine learning approaches to decipher these influences and elucidate crop yield relations with their critical drivers. Here, we used random forest modeling with recursive feature elimination (RFE) to discern the diverse drivers of historical (1981-2019) county-level maize and soybean yields in the U.S. within the realms of environment (growing and extreme degree days, precipitation, vapor pressure deficit, evaporative demand, crop water use, soil moisture), soils (sand, silt, and clay contents, bulk density, soil organic carbon, available water capacity), and management (irrigation intensity, tile drainage, nitrogen input, depth to groundwater). We found that the most effective models selected predictors from all three realms and achieved remarkable explanatory capability, accounting for 75%-80% of the spatial and temporal variance combined. Environmental predictors exhibited a non-negotiable role in determining model performance, while their combination with either soil or management predictors approached the efficacy of the best-performing model. Specific variables within each individual predictor set and their combinations were analyzed for their relative importance to the model skill. The best performing model that used soils, management, and seasonal environmental predictors evaluated extreme heat as the most influential predictor for both crops. On further inclusion of sub-seasonal environmental variables with soil and management predictors, the relative importance shifted, with irrigation intensity assuming prominence as the most influential predictor, accompanied by tile drainage, silt content, and July precipitation for both crops. RFE revealed that only eight of the most relevant predictors were sufficient to explain >70% of the yield variance, even when the total predictors included were as high as 52. that Visual representations were developed to offer insights into the functional response of crop yield to changes in critical predictors, thereby facilitating predictions across diverse agricultural production systems and over time. This research underscores the value of including soil and management indicators alongside environmental predictors to improve understanding and predictability of the intricate dynamics governing crop yield variability. Keywords: Climate variability, Drainage, Irrigation, Maize, Nitrogen, Random forest, Soils, Soybean.

GIS-based Land Suitability Evaluation for Sesame (Sesamum indicum L.) Production using Agro-climatic Indices in the Semi-arid Areas of Tigray

Introduction: Climate is the most important factor in determining crop production systems in Ethiopia. However, land suitability evaluation using climate variables for specific crops has been barely conducted to demonstrate the suitability of a location for the growth and yield of the crop. This study was, therefore, conducted to analyze land suitability evaluation for sesame production using agro-climatic variables by employing a multi-criteria approach. Materials and Methods: Temperature (maximum, minimum, and average temperature), annual rainfall, elevation, slope, growing degree days (GDD), and length of growing period were classified, and re-classified. Finally, a suitability map overlay was produced using ArcMap version 10.3.1 Results and Discussion: The results indicated that areas with higher elevations, like Adi Remets being 1860 meters above sea level, were classified as moderately suitable, whereas the majority of the study area was suitable for sesame production. After re-classifying the climate variables, elevation, and GDD, a suitability map overlay was produced for sesame production. The overall suitability map showed that areas around Humera, Dansha, Sheraro, Badime, Adigoshu, and some parts of Dedebit, Adiremets, and Maygaba were found from highly suitable to suitable, with 1,024,543.2 and 664,545.5 hectares of land, respectively. There has been no evidence with regards to land suitability classification for sesame production in the study area; hence, the results obtained in this study demonstrated the majority of the cultivated areas in the western and northwestern zones of Tigray to be high to moderately suitable for sesame production. Conclusion: Based on the results, a land suitability map representing a homogenous environment for improved sesame production was produced using a multi-criteria approach in the northwestern and western zones of Tigray. While the results obtained in this study might be helpful for agricultural experts, researchers, decision-makers, and farmers for crop intensification and expansion strategies, they can explicitly help in decisions in the marketing processes with respect to the crop’s value chain and provide a homogenous environment in the study area.

