Growing Degree Units Research Articles

Performance and phenotypic stability of maize hybrids containing exotic introgressions in multi‐environment trials

AbstractMaize (Zea mays L.) grown in the US Midwest contains only a small fraction of the genetic diversity present in the species. Maize populations from other parts of the world may contain genetic variation that could be used to improve or increase the diversity of US germplasm. This study was conducted to assess the performance and phenotypic stability of hybrids containing diverse exotic introgressions across North American environments. Doubled haploid (DH) lines were created by the Germplasm Enhancement of Maize project from backcross 1 families that used 27 open‐pollinated populations from Latin America as donor parents and PHZ51, a non‐Stiff Stalk inbred developed in Iowa, as the recurrent parent. DH lines were testcrossed with LH195, a Stiff Stalk inbred developed in Iowa, and hybrid field trials were performed at 24 environments in the United States and Canada. Experimental hybrids had variable flowering time, plant and ear height, and test weight, but none had significantly greater yield than the PHZ51 × LH195 hybrid. The slopes of linear reaction norm models were significantly lower for the experimental hybrids than the US‐adapted reference hybrids for three traits: yield, growing degree units (GDU) to anthesis, and GDU to silking. The results suggest that unfavorable alleles and phenotypic stability should be considered when exotic open‐pollinated populations are used in US maize breeding.

Impact of Planting Date on Growing Degree Units, Grain Yield and Growth Traits of Some Egyptian Yellow Maize Inbred Lines

This study was conducted in 2021 to study the impact of planting date on maize inbred lines and determine their accumulated growing degree units (GDU).The experiment was held at three agriculture research stations (Gemmeiza, Ismaelia and Sids) with RCBD of four replications. It was planned to provide also some insights to optimize maize production and mitigate the impact of climatic changes to all researchers in the scientific community. Three planting dates i.e. April 20th (early), May20th (optimal) and June 20th (moderately late) were applied to investigate their effect on number of days to 50% tasselling and silking, plant height and grain yield of 16 Egyptian yellow maize inbred lines. In addition, to determine accumulated growing degree units (GDU) from planting to 50% silking. Results revealed highly significant differences among tested inbreds for all studied traits at the three planting dates indicating presence of genetic diversity among tested inbreds. Also, the same trend was found for differences among locations and inbreds x location indicating climatic variability among the three testing locations and that inbreds behaved differently at different locations due to differences in climatic conditions among locations and also genetic diversity among inbreds. Number of days to 50% tasselling and silking was reduced from April to June planting as a result of increased temperatures during plant development. Longest plant heights were obtained in April planting as a result of prolonged vegetative growth due to cool - moderate temperatures prevailing at early vegetative growth. Grain yield of all tested inbreds was reduced, with few exceptions, in June 20th planting as a result of high temperatures prevailing at time of tasselling and silking which resulted in pollen, stigma or fertilization abortion and caused poor seed setting. Highest yield of studied inbreds was obtained at Gemmeize location in April 20th planting and it is recommended as a suitable production location for inbreds. Inbred Sakha 3 was the best performing under variable planting dates. Also, inbreds Sakha 1, Sakha 11, Gz 639 and Gm 1021 performed well on certain planting dates. Determination of accumulated growing degree units for tested inbreds revealed big variation among them as a result of genetic diversity of inbreds and matched well with variation present in their silking dates and specified earliness or lateness of tested inbreds. Results of GDU would help in expectation of timing for many agricultural practices such beginning of detasselling process and harvest time in hybrid breeding programs.

Corn planting and harvest scheduling under storage capacity and growing degree units uncertainty

Planting and harvest scheduling is a crucial part of crop production due to its significant impact on other factors such as balancing the capacities for harvest, yield potential, sales price, storage, and transportation. Corn planting and harvest scheduling is challenging because corn hybrids have different planting windows, and, subsequently, inaccurate planting and harvest scheduling can result in inconsistent and unpredictable weekly harvest quantities and logistical and productivity issues. In the 2021 Syngenta Crop Challenge, participants were given several large datasets including recorded historical daily growing degree units (GDU) of two sites and provided with planting windows, required GDUs, and harvest quantities of corn hybrids planted in these two sites, and were asked to schedule planting and harvesting dates of corn hybrids under two storage capacity cases so that facilities are not over capacity in harvesting weeks and have consistent weekly harvest quantities. The research problem includes determining the planting and harvest scheduling of corn hybrids under two storage capacity cases: (1) given the maximum storage capacity, and (2) without maximum storage capacity to determine the lowest storage capacity for each site. To help improve corn planting and harvest scheduling, we propose two mixed-integer linear programming (MILP) models and a heuristic algorithm to solve this problem for both storage capacity cases. Daily GDUs are required for planting and harvest scheduling, but they are unknown at the beginning of the growing season. As such, we use recurrent neural networks to predict the weekly GDUs of 70 weeks and consider this as the predicted GDU scenario to solve this problem. In addition, we solve this problem considering all given 10 historical GDU scenarios from 2010 to 2019 together for both storage capacity cases to include historical GDUs directly to our model rather than using predicted GDUs. Our extensive computational experiments and results demonstrate the effectiveness of our proposed methods, which can provide optimal planting and harvest scheduling considering deterministic GDU scenario and uncertainties in historical GDU scenarios for both storage capacity cases to provide consistent weekly harvest quantities that are below the maximum capacity.

