ABSTRACT
Various kinds of field crops growing on two commercial farms in the Whitehorse area of the southern Yukon Territory were surveyed for diseases in summer 2020 by staff of the Agriculture Branch of the Government of Yukon. They included barley, wheat, canola, beets, broccoli, cabbage, carrots, potatoes and turnips. Fields were visited one or more times during July and August. The incidence and severity of diseases were visually assessed on a crop-by-crop basis and samples were collected for laboratory analysis of the pathogens present, if any. Both infectious and non-infectious diseases were present on most crops. The infectious diseases were caused by various species of plant pathogenic bacteria and fungi that were common on these crops growing in other areas of Canada. INTRODUCTION AND METHODS: The 2020 field crop disease survey is believed to be the first organized study of its kind on agricultural crops in the Territory. In his book, "An Annotated Index of Plant Diseases in Canada . . . ", I.L. Conners lists over 300 records of plant diseases on trees, shrubs, herbs and grasses in the Yukon that were published by individuals who were surveying forests and native vegetation mainly for federal government departments, universities and other agencies (Conners 1967). The objectives of the 2020 survey were: (1) to determine the kinds and levels of diseases on selected Yukon crops, (2) to identify the major pathogen species attacking Yukon crops, and (3) to use the results to plan future surveillance activities aimed at helping producers to improve their current disease management programs. All of the fields included in the 2020 survey were situated on two commercial farms, which were designated as Farm #1 and #2, in the Whitehorse area in the southern Yukon (Fig. 1). The crops surveyed included cereals (barley and wheat), oilseeds (canola) and vegetables (beets, broccoli, cabbage, carrots, potatoes and turnips). Fields were visited one or more times in the mid- to late growing season (July/August) at a time when damage from diseases was most noticeable. Symptoms were visually assessed on a crop-by-crop basis by determining their incidence and severity. Incidence was represented by the percentage of plants, leaves, heads, kernels, etc., damaged in the target crop, while severity was estimated to be the proportion of the leaf, fruit, head, root/canopy area, etc., affected by a specific disease as follows: Proportion of the canopy affected based on a 0-4 rating scale, where: 0 = no disease symptoms, 1 = 1-10% of the crop canopy showing symptoms;2 = 11-25% showing symptoms, 3 = 26- 50% showing symptoms, and 4 = > 50% showing symptoms. Photographs of affected plants were taken and sent to plant pathologists across Western Canada for their opinions on causation. Where possible, representative samples of plants with disease symptoms were packaged and sent to the Alberta Plant Health Lab (APHL) in Edmonton, AB for diagnostic analyses. Background information, such as the general cultural practices and cropping history, was obtained from the producers wherever possible. GPS coordinates were obtained for each field to enable future mapping Cereals: Individual fields of barley (11 ha) and wheat (30 ha) located at Farm #1 were surveyed. The barley was a two-row forage cultivar 'CDC Maverick', while the wheat was an unspecified cultivar of Canada Prairie Spring (CPS) Wheat. Plant samples were taken along a W-shaped transect for a total of five sampling points for the barley field (< 20 ha) and ten sampling points for the wheat field (> 20 ha). The first visit, which occurred on July 30, involved visual inspection and destructive sampling wherein plants were collected and removed from the field for a detailed disease assessment at a lab space in Whitehorse. There, the roots were rinsed off and the plants were examined for disease symptoms. The second visit to these fields, which occurred on August 27, only involved visual examination of the standing crop. Oilseeds: A single 40 ha field of Polish canola (cv. 'Synergy') was examined o
ABSTRACT
