Cumulative Growing Degree Days Research Articles

Influence of cover crop residue and residual herbicide on emergence dynamics of glyphosate-resistant Palmer amaranth (Amaranthus palmeri) in grain sorghum

Abstract A field study was conducted from 2020 to 2023 at Kansas State University Agricultural Research Center near Hays, KS, to understand the emergence dynamics and periodicity of glyphosate-resistant (GR) Palmer amaranth (Amaranthus palmeri S. Watson) as influenced by cover crop (CC) residue and residual herbicide in grain sorghum [Sorghum bicolor (L.) Moench]. The study site was under a wheat (Triticum aestivum L.)–sorghum–fallow rotation with a natural seedbank of GR A. palmeri. Treatments included (1) fall-planted CC mixture [winter triticale (×Triticosecale Wittm. ex A. Camus [Secale × Triticum])/winter peas (Pisum sativum L.)/rapeseed (Brassica napus L.)/radish (Raphanus sativus L.)] after wheat harvest and terminated at triticale heading stage (next spring before sorghum planting) with glyphosate alone or (2) glyphosate plus acetochlor/atrazine, (3) chemical fallow (no CC but treated with acetochlor/atrazine and dicamba before sorghum planting), and (4) nontreated control (no CC and no herbicide). Results indicated that CC terminated with glyphosate plus acetochlor/atrazine had a delayed and reduced cumulative emergence of GR A. palmeri as compared with chemical fallow and CC terminated with glyphosate alone across all 3 yr. Compared with chemical fallow, the CC terminated with glyphosate alone and glyphosate plus acetochlor/atrazine required 66 to 643 and 105 to 1,257 more cumulative growing degree days, respectively, to achieve 90% cumulative emergence of GR A. palmeri across all 3 yr. The combined effect of CC residue with glyphosate plus acetochlor/atrazine reduced the total emergence counts of GR A. palmeri by 42% to 56% and 82% to 94% as compared with chemical fallow and nontreated control, respectively. These results suggest that fall-planted CC combined with a residual herbicide at termination can be utilized for GR A. palmeri suppression in grain sorghum.

Determining the Timing of Spore Release by Botryosphaeriaceae Species in Oregon Vineyards.

Botryosphaeria dieback, caused by a group of pathogens in the Botryosphaeriaceae family, is one of the most common grapevine trunk disease complexes (GTDs) found in Oregon vineyards. To understand the period of spores released by Botryosphaeria spp. in Oregon vineyards, four Burkard 7-day recording volumetric spore traps were placed in vineyard blocks in northern and southern Oregon. Each trap was placed near a younger (<10 years) and older (>30 years) block in both regions. Spore traps were deployed at the beginning of December 2019 and continued until March 2021. Between these timeframes, 475 and 477 days of samples were collected from each spore trap in northern and southern Oregon, respectively. DNA extraction was performed from individual day samples and followed by qPCR analysis of Botryosphaeria spores trapped in each tape sample. Weather data such as temperature, precipitation, relative humidity (RH), and wind speed were collected from nearby weather stations. Association between these data and number of spores detected were analyzed using Pearson correlation analysis. In northern Oregon, the detection occurred between December and February, and the first spore detection occurred when cumulative growing degree day (GDD) totaled to 4,357 and 4,351 units (TBase = 0°C, biofix date = January 1) during the first and second seasons, respectively. Similarly, in southern Oregon, the detection occurred between November and January, and the first spore detection occurred when cumulative GDD was 4,405 units during the second season. Hours of continuous RH >86% was significantly associated with number of spores released (P = 0.026; r = 0.42). During the spore detected dates, the RH was >86% for at least 19 consecutive hours. These findings provide an important implication to manage Botryosphaeria dieback by protecting pruning wounds during the most-spore-released periods. Furthermore, the knowledge of weather variables and their possible association with spore detection provides important information towards developing predictive models in future studies.

Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology

This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving a yield prediction model with cumulative growing degree days (CGDD) and days from sowing (DFS) data. The study area was located in Mosonmagyaróvár, Hungary. A small-scale field trial in winter wheat was constructed as a randomized block design including Environmental: N-135.3, P2O5-77.5, K2O-0; Balance: N-135.1, P2O5-91, K2O-0; Genezis: N-135, P2O5-75, K2O-45; and Control: N, P, K 0 kg/ha. The crop growth was monitored every second week between April and June 2022 and 2023, respectively. NDVI measurements recorded by GreenSeeker were taken at three pre-defined GPS points for each plot; NDVI values based on the MicaSense camera Red and NIR bands were calculated for the same points. Results showed a significant difference (p ≤ 0.05) between the Control and treated areas by GreenSeeker measurements and Micasense-based calculated NDVI values throughout the growing season, except for the heading stage. At the heading stage, significant differences could be measured by GreenSeeker. However, remotely sensed images did not show significant differences between the treated and Control parcels. Nevertheless, both sensors were found suitable for yield prediction, and 226 DAS was the most appropriate date for predicting winter wheat’s yield in treated plots based on NDVI values and meteorological data.

Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar.

Global warming has been implicated in widespread demographic changes in Atlantic salmon Salmo salar populations, but projections of life-history responses to future climate change are lacking. Here, we first exploit multiple decades of climate and biological data from the Burrishoole catchment in the west of Ireland to model statistical relationships between atmospheric variables, water temperature, and freshwater growth of juvenile Atlantic salmon. We then use this information to project potential changes in juvenile growth and life-history scheduling under three shared socioeconomic pathway and representative concentration pathway scenarios from 1961 to 2100, based on an ensemble of five climate models. Historical water temperatures were well predicted with a recurrent neural network, using observation-based atmospheric forcing data. Length-at-age was in turn also well predicted by cumulative growing degree days calculated from these water temperatures. Most juveniles in the Burrishoole population migrated to sea as 2-year-old smolts, but our future projections indicate that the system should start producing a greater proportion of 1-year-old smolts, as increasingly more juveniles cross a size-based threshold in their first summer for smoltification the following spring. Those failing to cross the size-based threshold will instead become 2-year-old smolts, but at a larger length relative to 2-year-old smolts observed currently, owing to greater overall freshwater growth opportunity. These changes in age- and size-at-seaward migration could have cascading effects on age- and size-at-maturity and reproductive output. Consequently, the seemingly small changes that our results demonstrate have the potential to cause significant shifts in population dynamics over the full life cycle. This workflow is highly applicable across the range of the Atlantic salmon, as well as to other anadromous species, as it uses openly accessible climate data and a length-at-age model with minimal input requirements, fostering improved general understanding of phenotypic and demographic responses to climate change and management implications.

Life outside the fishbowl: Tracking an introduced population of goldfish (Carassius auratus) in an embayment on the Laurentian Great Lakes

Invasive species are a key threat to biodiversity in freshwater ecosystems; and, as such, their management can be an important part of ecosystem restoration and conservation. In Hamilton Harbour, an impaired Lake Ontario embayment, invasive species are an important threat. In the early 2000s, an increase in invasive goldfish (Carassius auratus) was identified during monitoring surveys. This population of goldfish was studied via acoustic telemetry to gain knowledge about its biology to support management control options. Hamilton Harbour goldfish exhibited seasonal patterns in site and depth use, and clear preferences for over-wintering and spawning areas. Goldfish were largely resident to Hamilton Harbour with the exception of one individual. To predict when goldfish would move into spawning areas, we examined goldfish presence, abundance, and temperature at a fish-barrier connected to the harbour and developed models to predict goldfish presence based on Cumulative Growing Degree Days (CGDD) and Day of Year (DOY). Goldfish were captured in large numbers (>100 day−1) at the fishway when CGDD > 25.0 and DOY > 100; therefore, we predicted that our tagged fish would move to spawning areas when these thresholds were reached. Both models accurately predicted when tagged fish moved to spawning areas which largely occurred when water temperatures were lower (≥ 9.7 °C) than thresholds previously identified in the literature (i.e., 15.0 °C − 22.0 °C). This suggested that pre-spawn/staging behaviour was detected using telemetry driven by factors including water temperature. Results from this work will inform control strategies for goldfish, including active removal during aggregation prior to spawning.