In-season forecasting of within-field grain yield from Sentinel-2 time series data

Precise knowledge of cropland productivity is relevant for farmers to enable optimizing managing practices; particularly with the perspective of anticipating crop yield ahead of harvest. The current availability of high spatiotemporal resolution Sentinel-2 satellite data offers a unique opportunity to monitor croplands over time. In this context, the recently introduced kernel NDVI (kNDVI) statistically optimizes the conventional NDVI formulation by applying a nonlinear function to the involved bands, and so maximizes the spectral information extraction. This study proposes a workflow for within-field yield forecasting from Sentinel-2 kNDVI time series analysis focusing on winter cereal croplands in Switzerland over three years, comparing with NDVI as baseline. For a temporally continuous modelling of crop yields, Gaussian Process Regression (GPR) was applied to reconstruct cloud-free time series of the complete crop growing seasons. Following, distinct machine learning regression models (GPR, Kernel Ridge Regression and Random Forest) were developed to forecast yield at any point in time throughout the cropland growing season. The integration of Growing Degree Days (GDD) information as temporal spacing reference of the time series considerably improved the accuracy and consistency of in-season yield forecasting. Training and testing within the same year demonstrated that yield can be accurately forecast approximately 2–2.5 months ahead of harvest, at crops’ anthesis (flowering) phase, with an RMSE up to 0.71 t/ha and a relative RMSE of 7.60%. Although the forecasting accuracy of the models decreased when predicting yield for the unseen years, still satisfactory results were obtained: RMSE = 0.97 t/ha, relative RMSE = 11.47%.

THE EFFECTS OF DIFFERENT SOWING TIMES ON THE PHENOLOGICAL CHARACTERISTICS AND SEED YIELD OF THE PEA

The pea is a legume with a high protein content and high nutritional value. In recent times, it has come to be
 consumed as a frozen and canned food as well as fresh. It has a special place among legumes because of its
 suitability for crop rotation. In Turkey, it is mostly grown as a winter crop in the western regions. Sowing
 takes place in the fall for the winter growing season. Determining the best sowing time is vital to avoid cold
 damage. This study was conducted over two years in 2020-21 and 2021-22 growing seasons in the field crops
 trial fields of Aydin Adnan Menderes University Faculty of Agriculture. During the study, the dry matter
 values and GDD (growing degree days) of four pea cultivars (Giresun, Mayer, Local 1 and Local 2) were
 calculated at four phenological periods (emergence, flowering, pod set and maturity) for three different sowing
 times (November 1, November 20 and December 10). In addition, the yield and yield characteristics and
 protein content were analyzed. Among the four varieties, Mayer (1.95t ha-1) recorded the highest yield value,
 while Giresun (25.6%) achieved the highest protein content. The number of days to maturity, dry matter
 content and GDD values of the varieties at the different phenological periods varied with the sowing time.
 Higher GDD and dry matter content levels were measured in early sowing. The later the sowing, the shorter
 the period from pod set to maturation and the lower the dry matter content.

Effect of Sowing Time Induced Weather Regimes on Growth and Yield of Chickpea under Irrigated Condition

A field experiment conducted to study the effect to find out optimum time of sowing and suitable cultivar for sustainable chickpea production in the region. The study was undertaken to find yield variation with extended sowing date. Five different sowing dates viz., 30th October (44 MW), 15th November (46 MW), 30th November (48 MW), 15th December (50 MW) and 30th December (52 MW) with two chickpea cultivars viz., JAKI 9218 and Vijay were studied. The varieties selected dominated chickpea cultivation in the region. The results revealed that crop sown on 30th October produced higher but statistically equivalent number of pods/plant, number of seeds/plant and grain yield as compared to 15th November sowing. Crop sown on 30th October accumulated higher Growing Degree Days (GDD: 2012 day0C) and Heliothermal Units (HTU: 13515 0C day hr) closely followed by 15th November sown crop (GDD; 1975-day 0C, HTU: 13130 0C day hr) during cropping period from vegetative to pod-seed development phase. The number of pods/plant, weight of seed/plant and test weight was higher in cultivar JAKI 9218 than Vijay. Maximum GDD (1888 0C day) and HTU (12706 0C day hr) were accumulated by cultivar JAKI 9218 closely followed by Vijay (GDD: 1856 0C day; HTU: 12449 0C day hr).

