Production of Dianthus Caryophyllus L. Cultivar (Haytor White) as Cut Flowers Using Day- Extension Treatment and Mulching

The experiment was carried out in the greenhouse of the Agricultural Research and Experiment Station - Kirkuk University - College of Agriculture for the agricultural season 2020-2021. The experiment was started on 20-11-2020. Carnations were taken from the mothers before the start of the experiment and planted in culture dishes to complete the rooting process. Carnations were planted on terraces with a length of (20 m) and width (75 cm), and the distance between one line and another (30 cm) and between one plant another (30 cm) as well. Artificial lighting (white LED light factor, 40 watts) was used for four hours in addition to the natural light which started half hour before sunset. The intensity of lighting was measured by a light meter (Lux 6612). As for the mulching factor, the industrial mulching was used with white polyethylene and the natural mulching with shredded yellow corn residue with a thickness of 2.5 cm, in addition to the control treatment. The experiment was carried out in a split-plot system in a randomized complete block design (RCBD) and the treatment of illumination was placed in the main plots, while mulching was placed in the secondary plots, and the results were tested according to Duncan’s multiple range test at a probability level of 5%. The following characteristics were studied: plant height (cm), plant total chlorophyll content (CCI), number of days required for flowering (day), number of flower/m2, and flower stem length (cm). The results showed the superiority of lighting and mulching treatment in all vegetative and flowering traits under study.

Effects of Corn Stalks and Urea on N2O Production from Corn Field Soil

Returning corn stalks to the field is an important and widely used soil management practice which is conducive to the sustainable development of agriculture. In this study, the effects of corn stalks and urea on N2O production in corn field soil were investigated through a 21-day incubation experiment. This study showed that increasing amounts of urea added to soil with a history of corn cultivation leads to increasing overall N2O emissions, by increasing both the intensity and the duration of emissions. Although N2O production was affected primarily by urea-derived NH4+-N and NO3−-N, its main source was native soil nitrogen, which accounted for 78.5 to 94.5% of N2O. Returning corn stalk residue to the field reduced the production of N2O, and the more urea was applied, the stronger the effect of corn residue on reducing N2O emissions. Combining the application of corn stalks and urea could reduce the concentration of NH4+-N and NO3−-N derived from urea, and then reduce the substrate required for N2O production in nitrification and denitrification processes. In addition, the combined application of corn stalks and urea could effectively inhibit the abundance of key N2O-producing genes AOA amoA, nirS and nirK.

Corn Residue Covered Area Mapping with a Deep Learning Method Using Chinese GF-1 B/D High Resolution Remote Sensing Images

Black soil is one of the most productive soils with high organic matter content. Crop residue covering is important for protecting black soil from alleviating soil erosion and increasing soil organic carbon. Mapping crop residue covered areas accurately using remote sensing images can monitor the protection of black soil in regional areas. Considering the inhomogeneity and randomness, resulting from human management difference, the high spatial resolution Chinese GF-1 B/D image and developed MSCU-net+C deep learning method are used to mapping corn residue covered area (CRCA) in this study. The developed MSCU-net+C is joined by a multiscale convolution group (MSCG), the global loss function, and Convolutional Block Attention Module (CBAM) based on U-net and the full connected conditional random field (FCCRF). The effectiveness of the proposed MSCU-net+C is validated by the ablation experiment and comparison experiment for mapping CRCA in Lishu County, Jilin Province, China. The accuracy assessment results show that the developed MSCU-net+C improve the CRCA classification accuracy from IOUAVG = 0.8604 and KappaAVG = 0.8864 to IOUAVG = 0.9081 and KappaAVG = 0.9258 compared with U-net. Our developed and other deep semantic segmentation networks (MU-net, GU-net, MSCU-net, SegNet, and Dlv3+) improve the classification accuracy of IOUAVG/KappaAVG with 0.0091/0.0058, 0.0133/0.0091, 0.044/0.0345, 0.0104/0.0069, and 0.0107/0.0072 compared with U-net, respectively. The classification accuracies of IOUAVG/KappaAVG of traditional machine learning methods, including support vector machine (SVM) and neural network (NN), are 0.576/0.5526 and 0.6417/0.6482, respectively. These results reveal that the developed MSCU-net+C can be used to map CRCA for monitoring black soil protection.