Effects of Combined Application of Organic Fertilizer on the Growth and Yield of Pakchoi under Different Irrigation Water Types

The long-term utilization of inorganic fertilizers in pakchoi cultivation can result in increased nitrate levels, potentially posing health risks to human consumers. For this study, we investigated the efficacy of organic fertilizers as a promising alternative for enhancing soil structure, improving fertility, and increasing the yield of pakchoi. A two-year field trial was conducted from 2022 to 2023 to examine the effects of the combined application of organic fertilizer on the growth and yield of pakchoi. Three types of irrigation water, namely fresh water (F), brackish water (B), and magnetized–ionized brackish water (MIB), were used in combination with five different organic fertilizer rates (0, 20, 40, 60, and 80 kg/ha, denoted as 0, 1, 2, 3, and 4). The results revealed that treatments F2, F3, B2, B3, and MIB3 significantly improved the growth indexes of pakchoi. Notably, treatments F3, B3, and MIB3 resulted in an earlier onset of the fast growth period for leaf area index and fresh weight. During this period, we observed the highest cumulative growing degree days (ΔCGDD) values, which were 628.36 °C for plant height (MIB4), 475.01 °C for leaf area index (B3), 259.73 °C for fresh weight (B3), and 416.82 °C for dry matter accumulation (B3). The logistic model indicated an increase in eigenvalue at an organic fertilizer application rate of 60 kg/ha, while excessive fertilization had inhibitory effects. Under brackish water irrigation, both plant height and leaf area index demonstrated significant positive effects on yield, with plant height having a particularly noteworthy direct effect at a coefficient of 0.935. MIB water irrigation demonstrated superior advantages for promoting pakchoi growth, leading to significantly higher rates of fresh weight and dry matter accumulation compared to traditional brackish water irrigation. The maximum value of each growth index exerted a significant direct influence on its respective growth parameter, whereas ΔCGDD demonstrated a relatively smaller or potentially negative effect. Applying organic fertilizer appropriately can assist in the production of pakchoi and provide a scientific basis for increasing yield.

Predicting corn tiller development in restrictive environments can be achieved to enhance defensive management decision tools for producers.

While globally appreciated for reliable, intensification-friendly phenotypes, modern corn (Zea mays L.) genotypes retain crop plasticity potential. For example, weather and heterogeneous field conditions can overcome phenotype uniformity and facilitate tiller expression. Such plasticity may be of interest in restrictive or otherwise variable environments around the world, where corn production is steadily expanding. No substantial effort has been made in available literature to predict tiller development in field scenarios, which could provide insight on corn plasticity capabilities and drivers. Therefore, the objectives of this investigation are as follows: 1) identify environment, management, or combinations of these factors key to accurately predict tiller density dynamics in corn; and 2) test outof-season prediction accuracy for identified factors. Replicated field trials were conducted in 17 diverse site-years in Kansas (United States) during the 2019, 2020, and 2021 seasons. Two modern corn genotypes were evaluated with target plant densities of 25000, 42000, and 60000 plants ha -1. Environmental, phenological, and morphological data were recorded and evaluated with generalized additive models. Plant density interactions with cumulative growing degree days, photothermal quotient, mean minimum and maximum daily temperatures, cumulative vapor pressure deficit, soil nitrate, and soil phosphorus were identified as important predictive factors of tiller density. Many of these factors had stark non-limiting thresholds. Factors impacting growth rates and photosynthesis (specifically vapor pressure deficit and maximum temperatures) were most sensitive to changes in plant density. Out-of-season prediction errors were seasonally variable, highlighting model limitations due to training datasets. This study demonstrates that tillering is a predictable plasticity mechanism in corn, and therefore could be incorporated into decision tools for restrictive growing regions. While useful for diagnostics, these models are limited in forecast utility and should be coupled with appropriate decision theory and risk assessments for producers in climatically and socioeconomically vulnerable environments.

