Categories Of Weeds Research Articles

Crop establishment and weed management techniques to control in wet-rice weeds under lateritic soils of West Bengal

A field experiment was conducted in sandy loam soil at Visva-Bharati University, Sriniketan, West Bengal in three consecutive rainy season of 2008, 2009 and 2010 to study the effect of crop establishment techniques and weed management on weed dynamics and yield of rice. Conventional transplanting method was found to be the most effective in controlling all categories of weeds throughout the growth period because of stagnation of water in the plots. Among weed management practices, pyrazosulfuron-ethyl (25 g/ha at 3 DAS/DAT) + mechanical weeding (cono-weeder) was the most effective in controlling the mixed weed flora. Similar trend was also observed in dry matter of weeds. The highest grain yield (3.75 t/ha) was obtained under system of rice intensification (SRI) with pyrazosulfuron-ethyl (25 g/ha at 3 DAS/DAT) + mechanical weeding while drum seeded with no weed control practices recorded the lowest grain yield (2.00 t/ha). SRI system with pyrazosulfuronethyl + mechanical weeding also gave the highest net return (` 19890/ha).

Tank mix application of tembotrione and atrazine to reduce weed growth and increase productivity of maize

The experiment was carried out during Kharif (wet) season of 2014 at Agricultural Farm of Palli Siksha Bhavana, Visva-Bharati, Sriniketan, West Bengal to find the effect of tank mix application of tembotrione and atrazine on weed growth and productivity of Kharif maize. Nine treatments comprising of herbicide tembotrione as early post-emergence at 80, 100, 120 g/ha alone and in combination with atrazine at 500 g/ha, sole application of atrazine at 1000 g/ha and weeding twice at 25 and 40 DAS and unweeded control were assigned in a randomized block design in three replication. Results revealed that the experimental field was infested with all categories of weeds including grassy, broad-leaved and sedges. Among them the most predominant weeds were Ludwigia parviflora, Cynodon doctylon, Cyperus rotundus and Fimbristylis miliacea. Overall weed infestation caused about 48% reduction in yield of maize. Combined application of tembotrione with atrazine was significantly superior to its sole application in all the doses tested. Tembotrione at 100 g/ha + stefes mero surfactant at 733 g/ha + atrazine 500 g/ha considerably reduced the weed infestation-registering lower weed density, dry weight, weed index, higher weed control efficiency and increase in values of growth and yield attributes and yield of maize, which were comparable with tembotrione at 80 g/ha + stefes mero surfactant at 733 g/ha + atrazine 500 g/ha and tembotrione at 120 g/ha + stefes mero surfactant at 733 g/ha + atrazine 500 g/ha. Thus, early post-emergence application of tembotrione at 80–100 g/ha + stefes mero surfactant at 733 g/ha + atrazine 500 g/ha appeared to be the most promising weed management practice for higher weed control efficiency, yield, gross and net return of Kharif (wet) season maize in lateritic soil of West Bengal.

Response of Sugar Beet Yield and Water Use Efficiency to Deficit Irrigation and Weed Competition under Drip Irrigation System

Under drip irrigation condition, two field experiments of sugar beet (Beta vulgaris L.) were conducted, in sandy soil, at El-Bostan area, Aly Mubarak Experimental Farm, Southern El-Tahrir Region, El-Buhira Governorate, Egypt, during 2013/14 and 2014/15 winter seasons, to evaluate the impact of water deficit irrigation and its relation with the critical period of weeds infestation on sugar beet yield, quality and water use efficiency. The experimental design was a split-plot design, where three water irrigation regimes treatments, i.e. 60, 80 and 100 % of evapotranspiration (ET0), were allocated in the main plots, and the ten weed removal intervals were allocated in the sub-plots which included five weed free for 3, 6, 9, 12 weeks after planting (WAP) and weed free the whole season, and five weed infestation for 3, 6, 9, 12 (WAP) and weed infestation for the whole season. The main findings indicated that the highest significant reduction on the fresh weights of grassy, broad-leaved and total weeds was obtained by irrigation at 60 % ET0 by14.8, 16.0 and 15.6 %, respectively, in 2013/14 season and 25.0, 13.6 and 17.3 %, respectively, in 2014/15 season, as compared to 100 % ET0, which reflected in increases on root and gross sugar yield per faddan by 58.8 and 60.2 %, respectively, in 2013/14 season and 65.5 and 40.1 %, respectively, in 2014/15 season, as compared to irrigation at 100 % ET0. Furthermore, water use efficiency of irrigation at 60% ET0 gave the highest values of root and sugar yield by 7.2 and 1.2 kg/m3, respectively, in the first season and 7.1 and 1.3 kg/m3, respectively, in second season. The dominant annual weeds were the broadleaf weeds with infestation rates 3.0 kg and 3.5 kg fresh weight /m2 in the first and second seasons, respectively. Whilst, the infestation rate of the grassy weeds was 1.3 kg and 1.4 kg fresh weight/m2 in the both seasons, respectively. In the both seasons, the highest reduction on the fresh weight of the two weeds categories and their total was obtained from all weed free and weed infestation treatments (> 90 %) as compared to weed infestation for the whole season, except with weed free for 3 weeks (< 70%). These results reflected on sugar beet yields (ton/fed). In the first season, the significant increasing on dry weight of tops, roots and the gross sugar yield (ton/fed) was between 118.6, 302.5 and 353.8 %, respectively, in weed free for the whole season to 58.5 % for tops by weed free for 3 weeks, 135.2 % for roots by weed free for 6 weeks and 50.0 % for gross sugar yield by weed infestation for 9 weeks, as compared to weed infestation for the whole season. In second season, the significant increasing on dry weight of the tops, roots and gross sugar yield (ton/fed) was between 98.8, 311.3 and 288.7 %, respectively, in weed free for the whole season to 37.5 % for the tops and 35.0 % for the roots in weed free for 3 weeks and 106.5 % in weed infestation for 6 weeks, as compared to weed infestation for the whole season. While, the rest weed free and weed infestation treatments in the both seasons didn't reach to significant increasing values. For sugar beet quality i.e. TSS, sucrose and purity %, all weed free and weed infestation treatments gave the same values approximately in deferent intervals. Also, different weed removal intervals can raise water efficiency by 240-320 % compared to leave the weeds to compete sugar beet plants for whole season. Concerning the interaction effects, the data indicated that the great reduction on the fresh weight of the two weed categories and their total was obtained from irrigation at 60 % ET0 with weed free for whole season, weed competition for 3 WAP and weed free for 12 WAP, in both seasons. Also, the interactions between both 60 and 80% ET0 irrigation regimes with both weed free for 12 weeks and the whole season gave the highest increasing on the roots and gross sugar yields (ton/fed) in the both seasons. The findings also revealed that the critical period of weed interference between 3 and 12 weeks after planting and the yield losses in this period. Thus, we can conclude that to save water irrigation and maximize root and sugar yields of sugar beet (ton/fed) must be irrigate at 60% ET0 with controlling weeds until 12 week from planting.