74 Production Responses of an Alternative Beef Cow-calf System

A two-yr study was conducted to measure reproductive responses of an alternative cow-calf production system. Multiparous, crossbred cows (n = 160; average age = 5.7 ± 2.8 yrs-old) were utilized, per yr, in a generalized randomized bock design and unstructured treatment design. In yr one, cows were blocked by origin source and age, randomly assigned to one of two production systems, each with four replicates (n = 20 cows/rep). Once allotted to treatment and replicate, cows remained in assigned treatment for the duration of their productive life. Treatments were: 1) traditional extensive spring calving system utilizing perennial pasture and corn residue grazing (TRAD); 2) alternative fall-calving system utilizing confinement, summer-planted oats, and corn residue grazing (ALT). Breeding body condition score (BCS) was greater (P < 0.01) for TRAD compared to ALT (6.45 vs. 5.47 ± 0.159, respectively). There were no differences (P ≥ 0.24) in conception rates (93.86 vs. 94.14 ± 2.26 %), pregnancy loss (4.37 vs. 5.62 ± 1.86 %), calving rates (89.50 vs. 88.49 ± 3.06 %), and weaning rates (86.88 vs. 82.50 ± 3.08 %) for TRAD and ALT, respectively. There was no difference (P = 0.47) in calf body weight at birth (40 vs. 39 ± 1.0 kg) for TRAD and ALT, respectively. Weaning BCS was greater (P < 0.01) for TRAD compared to ALT (5.71 vs. 5.27 ± 0.048). Calves in the TRAD system had greater (P < 0.001) wean BW (237 vs. 185 ± 3.7 kg) compared to ALT calves. Cows from TRAD system had greater (P < 0.001) kg weaned per cow exposed to bulls (208 vs. 151 ± 6.3 kg) compared to ALT cows. Results indicate no difference in reproductive performance among systems. The extensive spring-calving system produced heavier calves at weaning, leading to more kg of calf weaned per cow exposed.

37 Evaluation of Methane and Carbon Dioxide Production in Growing and Finishing Cattle Raised in Conventional or Confinement-based Herds

Cattle originating from 2 different herds were used during growing and finishing programs to evaluate differences in CH4 and CO2 production. The conventional (CONV) herd used spring calving, summer grass grazing, and winter corn residue grazing. An alternate (ALT) herd housed cows in confinement pens during summer, calved in summer, grazed cover crops in fall and grazed corn residue before returning to confinement pens in spring. Each herd had 4 groups of 20 cows. For 2 years calves from each herd were weaned at the same age and then fed ad libitum for a 120-d growing (GR) period and fed a forage-based diet (NEg 1.23 Mcal/kg). Cattle were then adapted to a grain-based finishing (FIN) diet (NEg 1.51 Mcal/kg) and fed until reaching 1.27 cm backfat. Both CH4 and CO2 were collected in two pen-scale chambers by collecting air samples continuously from each pen ambient air. Each group was evaluated in the pen-scale chambers for 5 days during both the GR and FIN phases. Data were analyzed using the MIXED procedure of SAS with day in barn as a repeated measure. Whether cattle originated from CONV or ALT had no impact (P > 0.25) on CH4 or CO2 production. Feeding GR resulted in greater (P < 0.03) CH4 than FIN in grams per day (125.4 vs 117.5), grams per kg of intake (16.1 vs 11.5), or g per kg of body weight (0.50 vs 0.24). Greater CO2 production was observed for FIN as compared to GR which is due to greater size and energy intake. Diet impacts CH4 more so than cow-calf production system cattle originate from.