Germination Pattern and Seed Longevity of Echinochloa colona (L.) Link in Eastern Australia

Knowledge of the germination pattern and seed longevity of Echinochloa colona under field conditions could play a crucial role in effective weed management. Germination patterns of four populations (B17/12, B17/13, B17/7, and B17/25) of E. colona collected from eastern Australia were studied under field conditions for two years. Each population had multiple cohorts, and peak germination occurred in December 2018. Population B17/12 had a lower dormancy level compared with other populations, as 87% of the seeds germinated in the first cohort. Regression analysis revealed that populations B17/12, B17/13, B17/7, and B17/25 required 2130, 3110, 4320, and 6040 cumulative growing degree days (GDD), respectively, for 80% germination. The last cohorts of each population (100% germination) were observed in February 2020. This suggests that the populations of E. colona had innate dormancy, and a proportion of seeds can germinate in the next season. For the seed persistence study, seed bags of three populations (B17/4, B17/26, and B17/35) were exhumed at different intervals at two locations (Gatton and St George) over 30 months to evaluate decay components. Averaged over locations, burial duration, and burial depth, populations behaved similarly for the seed decay component. After 30 months of seed placement at Gatton, viable seeds at 1, 5, and 15 cm burial depths were 8, 26, and 15%, respectively. However, during the same time period at St George, viable seeds at 1, 5, and 15 cm burial depths were 0, 4, and 3%, respectively. These results implied that E. colona seeds persisted for a longer period (&gt;2 years) in the light-textured soil (Gatton), particularly at the 5 cm burial depth. After 30 months of seed placement at Gatton, seeds decayed faster at 1 cm compared with the 5 cm burial depth. The studies demonstrated that seed persistence and germination patterns of E. colona may vary with different soil and agro-climatic conditions. The results suggest that management strategies should be followed to enable early control of E. colona over a three year period and that restricting reinfestation of weed seeds through seed rains could lead to almost complete control of E. colona in the field.

A new regional cotton growth model based on reference crop evapotranspiration for predicting growth processes

Meteorological conditions and irrigation amounts are key factors that affect crop growth processes. Typically, crop growth and development are modeled as a function of time or growing degree days (GDD). Although the most important component of GDD is temperature, it can vary significantly year to year while also gradually shifting due to climate changes. However, cotton is highly sensitive to various meteorological factors, and reference crop evapotranspiration (ETO) integrates the primary meteorological factors responsible for global dryland extension and aridity changes. This paper constructs a cotton growth model using ETO, which improves the accuracy of crop growth simulation. Two cotton growth models based on the logistic model established using GDD or ETO as independent factors are evaluated in this paper. Additionally, this paper examines mathematical models that relate irrigation amount and irrigation water utilization efficiency (IWUE) to the maximum leaf area index (LAImax) and cotton yield, revealing some key findings. First, the model using cumulative reference crop evapotranspiration (CETO) as the independent variable is more accurate than the one using cumulative growing degree days. To better reflect the effects of meteorological conditions on cotton growth, this paper recommends using CETO as the independent variable to establish cotton growth models. Secondly, the maximum cotton yield is 7171.7 kg/ha when LAImax is 6.043 cm2/cm2, the corresponding required irrigation amount is 518.793 mm, and IWUE is 21.153 kg/(ha·mm). Future studies should consider multiple associated meteorological factors and use ETO crop growth models to simulate and predict crop growth and yield.

Reproductive phenology and behaviour of endangered redside dace (Clinostomus elongatus) in urban streams.

Investigation of the reproductive phenology and spawning behaviour of imperilled species in relation to environmental variability is needed to understand a critical component of species life history. In this study, we used redside dace (Clinostomus elongatus), a freshwater leuciscid listed as Endangered under Canada's Species at Risk Act, to model spawning phenology and make predictions about spawning initiation using historical and climate change projected thermal cues (measured as cumulative growing degree days), and provide an ethological description of spawning behaviour. Logistic regression models applied to 4 years of average daily stream water temperature data and field behavioural observations of the onset of spawning activity indicated a 50% probability of spawning initiation when cumulative growing degree days reached 214°C days and a 95% probability of spawning initiation at 288°C days. Using two climate change scenarios (i.e., a mid-century 1.6°C increase and an end of century 3.6°C increase), spawning initiation was predicted to advance 3 days by the year 2050 and 7 days by the year 2100. Underwater video cameras placed at two sites within an urban stream captured 73 unique spawning events revealing that redside dace spawn in pairs as well as in dense, tightly packed groups (more than 20 individuals). Moreover, there is evidence of redside dace having a polygynandrous mating system, as female redside dace spawned with multiple males in 45.2% of the total spawning events recorded. Taken together, this study provides important insights into redside dace spawning initiation and behaviour, key life-history traits having conservation implications for future reproductive success and, ultimately, population dynamics.