Influence of Weather Parameters of Growth and Yield Attribute of Different Sowing Date on Different Varieties of Pea Plant

A field experiments was conducted during winter seasons of 2021-22 at Agricultural Research Farm of Sam Higginbottom University of Agriculture Technology and Science, Prayagraj (U.P) “Influence of weather parameters of growth and yield attribute of different sowing date on different varieties of pea plant” Keeping in this view experiment was conducted in Factorial RBD with three replications having two factors with 27 plots. First factor comprised of three dates of sowing (3RD Nov. 13TH Nov and 23RD Nov.) whereas second factor consist of three Field pea varieties SS-10, GS-10, RONALDO-10.Crop sown on 13th November was recorded significantly higher yield as compared to 3rd November and 23rd November. For plant growth parameters of Pea maximum was recorded in 13th November as compared to 3rd November and 23rd November and in case of variety maximum plant height, no of leaves, no of branches grain yield test weight recorded with Ronaldo-10 as compared to GS-10 and SS-10., Vegetative growth, flowering, Pod formation and Maturity and stress degree day index was computed. With Ronaldo-10 variety proved the most remunerative and economically feasible for cultivation of Field Pea under the agro climatic conditions of Prayagraj U. P. Growing Degree Days (GDD) or effective heat units or growing degree units are heuristic tool in phenology. It is a simple means of relating plant growth, development and maturity to air temperature.

Optimizing Crop Planting Schedule Considering Planting Window and Storage Capacity.

Technology advancement has contributed significantly to productivity improvement in the agricultural sector. However, field operation and farm resource utilization remain a challenge. For major row crops, designing an optimal crop planting strategy is crucial since the planting dates are contingent upon weather conditions and storage capacity. This manuscript proposes a two-stage decision support system to optimize planting decisions, considering weather uncertainties and resource constraints. The first stage involves creating a weather prediction model for Growing Degree Units (GDUs). In the second stage, the GDUs prediction from the first stage is incorporated to formulate an optimization model for the planting schedule. The efficacy of the proposed model is demonstrated through a case study based on Syngenta Crop Challenge (2021). It has been shown that the 1D-CNN model outperforms other prediction models with an RRMSE of 7 to 8% for two different locations. The decision-making model in the second stage provides an optimal planting schedule such that weekly harvested quantities will be evenly allocated utilizing a minimum number of harvesting weeks. We analyzed the model performance for two scenarios: fixed and flexible storage capacity at multiple geographic locations. Results suggest that the proposed model can provide an optimized planting schedule considering planting window and storage capacity. The model has also demonstrated its robustness under multiple scenarios.

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 < 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions.

Registration of Gaspé Flint 1.1.1, a small‐size early‐flowering maize inbred line

AbstractFlowering time is one of the most important traits affecting cultivar adaptation and remains of great interest for breeders. Early flowering genetic stocks have been selected and utilized as models for scientific, breeding, and educational purposes. In this study, Gaspé Flint 1.1.1 (GF111; Reg. no. GS‐11, PI 698804), a new rapid cycle, diminutive maize (Zea mays L.) inbred line, is described. GF111 was developed by repeated selfing (eight cycles) and selection starting from an open‐pollinated accession of the Canadian landrace Gaspé Flint. GF111 can be grown in 1‐L pots and is characterized by short stature (0.4–0.8 m), small number of leaves (8.1–9.7), two ears per plant with only one being fertilized, from zero to one tillers, early flowering (41–50 d after sowing and as little as 357 growing degree units), and very limited protandry. High‐density molecular marker analysis showed a high level of homozygosity (>96%) and an approximately 30‐Mb‐long region shared with the maize reference line B73 on chromosome 10. Because of its high homozygosity, rapid generation cycle, easy hand‐based pollination, good fertility, and limited growing requirements as to space and resources, GF111 has great potential to be used in genetic and gene functional studies, in phenotypic screenings on phenomics platforms, and for teaching activities in plant biology and breeding.