The Critical Period for Weed Control in Safflower Fields of Turkey

Crop-weed competition is among the most significant topics in field crops if the crop is a weak competitor in this rivalry. Safflower is a valuable oil seed crop that is drought tolerant and used as a raw material in the food and biofuel industries. Although it has good drought tolerance, it is a sensitive crop against weed competition, especially wild mustard. The critical period for weed control (CPWC) is a useful tool to decide which time period is more suited for weed control economically. In this study, the beginning and end of the CPWC were determined for the summer safflower fields of Turkey. Weeds caused 50.61, 65.87, and 63.34% safflower yield reductions in 2017, 2018, and 2019, respectively. The beginning of the CPWC was 165, 198, and 225 air growing degree days (air GDD) for these years, while the end of CPWC varied 1348, 1837, and 2268 at 5% acceptable yield loss.

Modeling Plant Height and Biomass Production of Cluster Bean and Sesbania across Diverse Irrigation Qualities in Pakistan’s Thar Desert

Plant height (PH) plays a crucial role in determining per-plant growth and biomass production. Various characteristics of PH, along with the formulation of mathematical growth models, can provide a theoretical yield or biomass production based on water quality, fruit quality, and yields. The aim of this study was to investigate the relationship between PH and biomass per-plant production of two fodder crops (Cluster bean and Sesbania) under different water quality irrigation parameters in the Thar desert of Pakistan. Universal models of PH were established in which growing degree days (GDDs) and different water quality irrigation techniques have been applied as independent variables to calculate the maximum plant height of both of these crops. For this purpose, the logistic growth model, Gaussian growth model, modified Gaussian growth model, and Cubic polynomial growth model were used. Furthermore, universal biomass per plant production models have been developed for both crops, between biomass per plant, GDDs, and PH. However, among all these developed models, the modified Gaussian and Cubic polynomial growth models produced the best results. The Cubic polynomial model has meaningless parameters that make the model not very accurate, so the modified Gaussian growth model is the best among all models. Furthermore, the relationship between biomass per plant and different water qualities was established using Michaelis–Menten equations for both crops. It was observed that an increase in salt concentration within the water quality led to a decline in biomass per plant, indicating a negative linear relationship between these factors. The growth of Cluster bean and Sesbania ceased when the electrical conductivity (EC) reached or exceeded 12.34 ds/m and 11.51 ds/m, respectively. Furthermore, the results show that Cluster bean and Sesbania have the maximum plant height under brackish water irrigation when the GDD is at 1500 °C, while in freshwater irrigation, the maximum plant height of Sesbania and Cluster bean was observed when the GDD is at 1444 °C and 1600 °C. It was concluded that these developed mathematical models can provide crucial insights for enhancing production in desert conditions by improving water use efficiency across diverse irrigation water qualities.