Phenologies of Canada thistle (Cirsium arvense) and Cirsium species native to the upper Midwest: implications for the ecological host range of the biocontrol agent Hadroplontus litura

AbstractNative Cirsium species play an important role in landscapes across North America. Hadroplontus litura (F.) (formerly Ceutorhynchus litura), the stem-mining weevil and biological control agent of Canada thistle [Cirsium arvense (L.) Scop.] can complete its life cycle on five Cirsium species native to the upper Midwest. Although these five Cirsium species are within the fundamental host range of H. litura, as determined by host-range tests, we wanted to explore whether phenological differences among Cirsium species help define the field ecological host range of H. litura. The objective of this study was to determine the phenology of Cirsium species native to the upper Midwest in relation to C. arvense and H. litura. Our goal was to explore whether shoots of native Cirsium species could escape H. litura shoot oviposition in spring due to delayed shoot emergence relative to C. arvense. Soil cumulative growing degree days (GDD) were a superior predictor of shoot emergence for perennial Cirsium species or initiation of leaves in biennial Cirsium species, with a 2.4 times larger effect on time to emergence relative to air GDD. All native Cirsium species initiated new leaves or shoots before C. arvense shoot emergence, even when native Cirsium species growth was delayed in the spring. In turn, C. arvense shoots emerged approximately 1 to 3 wk before female H. litura began to lay eggs. As such, all native Cirsium plants had shoots available for H. litura oviposition. There was no phenological separation between native Cirsium and C. arvense shoot emergence or initiation that would render native Cirsium species safe from H. litura attack. Based on the phenology of shoot emergence or initiation in the spring, all tested Cirsium species native to the upper Midwest would be within the ecological host range of H. litura.

Evaluation of artificial intelligence algorithms with sensor data assimilation in estimating crop evapotranspiration and crop water stress index for irrigation water management

Irrigation water management using automated irrigation decision support system (IDSS) as a smart irrigation scheduling tool can improve water use efficiency and crop production, especially under circumstances of limited water supply. The current study evaluated the performance of different artificial intelligence (AI) algorithms and their ensembles in forecasting Crop Evapotranspiration (ETc) and Crop Water Stress Index (CWSI) against calculated single crop coefficient FAO56 ETc and Jackson's theoretical CWSI, respectively. Soil moisture, canopy temperatures (Tc) and Normalized Difference Vegetation Index (NDVI) were all measured from irrigated and non-irrigated maize plots in West Central Nebraska during 2020 and 2021 growing seasons. There were fifteen and twelve input combinations used for ETc and CWSI predictions, respectively, having input variables such as weather and soil moisture as well as ancillary variables, including NDVI, reference evapotranspiration (ETr), and cumulative growing degree days (CGDDs). While evaluating the models, four statistical performance indicators including coefficient of determination (r2), root mean square error (RMSE), mean absoluter error (MAE), and mean absolute percentage error (MAPE) were used. Furthermore, ranking scores were performed on statistical results to find the overall best model across all the input combinations. Based on total ranking scores, CatBoost (RMSE ranging between 0.06 – 0.09 unitless) was the best model in predicting CWSI, while Stacked Regression (RMSE ranging between 0.27 – 0.72 mm d−1) was the best model for ETc estimation. Future research will consider designing and evaluating an IDSS using identified best machine learning models to establish soil water and plant stress feedback for automated irrigation scheduling.

Chickpea Phenology and Yield Related to Agrometeorological Indices Under Different Temperature Regimes