Planting depth affects corn emergence, growth and development, and yield

AbstractCurrent planting depth recommendations for corn (Zea mays L.) producers range from 38 to 51 mm in the midwestern United States. Characterization of emergence and soil conditions at a range of depths under later‐planted scenarios is important as days adequate for field work in April have been reducing and may affect planting dates. Field studies were conducted in Ohio from 2017 to 2019 to determine the effects of planting depth and soil type on emergence, growth, and grain yield. The research was conducted in two soil types (Strawn–Crosby or Kokomo) using a randomized complete block design with four replications targeting three planting depths (25, 51, and 76 mm). Sensors were installed at the depth of seeding to measure soil moisture and temperature during the emergence period. Plants were harvested to assess growth at V3 and yield at R6. Seeds targeted at 25‐mm depth resulted in the shortest time to 10% emerged but had the least uniform emergence (longest time from 10–90% emerged) compared to the 51 and 76 mm targeted depths. Shallow planting depth also correlated to fewer leaves per plant and fewer kernels produced per plant. Emergence 7 d or 86 soil accumulated growing degree units (sGDU) after planting across fields resulted in a per plant yield reduction of 5% per day or 0.6% per sGDU, respectively. In the Strawn–Crosby field, plot yields did not differ between planting depths; in the Kokomo field yields increased by 8 or 10% when planting at 51 and 76 mm, respectively, as compared to 25 mm. Planting depth adjustments can enhance crop performance, though grain yield responses may differ by soil type.

Calculation of Agro-Climatic Factors from Global Climatic Data

This manuscript aims to create large-scale calculations of agro-climatic factors from global climatic data with high granularity-climatic ERA5-Land dataset from the Copernicus Climate Change Service in particular. First, we analyze existing approaches used for agro-climatic factor calculation and formulate a frame for our calculations. Then we describe the design of our methods for calculation and visualization of certain agro-climatic factors. We then run two case studies. Firstly, the case study of Kojčice validates the uncertainty of input data by in-situ sensors. Then, the case study of the Pilsen region presents certain agro-climatic factors calculated for a representative point of the area and visualizes their time-variability in graphs. Maps represent a spatial distribution of the chosen factors for the Pilsen region. The calculated agro-climatic factors are frost dates, frost-free periods, growing degree units, heat stress units, number of growing days, number of optimal growing days, dates of fall nitrogen application, precipitation, evapotranspiration, and runoff sums together as water balance and solar radiation. The algorithms are usable anywhere in the world, especially in temperate and subtropical zones.

Early plant height: A defining factor for yields of silage maize with contrasting phosphorus supply

AbstractSilage maize (Zea mays L.) is an important crop for forage on Northwestern European dairy cattle farms. We examined the effect of readily available soil phosphorus (P) on early maize growth and linked in‐season height growth to final harvest yield using field plots with contrasting P supply in a one‐year study embedded in a long‐term experiment. Water‐extractable P (Pw) was used as a proxy for readily available P in soil. Plant height, dry matter (DM) accumulation, P and nitrogen (N) uptakes were determined eight times from the two‐leaf stage until final whole‐crop harvest and fitted to logistic growth models. The models revealed that the final yield was significantly related to the time required to reach the maximum rate of height growth (occurring from 330 to 485 accumulated growing degree units, GDU), but not the time required to reach the maximum rate of DM accumulation (occurring from 561 to 649 GDU). Furthermore, plant height at the four‐leaf stage and onwards was significantly related to the final harvest yield. Soil Pw linked closely to height growth parameters; higher levels of Pw gave earlier peaks in height growth. For this light sandy loam with a wide gradient in P content, we conclude that suboptimal P supply postpones height growth and reduces final yields. A sufficient P supply links to an early rapid increase in plant height and forms the potential for optimum nutrient uptake and high forage yields. Thus, early‐season plant height may serve as a simple morphometric indicator for final yields.