Introduction: Most areas under spring sugar beet cultivation face severe water restrictions and increasing the area under cultivation of this crop in most of these areas is contrary to the principle of conservation of water and soil resources. The use of new areas for winter sugar beet cultivation should be the area under cultivation of this crop in hot and dry areas. Therefore, winter sowing (pending) of sugar beet with emphasis on the limitations of the country's water resources has been proposed as a solution. Materials and Methods: In this study, the quantitative and qualitative yield of 16 sugar beet genotypes in winter planting were studied as a randomized complete block design with four replications in the Torbat-e-Jam region in the two cropping years (2020-2021 and 2021-2022). The studied genotypes included F-20739, F-20837, F-21083, SBSI-5, SBSI-15, SVZA 2019-JD389, SVZA 2019-JD0402, SVZA 2019-JD0400, SVZA 2019-JD0401, FDIR 19 B 3021, FDIR 19 B 4028, F-20591, SBSI-6, SBSI-16, SBSI-7 and SBSI-17 are the breeding populations obtained from the gene bank of the Sugar Beet Seed Breeding Research Institute. In this research, traits such as root yield, sugar content, sugar yield, white sugar yield, Na, K, N, alkalinity, molasses sugar, white sugar content, and extraction coefficient of sugar were measured. Data were analyzed using SAS 9.1 software. The analysis of variance on test data and comparison to the middle of the Duncan test was performed at the 5% level. Factor analysis was calculated to identify the main factors using MINITAB software. Cluster analysis of the studied genotypes was obtained after standardizing the data by the Ward method and using Euclidean distance criterion with the help of SPSS software. Results and Discussion: The results of the combined analysis of variance showed that there was a significant difference between different genotypes of sugar beet at the level of 1% probability for all studied traits except for nitrogen content. The mean comparison showed that the SBSI-15 genotype had the highest root yield (60.66 ton.ha). It should be noted that this genotype in terms of yield index traits did not show significantly different from genotypes F-20739, SBSI-15, SVZA 2019-JD389, SVZA 2019-JD0402, SVZA 2019-JD0400, SVZA 2019-JD0401, and FDIR 19 B 4028. Also, the F-20739 genotype had the highest amounts of sugar content (19.5%), white sugar content (16.3%) and extraction coefficient of sugar (83.2%) and the lowest amount of potassium (4.24 meq .100 g-1 of root weight) and Molasses sugar (2.7%). In addition, the highest sugar yield (10.69 t/ha) and white sugar yield (8.68 t/ha) were in FDIR 19 B 3021 genotype. Investigating the correlation of traits showed the highest positive and significant correlation was between sugar yield and white sugar yield (0.99**) and the highest negative and significant correlation was between extraction coefficient of sugar and molasses sugar (-0.95**). Principal factor analysis based on the mean of the traits identified three factors that accounted for a total of 91% of the variability between the data. SBSI-15, SVZA 2019-JD0398, SVZA 2019-JD0402, SVZA 2019-JD0400, SVZA 2019-JD0401, FDIR 19 B 3021, and FDIR 19 B 4028 genotypes are distinguished different from other genotypes and they were as superior genotypes in terms of yield index traits. The dendrogram generated from the cluster analysis for white sugar yield classified genotypes into three main groups.
ABSTRACT
The complexity of predicting the impact of extraordinary events in the bio-based industrial symbiosis (BBIS) emerges as the main challenge addressed in this study. Complex systems theory, value chain dynamics, and the geographic economy constitute the best available frameworks to shed light on the aim of identifying the relationship between circular economy (CE) and viable value chains in BBIS. The Bazancourt-Pomacle biorefinery was selected as the case study to be analyzed at the mesoscale via System dynamics modelling. A scenario-based approach was adopted to identify the most required conditions to implement circularity in the sugar-beet value chain in BBIS. Three scenarios have been proposed up to 2027, the baseline scenario, the second scenario that considers the non-viable value chain scenario due to climate change effects, and the third showing an outstanding implementation of CE strategies (risk mitigation;production changeover, re-design of products and by-products, capacity buffers, and responsiveness) applied to face COVID19 outbreak is the most circular among the three. Finally, the third scenario also ensures the viability of the sugar beet value chain when facing the COVID19 outbreak by triggering leagility (leanness + agility), resilience, and survivability making the shift from bioethanol to alcohol production possible and therefore maintaining the value chain functionality.