Addressing food insecurity: An exploration of wheat production expansion

Wheat plays a crucial role in global food security, serving as a vital food crop that feeds billions of people worldwide. Currently, Russia and Ukraine are responsible for exporting approximately 25% of the world’s wheat, making any issues in these regions a cause for concern regarding global wheat supply. The problems faced in these areas have led to a surge in wheat prices worldwide. Consequently, it becomes necessary to explore alternative regions that can compensate for the decline in wheat production and supply. This study focuses on wheat production and yield in major producing countries, utilizing the GYGA (Global Yield Gap Atlas) protocol for predictions. The findings reveal a global wheat production gap of 270,378,793 tons. Notably, the largest gap in irrigated wheat production exists in countries like China, India, Pakistan, Turkey, Iran, Afghanistan, Uzbekistan, Egypt, and Azerbaijan. Additionally, the rainfed wheat production gap on a global scale amounts to 545,215,692 tons, with Russia, the USA, Kazakhstan, Australia, Ukraine, China, Turkey, Canada, India, and France having the most significant production gaps. Through boundary line analysis, specific criteria were identified for suitable areas of irrigated and rainfed wheat cultivation. For irrigated conditions, the temperature range of 3000 to 7000 GDD (Growing Degree Days) and a temperature seasonality of 3 were determined as favorable. Under rainfed conditions, the suitable areas encompass a temperature range of 2000 to 4000 GDD, an aridity index exceeding 600, and a temperature seasonality of 2. Thirteen countries possess extensive agricultural land within the climatic codes favorable for irrigated wheat cultivation. Approximately 50% of the agricultural lands within these countries, corresponding to the total arable area for irrigated wheat, fall within the climatic codes 3403, 5403, 5303, 4303, 5503, 5203, 3503, 3303, and 4103. China, the United States, Ukraine, Russia, and Iran are the top five countries with favorable lands for irrigated wheat cultivation. Similarly, fourteen countries have significant agricultural lands within the favorable climatic codes for rainfed wheat cultivation. Around 52% of the agricultural lands within these countries are within the climatic codes 3702, 2702, 2802, and 4602. France, Germany, Britain, Poland, and Denmark possess the highest potential to expand rainfed wheat cultivation areas within these favorable climate codes, with respective areas of 2.7, 2.6, 1.6, and 0.9 million hectares. According to the study, the North China Plain emerges as a primary region for increasing irrigated wheat production, both in terms of cultivated area and yield potential. For rainfed conditions, the European continent stands out as a significant region to enhance wheat production.

Genetic diversity and proximate analysis of Indonesian local mung bean (Vigna radiata)

Abstract. Afza H, Palupi ER, Herlina L, Ilyas S. 2023. Genetic diversity and proximate analysis of Indonesian local mung bean (Vigna radiata). Biodiversitas 24: 6377-6388. Mung bean is an important food crop because it has a high protein content. However, not much information is available about Indonesian local mung beans. This study aimed to obtain information about flowering and seed formation in local mung beans (Vigna radiata L.) to determine the genetic diversity of mung beans using simple sequence repeats and the protein, starch, fat, and ash content. Mung bean seeds of 15 genotypes were obtained from the Indonesian Agricultural Gene Bank collection. The experiment was arranged using a one-factor randomized complete block design. Genotypes with a low degree of non-synchrony of pod maturity (DDd) were Local Bima and Arta Koneng 01070. The highest fruit set was observed in the Madura Local accession (73.09%). The accumulated value of heat units was calculated as growing degree days; the heat units from planting to the first flowering ranged from 544.6-670°C and from planting to the first ripe pod (heatd2) and 90% ripe pod (heatd3) phases ranged from 819.7-933.7°C and 1,111.9-1,419.1°C. Phylogenetic tree construction was made based on scoring of the presence of alleles that appeared on the electrophoresis results of 12 microsatellite markers on 15 mung bean genotypes. The superior varieties, Vima1 and Walet, were separated from the other 13 local genotypes at a genetic similarity coefficient of 0.583. The protein contents of the tested mung bean genotypes were 19.19%-23.06%.

The Goldilocks Zone for maize agriculture and the settlement and abandonment of the West Tavaputs Plateau

Between 2100 and 500 BP, maize farming spread throughout the American Southwest, driving changes in land use patterns and relative increases in the number of archaeological sites, which is commonly interpreted as increases in population. In the context of the Southwest, with its variable topography and limited water sources, the locations suitable for productive maize agriculture are limited. Higher elevation locations often have a greater abundance of water, both in the form of precipitation and perennial water sources but lack the growing season lengths required for maize farming. Lower elevation locations have the long growing seasons required for maize, but often have higher evaporation rates corresponding with higher temperatures, and thus decreased soil moisture retention. The relationship between elevation and maize agriculture is well recognized, and locations where both growing season length and moisture requirements can be met serve as a Goldilocks Zone in which maize farming is possible. Here, we use a regional approach to model the Goldilocks Zone for maize farming in Utah, and then contextualize patterns of settlement and abandonment on the West Tavaputs Plateau (WTP) in east-central Utah. We use archaeological sites with direct evidence of maize cultivation, modern growing degree days estimates, and modern estimates of moisture abundance to bracket the prehistoric maize farming niche in Utah. Then, we apply the model to archaeological data from the WTP to test hypotheses derived from the Ideal Free Distribution model about the effects population and precipitation changes had on prehistoric land use patterns of settlement and abandonment, and whether the patterns of settlement and abandonment differ from one another. We find that a) population drives land use patterns in the WTP, but not in the way we originally hypothesized, suggesting there is an important decision variable missing from our model, which may pertain to the specific topographic setting of the WTP and the distribution of its water sources, b) precipitation has no effect on land use patterns suggesting that maize agriculture on the WTP was dependent on irrigation, and c) that patterns of settlement and abandonment seem to mirror each other.