Short‐season corn hybrids to avoid heat and drought stress in the Mid‐South USA

AbstractCorn grain yield is extremely responsive to the weather conditions during the growing season. Drought stress, as a result of high temperatures and limited naturally occurring precipitation, often limits grain yield in the Mid‐South. Drought stresses often occur during the transition from the vegetative to the reproductive growth stage, which is most impactful to yield because of the high water demand. Our hypothesis was that short‐season corn hybrids of <110 d (∼2700 growing degree units) could avoid heat and drought by flowering earlier when these heat and drought stresses are less likely. Our objective was to evaluate short‐season hybrids in Mid‐South environments along with standard full‐season hybrids for grain yield. The study was conducted in Brooksville and Starkville, MS, in 2015 and Starkville and Verona, MS, in 2016. Earlier maturing hybrids tasseled 3–7 d before standard full‐season hybrids, providing very little benefit from a drought avoidance perspective. Choosing full‐season hybrids increases yield potential in high‐yielding environments in Mississippi in which growing degree units are not limiting as in northern geographies. Short‐season hybrids did not aid in the avoidance of heat and drought and limited overall grain yield. Across all locations, there was a 0.8 bu ac−1 increase in corn grain yield for every day increase in relative maturity from 92 to 120 d.

Impact of Planter Closing Wheels on Corn Emergence in No-Till Systems

HighlightsAftermarket closing wheels increased corn emergence by 2% over standard rubber wheels.Yield was not significant by closing wheel type.Abstract. Producers are increasingly adopting cover crops and no-till planting for a variety of reasons including improving soil fertility and reducing energy inputs. However, adopting these practices may require changes in equipment and management strategy; therefore, research is needed to develop best practices for producers to reduce the risk and encourage adoption. The use of aftermarket closing wheels has been cited as a method to improve emergence under no-till conditions as preparing an ideal seedbed can be more difficult under these conditions due to limited seed-soil contact and side wall compaction. The effect of three aftermarket and the standard rubber closing wheels on emergence and yield under no-till planting of corn into heavy crop residue or cover crops was measured at three Wisconsin locations using a randomized complete block experimental design. Soil temperature and moisture was also monitored during the growing season. Corn plant emergence was measured at least three times to estimate the rate of emergence as a function of growing degree units using air and soil temperatures. The final emergence of corn planted with an aftermarket wheel was found to be significantly higher than the standard rubber closing wheel (p=0.069, a=0.1) across all locations. Yield was not found to be significant by wheel type most likely due to differences in field history and in season management practices. Keywords: Closing wheel, Cover crop, Emergence, No-till, Planter set-up.

Rice Aboveground Nitrogen Content, Development, and Yield as Affected by Nitrogen Fertilization and Flood Timing

Delaying fertilizer N and flooding delays maturity of drilled, delayed flood rice. Delaying the permanent flood may increase uptake of native soil N in some soils. The optimal preflood N and flood timing window is narrowest on the most N‐deficient soil. Urea fertilizer is typically applied near the five‐leaf stage to rice (Oryza sativa L.) grown in a dry‐seeded, delayed‐flood production system. How long the preflood N can be delayed without adversely effecting yield is poorly understood. We examined the effects of delaying preflood N application and flooding on rice growth, aboveground N content, heading date, yield components, and grain yield. Trials were established on silt loam soils at the Pine Tree Research Station (PTRS) and Rice Research and Extension Center (RREC) during 2015 and 2016 and included up to five cultivars. Urea was applied at 0, 45, 90, 135, and 180 kg N ha−1 on five to seven different dates with applications beginning near the three‐leaf stage and ranging from 127 to 1035 growing degree units (GDU) among five site‐years (PTRS‐2015a, PTRS‐2015b, PTRS‐2016, RREC‐2015, RREC‐2016). Aboveground N content increased by 25 to 59 kg N ha−1 as N fertilization and flooding were delayed at PTRS‐2015a, PTRS‐2016, and RREC‐2016 due to increased uptake of soil N. Preflood N fertilization and flooding time had no effect on grain yield at the RREC. At the PTRS, relative grain yield of rice receiving no fertilizer N increased from 20 to 41% of the maximum yield as fertilization and flooding were delayed. Maximal relative yield (91%) was produced when fertilization and flooding were performed between 164 and 531 GDU and declined to 62% by 1035 GDU at PTRS. Delaying preflood N fertilization beyond 531 GDU may reduce grain yield on some silt loam soils.