Agrometeorological variables have a major impact on crop development and yield, known as crop phenology. The phenology of chickpea under various thermal regimes was evaluated in a field experiment during the rabi season of 2019–2020 at Ishwardi, Pabna district of Bangladesh. Three chickpea cultivars (V1 = BARI Chola 5, V2 = BARI Chola 10, and V3 = BARI Chola 11) were planted under irrigation in four temperature regimes (D1 = November 5, D2 = November 20, D3 = December 5, and D4 = December 20). Early sowing on November 5 had a larger growing degree day (GDD), 2135.5 C days from sowing to harvest, and later sowing on December 20 had a lower GDD (1743.4 C days). From planting to harvest, V1 required a greater GDD (1971.5C days) while V3 required a lower GDD (1805.8 C days). Average heliothermal unit (HTU) varied from 11764.07 C day hrs (D4) to 13879.4 C day hrs (D1). Variety V1 required 13757.85°C day hrs HTU, while V2 and V3 required 13347.05°C day hrs and 11636.8 °C day hrs, respectively. The number of days needed from planting to harvest under D1, D2, D3, and D4 growing conditions were 136.3, 122.6, 118.6, and 107.4 days, respectively. Growth and yield were better for V1 and V2 under D1 settings, but they were lower under D3 conditions, while V3 was better under D2 conditions. BARI Chola 11 among the genotypes was harvested much more quickly and produced more effectively in late-sowing conditions. Physiological maturity and grain yield can be predicted with 92% and 99% accuracy, respectively, using cumulative GDD and HTU. Therefore, the study recommended that different thermal conditions and cultivars' reactions to heat utilization and economic yield have a substantial impact on chickpea cultivar yields.

Optimal soybean maturity group selection is influenced by planting date in northern production systems

Changing climate conditions have resulted in a shift towards longer growing seasons in Michigan and other northern U.S. states, providing soybean [Glycine Max (L.) Merr.] growers an opportunity to achieve early planting dates (PDs). However, increasingly variable spring precipitation may result in a wide range of PDs. Current soybean maturity group (MG) recommendations are based only on optimal PD and may not fully utilize the available growing season. To determine how MG selection should be adjusted based on PD, six varieties ranging from MG 1.0 to 3.5 were planted on four PDs (from late April to late June) during the 2018 and 2019 growing seasons at two locations in Michigan. Seed yield increased by an average of 286, 171, and 73.0 kg ha−1 for each 0.5-MG increase when planting was conducted on day of year (DOY) 120, 140, and 160, respectively. When planting was delayed to DOY 180, there was a yield reduction of 51.4 kg ha−1 for each 0.5-MG increase. The increase in yield from using a late MG in early planting was correlated with an increase in seeds m−2 which was mainly driven by a consistent increase in cumulative growing degree days during pod and seed set. Results from this study indicate that soybean growers can increase seed yield and profitability by adjusting MG selection based on PD such that the maximum available growing season is utilized. Specifically, using a late MG when planting early and an early MG in late-season planting can maximize yield and profits.

Climate change effects on bread wheat phenology and grain quality: A case study in the north of Italy.

Increasing temperatures, heat waves, and reduction of annual precipitation are all the expressions of climate change (CC), strongly affecting bread wheat (Triticum aestivum L.) grain yield in Southern Europe. Being temperature the major driving force of plants’ phenological development, these variations also have effects on wheat phenology, with possible consequences on grain quality, and gluten protein accumulation. Here, through a case study in the Bolognese Plain (North of Italy), we assessed the effects of CC in the area, the impacts on bread wheat phenological development, and the consequences on grain gluten quality. The increasing trend in mean annual air temperature in the area since 1952 was significant, with a breakpoint identified in 1989, rising from 12.7 to 14.1°C, accompanied by the signals of increasing aridity, i.e., increase in water table depth. Bread wheat phenological development was compared in two 15-year periods before and after the breakpoint, i.e., 1952–1966 (past period), and 2006–2020 (present period), the latest characterized by aridity and increased temperatures. A significant shortening of the chronological time necessary to reach the main phenological phases was observed for the present period compared to the past period, finally shortening the whole life cycle. This reduction, as well as the higher temperature regime, affected gluten accumulation during the grain-filling process, as emerged analyzing gluten composition in grain samples of the same variety harvested in the area both before and after the breakpoint in temperature. In particular, the proportion of gluten polymers (i.e., gliadins, high and low molecular weight glutenins, and their ratio) showed a strong and significant correlation with cumulative growing degree days (CGDDs) accumulated during the grain filling. Higher CGDD values during the period, typical of CC in Southern Europe, accounting for higher temperature and faster grain filling, correlated with gliadins, high molecular weight glutenins, and their proportion with low molecular weight glutenins. In summary, herein reported, data might contribute to assessing the effects of CC on wheat phenology and quality, representing a tool for both predictive purposes and decision supporting systems for farmers, as well as can guide future breeding choices for varietal innovation.