Development and validation of phenological models for eight varieties of sweet cherry (Prunus avium L.) growing under Mediterranean climate condition

The projected increase in temperatures caused by climate change will modify the phenological stages of sweet cherry in Mediterranean-type climate regions. This study aimed to develop and validate phenology models for eight sweet cherry varieties (Sam, Lapins, Rainier, Royal D, Santina, Regina, Kordia, Sweet Heart) in the Maule Region, Chile. The models were based on growing degree days (GDD), and the stochastic monomolecular model was employed to account for the randomness caused by measurement errors affecting the prediction of phenological states. A dataset of phenological stages was compiled over four growing seasons: 2017-2018, 2018-2019, 2019-2020, and 2020-2021. The results revealed a significant correlation (with R2 values between 0.96 and 0.98) the modified phenological (PS) scale of Biologische Bundesanstalt, Bundessortenamt, and Chemical Industry (BBCH) and GDD for all eight varieties. The validation of the model demonstrated root mean square error (RMSE), mean absolute error (MAE), and model efficiency (FE) values ranging from 1 to 1.3, 0.79 to 1.1, and 0.82 to 0.92, respectively. Finally, monomolecular model estimated the modified scale of BBCH with errors less than 2 %.

Lake Superior's summer cooling of shorelines and adjacent inland forests: Implications for refugia of boreal forests and disjunct arctic-alpine plants.

Climate refugia can serve as remnant habitat for cold-adapted species and delay forest transitions. The world's largest freshwater lake by surface area, Lake Superior, serves as a model system for understanding cooling-mediated refugia effects, as its cool summer water temperatures have maintained disjunct populations of arctic-alpine plants on its shoreline since deglaciation. It is known to affect local inland climates by providing a summer cooling effect; however, the inland temperature gradient and spatial patterns of cooling have not been well quantified. Here, we describe the extent, degree, and patterns of temperature buffering and examine drivers of buffering and disjunct plant occurrence for Lake Superior's north shore over a 3-year period at distances of 10, 100 m, 1, 10, and 100 km inland. We analyzed temperature data by year, month, summer maximum (July), and growing degree days (GDD0) for each site. Average summertime cooling at shore sites (10 m) was ~5°C cooler than reference sites (100 km inland), with a maximum difference of -19.2°C. The magnitude of cooling varied geographically, with sites further west and southeast showing little to no cooling effect, while the exposed north-central shore showed the highest degree of buffering (5.8°C cooler) and had a shorter growing season than reference sites. Finally, north-central shorelines had fewer days above 16°C, a threshold above which disjunct plants are unlikely to grow. These sites also showed the highest proportion of disjunct arctic-alpine species, reflecting the highest buffering from inland sites. On north-central shores, sites up to 10 km inland had less than 10 days per year warmer than 20°C, a threshold identified for boreal forest transition. An understanding of the extent of lake-mediated cooling on adjacent forests can better inform the risk to disjunct species, inland forests, and vegetation transition models on Lake Superior's north shore.