Estimating alfalfa yield and nutritive value using remote sensing and air temperature

In-field estimation of alfalfa (Medicago sativa L.) yield and nutritive value can inform management decisions to optimize forage quality and production. However, acquisition of timely information at the field scale is limited using traditional measurements such as destructive sampling and assessment of plant maturity. Remote sensing technologies (e.g., measurement of canopy reflectance) have the potential to enable rapid measurements at the field scale. Canopy reflectance (350–2500 nm) and Light Detection and Ranging (LiDAR)-estimated canopy height were measured in conjunction with destructive sampling of alfalfa across a range of maturities at Rosemount, MN in 2014 and 2015. Sets of specific spectral wavebands were determined via stepwise regression to predict alfalfa yield and nutritive value and models were reduced by spectral range to improve utility. Cumulative growing degree units (GDUs) and canopy height were tested as model covariates. An alternative GDU calculation (GDUALT) using a temporally graduating base temperature was also tested against the traditional static base temperature. The inclusion of GDUALT increased prediction accuracy for all response variables by 9–17%. Models using a common set of seven wavebands, combined with GDUALT, explained 81–90% of the variability in yield, crude protein (CP), neutral detergent fiber (NDF), and NDF digestibility (NDFd; 48-h in-vitro), respectively. This research establishes potential for remote sensing measurements to be integrated with air temperature information to achieve rapid and accurate predictions of alfalfa yield and nutritive value at the field scale for optimized harvest management.

Climate change and shifting arrival date of migratory birds over a century in the northern Great Plains

ABSTRACT Studies of the effects of climate change on migratory bird behavior have established that many species are shifting the average day of year of their arrival at nesting sites earlier. If migrating birds are adapted to arrive at the optimum stage in the growth season to maximize the availability of resources, then migration phenology shifts may result in arrival at nesting sites at selectively disadvantageous points in seasonal development of the nesting ecosystem. First arrival dates (FAD) are changing for many species, but we know little about shifts in the corresponding accumulated growing degree units (AGDU) of arrival date in association with increasing global temperatures. By transcribing field notes for migrant arrival times during the years of 1910–1950 in the region around Fargo, ND, we obtained a detailed and robust description of historical phenological patterns. Comparison of the arrival times of 83 of the same species in the same location over the past 10 years indicate that the majori...

Irrigated Soybean Leaf Photosynthesis in the Humid Subtropical Midsouth

Photosynthesis (CER (μmol CO2mleaf area-2 s−1)), stomatal conductance (gsw), and intercellular [CO2] (Ci) of soybean (Glycine maxL. Merr.) grown using the early soybean production system (ESPS) of the midsouth were determined. Three irrigated cultivars were grown using ESPS on Bosket (Mollic Hapludalfs) and Dundee (Typic Endoaqualf) soils in 2011 and 2012 at Stoneville, MS. Single leaf CER,gsw, andCiwere determined at growth stages R3, R4, and R5 using decreasing photosynthetic photon flux densities (PPFD,μmol m−2 s−1) beginning at 2000 PPFD and decreasing by 250 PPFD increments to 250 PPFD. Photosynthesis changes fit a quadratic polynomial for all fixed variables and range from ~6.0 and 9.0 CER at 250 PPFD and ~22.0 to 28.0 CER at 2000 PPFD. No cultivar differences in CER,gsw, orCiwere noted at any growth stage or site either year. In 2012, CER,gsw, andCiwere lower when measured at R5 than the two previous growth stages, which was not observed in 2011. The R5 sampling in 2012 had accumulated 39 to 70 more growing degree units at 10°C base temperature (GDU 10’s) than in 2011 and were likely more mature. Increased soybean yields from ESPS appear not to result from higher leaf CER.

Sowing and Flowering Delays Can Be an Efficient Strategy to Improve Coexistence of Genetically Modified and Conventional Maize

The coexistence between genetically modified (GM) and non‐GM maize (Zea mays L.) fields is subjected to regulation in several countries. A strategy to reduce cross‐pollination from GM to non‐GM fields was evaluated, on the basis of reducing the flowering coincidence by sowing at separate times. The trial included narrow plots in which transgenic maize was sown before, simultaneously, and after adjacent non‐GM maize. A three‐week delay between GM and non‐GM sowing dates resulted in flowering delays below 5 d for early sowings, but up to 10 to 15 d for late sowings. Cross‐pollination was monitored by xenia effect and validated by real‐time PCR. The combined effect of flowering delay and distance from the pollen source was evaluated. Cross‐pollination dramatically decreased with distance between pollen source and receptor. Flowering delay interacted with distance to the pollen donor source and further reduced GM pollen flow in all cases. Thus, it acted as a regulatory factor. A flowering delay of 92.9 growing degree units (GDU) (7 d in our experimental conditions) was enough to reduce the adventitious presence of GM kernels in non‐GM maize plots down to residual values. This study illustrates the possibility of improving coexistence through temporal separation of flowering dates on the basis of appropriate delays in sowing dates.