Leaf area and its impact in yield and quality of greenhouse tomato (Solanum lycopersicum L.)

This study aimed to estimate the tomato leaf area index (LAI) by comparing two methods (destructive and interception of photosynthetically active radiation) and the consequent relationship to fruit yield and quality. The experiment was carried out in a greenhouse with tezontle (red volcanic scoria) as the substrate and a drip irrigation system. The experiment consisted of three treatments: T1, T2 and T3, with one, two and three stems per plant, respectively. The LAI was measured with a ceptometer that estimates the intercepted radiation above and below the canopy. Maximum LAI was found at 1413 cumulative growing degree days (CGDD). Those indexes were 3.69, 5.27 and 6.16 for T1, T2 and T3, respectively. Individual correlation models were fitted linearly between the two methods. The R2 values were 0.98, 0.99 and 0.99 with yields of 20, 18 and 17 kg m-2 for T1, T2 and T3, respectively. In addition, T1 produced better fruit size quality with approximately 69, 23 and 8% classified as first, second and third class, respectively. Only 1% was classified as a small fruit. Increasing the number of stems per plant increased the LAI and fruit number but decreased fruit size. Highlights: The leaf area index (LAI) is a very important variable for growth and development of crops. The ceptometer proved to be a fast, useful and statistically reliable method to estimate LAI. The increase of photosynthetically active radiation favors the photosynthetic efficiency per unit area. The number of stems per plant increase the LAI, dry matter accumulation, plant height and the number of fruits. However, the size of fruit decreases.

Effects of seeding date on grain and biomass yield of intermediate wheatgrass

AbstractIntermediate wheatgrass (Thinopyrum intermedium) (IWG) is a perennial grass being domesticated for grain production with potential to provide economic return and ecosystem services across a broad geographic range in North America, yet optimum seeding dates for grain and biomass yield are unknown. Our objective was to determine the effect of late‐summer, fall, and spring seeding dates on grain and biomass yield of a grain‐type IWG population. Trials were conducted at St. Paul and Roseau, MN, Kalispell, MT, and Salina, KS. Seeding dates ranged from August to June of the following year. Grain and biomass yields were highest when seeded at the earliest late‐summer date for all environments except for Kansas, where a September 29 seeding date produced the greatest grain and biomass yields. Little to no grain was produced from spring seedings in the first production year, substantiating that photoperiod and vernalization requirements are needed for seed head induction. Grain and biomass yields were positively correlated to cumulative growing degree days (GDD) from seeding date to winter dormancy. A quadratic response was observed at Salina, KS, where seed yields maximized when GDD accumulation reached 912. Accumulation of vernalization units throughout fall, winter, and spring after seeding was also positively correlated with grain yield. The minimum vernalization units for grain production varied from 50 to 87 across sites. Results highlight important associations between thermal units and IWG grain yield when seeded in late summer; however, other variables affecting IWG seed head induction (e.g., photoperiod, snow cover) require further study.

Spectral indices measured with proximal sensing using canopy reflectance sensor, chlorophyll meter and leaf color chart for in-season grain yield prediction of basmati rice