Genetic Diversity and Genome-Wide Association Study for the Phenology Response of Winter Wheats of North America, Western Asia, and Europe

Wheat is a staple food in many areas around the World. In the 20th century, breeders and scientists were able to boost wheat yield considerably. However, a yield plateau has become a concern and is threatening food security. Investments in cutting-edge technologies, including genomics and precision phenology measurements, can provide valuable tools to drive crop improvement. The objectives of this study were to (i) investigate the genetic diversity in a set of winter wheat lines, (ii) characterize their phenological response under different vernalization and photoperiod conditions, and (iii) identify effective markers associated with the phenological traits. A total of 249 adapted genotypes of different geographical origin were genotyped using the 35K Axiom® Wheat Breeder’s Array. A total of 11,476 SNPs were used for genetic analysis. The set showed an average polymorphism information content of 0.37 and a genetic diversity of 0.43. A population structure analysis revealed three distinct subpopulations mainly related to their geographical origin (Europe, North America, and Western Asia). The lines of CGIAR origin showed the largest diversity and the lowest genetic distance to all other subpopulations. The phenology of the set was studied under controlled conditions using four combinations of long (19 h light) and short photoperiod (13 h light) and long vernalization (49 days at 5 °C) and no vernalization. With this, phenological traits such as earliness per se (Eps), relative response to vernalization (RRV), and relative response to photoperiod (RRP) were calculated. The phenotypic variation of growing degree days was significant in all phenology combinations. RRV ranged from 0 to 0.56, while RRP was higher with an overall average of 0.25. The GWAS analysis detected 30 marker-trait associations linked to five phenological traits. The highest significant marker was detected on chromosome 2D with a value of −log10(p) = 11.69. Only four loci known to regulate flowering exceeded the Bonferroni correction threshold of −log10(p) > 5.1. These results outline a solid foundation to address global food security and offer tremendous opportunities for advancing crop improvement strategies.

How have thermal conditions changed in different phenological stages of apple (Malus domestica) in Northeastern Hungary?

In temperate climates, most fruit trees need cold weather, low temperatures in winter, and a certain amount of heat during the growing season until harvest. One of the most apparent effects of climate change is the elevated temperature in all seasons of the year. In our study, the changes in thermal conditions have been calculated in Hungary's most significant growing region of apples using the Chill Unit for winters and the Growing Degree Days for summers. The meteorological data were obtained from the gridded dataset of the Hungarian Meteorological Service on a 10 km × 10 km grid, so the whole studied area is well-covered over the last 50 years. The results show that the trees are more exposed to early budding than a few decades ago. Furthermore, the accumulated heat amount in summers has increased drastically, which may increase the heat stress and lead to higher yield losses.

Phenology, heat unit requirement and heat use efficiency of African marigold under year-round transplanting conditions of Punjab, India

The present study was conducted during 2021-22 and 2022-23 to study crop phenology, heat unit requirement and heat use efficiency (HUE) in African marigold under year-round transplanting at Punjab Agricultural University, Ludhiana, Punjab, India. The highest heat use efficiency for seed and flower production were recorded in crop transplanted in rainy season particularly in the month of August. Higher growing degree days (GDD) and photothermal units (PTU) for attaining bud visibility and 50 % flowering stages were recorded under spring and summer transplanting (February to June) due to longer day length conditions which forced the crop to witness shorter seed filling period with lesser seed yield and HUE. Optimum seed yield period in marigold (rainy season transplanting), however, recorded lesser GDD and PTU for attainment of bud visibility and 50 % flowering as compared to summer transplanting dates and consequently had longer seed filling period resulting in higher seed yield. Notably, GDD and PTU for seed filling period recorded higher values in rainy season transplanted crop as compared to crop transplanted in other seasons of year. Correlation studies were conducted to understand the role of weather variables for high seed yield obtained under rainy season transplanting. Seed yield in African marigold recorded the highest value of correlation with HUE for seed production (r2= 0.978) followed by GDD for seed filling period (r2= 0.810), HUE for flower production (r2= 0.787) and PTU for seed filling period (r2= 0.774), respectively. Apparently, mean temperature and sunshine hours during seed filling period are the most important determinants of seed yield in African marigold.