Evaluations of Short-Season Corn Hybrids in the Mid-South USA

Short-season corn (Zea mays L.) hybrids could allow Mid-South USA producers to spread some of their risks and begin marketing grain when supplies are low and prices high. This experiment examined the production potential of 16 short-season hybrids and compared them to two full-season hybrids commonly produced in the Mid-South in 2002 and 2003. Individual experiments were conducted at Stoneville, MS, Colt, AR, and St. Joseph, LA, both years and Knoxville, TN in 2003. All plantings utilized a randomized complete block design replicated four times, irrigated, fertilized according to yield goals of 200 bu/acre, and weeds controlled with herbicides and cultivation. Grain yield, aflatoxin, and fumonisin contamination were collected at all locations. Growing degree units (GDU 50s) at anthesis and physiological maturity, grain test weight, and kernel weight were collected at Stoneville, MS, St. Joseph, LA, and Knoxville, TN. Most of the short-season hybrids produced comparable yields to the two full-season hybrids though grain bulk densities for most of them at St. Joseph, LA were less than at other locations. Differences in mycotoxin levels were observed only at Stoneville, MS. Questions exist about short-season hybrids either requiring or just acquiring more GDU 50s when grown in the Mid-South as opposed to their adapted environments. Development of short-season hybrids for the Mid-South is warranted based upon our research.

Variability in European Maize (Zea mays L.) Landraces under High and Low Nitrogen Inputs

An European maize (Zea mays L.) landrace core collection (EMLCC) was formed with samples from several countries. Evaluation of the EMLCC may contribute to broad the genetic base of maize breeding programs. The objective of this study was to assess the variability of EMLCC under low nitrogen (N) in relation to high N input. Eighty-five landraces of the EMLCC, grouped in four maturity groups, and three check hybrids were evaluated for response to low (0 kg ha−1) and high (150 kg ha−1) N in Spain and Greece. Five plant size traits (plant height, ear height, leaf length, leaf width and leaf area index), two grain traits (1000-kernel weight and grain yield), and two agronomic traits [growing degree units (GDU) and lodging] were studied. Overall means of plant size and grain traits increased when genotypes were grown at 150-N relative to 0-N input. The relative increase for grain traits was smaller in landraces than in hybrids. This suggests that landraces had lower grain yield response to N supply compared to hybrids. Linear regressions of plant size traits on GDU indicated that vegetative development was primarily associated with flowering lateness. The maturity group was the main source of variation for all traits. Landrace variability within maturity groups was significant for all traits across environments, despite significant landrace × environment interactions. Estimates of genetic and genotype × environment variances, and heritabilities at both high and low N inputs were not significantly different from each other. However estimates were generally larger at high N. Genetic and phenotypic correlation coefficients between the two N levels were very high for all traits.

Responses of Short‐Season Corn Hybrids to a Humid Subtropical Environment

Corn (Zea mays L.) hybrids commonly grown in the lower Mississippi River valley often encounter heat and drought stress during reproductive growth, which impairs yield and increases preharvest mycotoxin contamination. Four short‐season hybrids developed for production at ≥ 40° N latitude and two hybrids adapted to the Midsouth USA were grown at Stoneville, MS (33°26′ N, 90°55′ W), in 2002 and 2003 using N fertility treatments of 112 kg N ha−1 preplant, 224 kg N ha−1 preplant, or 112 kg N ha−1 preplant + 112 kg N ha−1 side‐dress at growth stage V6 (six leaves). Growing degree units at 10°C base temperature required for growth stages R1 (silking) and R6 (physiological maturity) of the four short‐season hybrids were 50 and 100 units greater, respectively, than when grown in their adapted environments. Yields of two of the short‐season hybrids compared well with the adapted hybrids. Kernel weights and grain bulk density differed among hybrids but were not below levels subject to dockage. Aflatoxin and fumonisin levels were higher in 2002 than 2003. Three of the short‐season hybrids did have aflatoxin levels in 2002 at least three times greater than the other three hybrids. Plots receiving 224 kg N ha−1 preplant yielded more than the other N fertility treatments. Kernel weights were 10 mg greater for the higher N fertility treatments. Nitrogen fertility had no effect on mycotoxins. Short‐season hybrids need to be individually evaluated for production potential in the lower Mississippi River valley.