A number of optical sensing tools are now available and can potentially be used for refining need-based fertilizer nitrogen (N) topdressing decisions. Algorithms for estimating field-specific fertilizer N needs are based on predictions of yield made while the crops are still growing in the field. The present study was conducted to establish and validate yield prediction models using spectral indices measured with proximal sensing using GreenSeeker canopy reflectance sensor, soil and plant analyzer development (SPAD) chlorophyll meter, and two different types of leaf color charts (LCCs) for five basmati rice genotypes across different growth stages. Regression analysis was performed using normalized difference vegetation index (NDVI) recorded with GreenSeeker sensor and leaf greenness indices measured with SPAD meter and LCCs developed by Punjab Agricultural University, Ludhiana (India) (PAU-LCC) and the International Rice Research Institute, Philippines (IRRI-LCC). The exponential relationship between NDVI and grain yield exhibited the highest coefficient of determination (R2) and minimum normalized root mean square error (NRMSE) at panicle initiation stage and explained 38.3%-76.4% variation in yield using genotype-specific models. Spectral indices pooled for different genotypes were closely related to grain yield at all growth stages and explained 53.4%–57.2% variation in grain yield. Normalizing different spectral indices with cumulative growing degree days (CGDD) decreased the accuracy of yield prediction. Normalization with days after transplanting (DAT), however, did not reduce or improve the predictability of yield. The ability of each model to predict grain yield was validated with an independent dataset collected from two experiments conducted at different sites with a range of fertilizer N doses. The NDVI-based genotype-specific models exhibited a robust linear correlation (R2 = 0.77, NRMSE = 7.37%, n = 180) between observed and predicted grain yields only at 35 DAT (i.e., panicle initiation stage), while the SPAD, PAU-LCC, and IRRI-LCC consistently provided reliable predictions (with respective R2 of 0.63, 0.60, and 0.53 and NRMSE of 10%, 10%, and 13.6%) even with genotype invariant models with 900 data points obtained at different growth stages. The study revealed that unnormalized values of spectral indices, namely NDVI, SPAD, PAU-LCC, and IRRI-LCC, can be satisfactorily used for in-season estimation of grain yield for basmati rice. As LCCs are very economical compared with chlorophyll meters and canopy reflectance sensors, they can be preferably used by small farmers in developing countries.

Phenology, Density and Parasitism of Asian Chestnut Gall Wasp (Dryocosmus kuriphilus) (Hymenoptera: Cynipidae) in Recently Invaded Chestnut (Castanea spp.) Orchards in Michigan.

Asian chestnut gall wasp (ACGW) (Dryocosmus kuriphilus Yasumatsu), an invasive pest native to China, was detected in Michigan, the largest chestnut producer in North America, in 2015. Along with quantifying gall densities, we tracked dates and cumulative growing degree days corresponding to ACGW life stages in five, seven, and nine orchards in 2017-2019, respectively. Gall formation, triggered by the onset of feeding by overwintered larvae, began soon after bud break. Most adult wasps emerged in mid summer, after pollen production peaked. Density of ACGW in all sites dropped sharply in 2019, probably as a result of larval mortality caused by severely cold temperatures in late January. Gall density was generally lower on Chinese chestnut (Castanea mollisima Blume) trees and C. mollisima hybrids, which share some coevolutionary history with ACGW, than on cultivars of European chestnut (C. sativa Miller) and Japanese-European (C. crenata Sieb. & Zucc. x C. sativa) trees, including the popular Colossal cultivar. Torymus sinensis Kamijo (Hymenoptera: Torymidae), a larval parasitoid previously introduced into the U.S. and several countries in Asia and Europe for ACGW biocontrol, appears to be spreading with ACGW in Michigan. Parasitoid larvae were recorded in four, six, and eight of the infested sites we sampled in 2017-2019, respectively, and parasitism rates ranged from 5 to70% of galls.

Hiding in plain sight: combining field-naturalist observations and herbarium records to reveal phenological change

As the climate warms, northern ecosystems are experiencing warmer winters and seasonal climatic shifts. Vascular plants are expected to respond to climate change by adjusting flowering or seeding periods. To determine how a native mixed-wood boreal floral assemblage has responded to warming temperatures over the 20th century, we collated historical observations made by field-naturalists as well as voucher data from the Thunder Bay region of Ontario, Canada. Combining these datasets, we performed regression analyses on 11 species of spring-flowering vascular plants to evaluate temporal trends and used spring cumulative growing degree day (sGDD0) to determine the influence of climate on flowering times. Four species showed consistent positive temporal trends (i.e., flowered later with time), while four species (three of which also demonstrated temporal trends) showed negative trends with sGDD0 (i.e., flowered earlier with an increased number of degree days above 0°C). The unexpected observation of later flowering times but predicted observation of earlier blooming with increased sGDD0 indicates that the inclusion of climate metrics may be necessary to determine the response of native vascular plants to the onset of changes in their environment. These observations were not statistically significant when field-naturalist or herbarium voucher data were analyzed separately, possibly due to low statistical power. Combining data from both sources, however, revealed common responses to climate warming among species within an ecoregion.