Effect of Bensulfuron-Methyl+Pretilachlor and Other Herbicides on Mixed Weed Flora of Wet Season Transplanted Rice

Field experiments were conducted during the wet season of 2012 and 2013 at farmer's field of village Binuria, Sriniketan, Birbhum, West Bengal, India with rice variety ‘Swarna’ (MTU 7029) to study the effect of bensulfuron-methyl + pretilachlor and other herbicides on growth of different weed species and productivity of transplanted rice. Twelve treatments in the experiment were assigned in a randomized block design with three replications. Results revealed that rice was infested with three categories of weeds viz. grasses, broadleaved and sedges. Cynodon dactylon and Paspalum distichum among the grasses; Fimbristylis miliacea, among the sedges and Ludwigia parviflora and Commelina nudiflora among the broadleaved weeds were predominant throughout the cropping period. Bensulfuron-methyl 0.6%+ pretilachlor 6% at 60+600 g ha−1applied at 3 DAT completely controlled the grasses, broadleaved and sedges at 30 and 60 DAT except Cynodon dactylon and Paspalum distichum. However, the density, as well as dry weight of both the species were significantly reduced. Metsulfuron methyl + chlorimuron-ethyl (Almix) + azimsulfuron at 4+35 g ha−1 at 15 DAT was also quite effective in controlling broad spectrum weeds. Lower values of weed density, total weed dry weight and higher values of herbicide efficiency index and yield of rice were registered with application of bensulfuron-methyl 0.6%+ pretilachlor 6% at 60 + 600 g ha−1 at 3 DAT followed by metsulfuron methyl + chlorimuron-ethyl (Almix) + azimsulfuron at 4+35 g ha−1 at 15 DAT. Weed infestation resulted in grain yield reduction of rice to the tune of 40% Application of bensulfuron-methyl 0.6%+ pretilachlor 6% at 60+600 g ha−1 at 3 DAT or metsulfuron methyl + chlorimuron-ethyl (Almix) + azimsulfuron at 4 + 35 g ha−1 at 15 DAT appeared to be the most promising for managing mixed weed flora and obtaining higher yield of transplanted rice in the lateritic belt of West Bengal, India.

A Classification Model of Identifying Weeds Based on Chaotic Neural Network and the Empirical Mode Decomposition

This paper puts forward a classification method of imitating the human eyes to recognize image as a whole which combined chaotic neural network and the Empirical Mode Decomposition (EMD). The method takes the individual of weeds plant as the research object and utilizes the chaotic neural network, the EMD, Teager energy operator and cluster analysis technology comprehensively. Firstly take the two-dimensional gray image matrix as the chaotic neural network weight matrix directly. Use the chaotic neural network with n neurons expression to iterate and get the one-dimensional output curve. Secondly, make the EMD decomposition for the curve and get the corresponding intrinsic mode function (IMF) curves. Then make the Teager energy transformation for each IMF component and get the average Teager energy. Finally use the fuzzy clustering algorithm to cluster analyze and get the clustering results. It can realize the classification of different categories of weeds through analysis and contrast the results with the original images. The experiment proves the effectiveness of the proposed algorithm for classifying weeds, and it is a universal new method of weeds classification.

Influence of different weed categories on growth and yields of maize (Zea mays) grown in a minor (dry) season of the humid tropics

Weed control is an important management practice in small-holder tropical upland farming systems, and manual weeding is a common feature in these units. This makes selective weeding possible, which is an essential component of integrated weed management. Field studies were initiated in Sri Lanka over a minor (dry) season to determine the impact of different weeds (grasses, broadleaves and sedges) and non-weeded conditions on the growth and yields of maize, cultivated under rainfed conditions. Weed populations were also determined, along with root distribution of the different weed management regimes at anthesis of maize. The presence of all types of weeds or grasses, which was the most dominant weed category, had the greatest adverse impact on growth and yields of maize. The reductions in seed yield due to the presence of all weeds or grasses alone were 59% and 32%, respectively. In contrast, growth and yields of maize was not affected by the small numbers of sedges present. The impact of broadleaved species was lower than that of all weeds or the grasses. Root distribution through the soil profile was greatest when maize was grown with broadleaved species or with all weeds. The roots of the maize-grass mixtures were concentrated in the upper soil layers. The beneficial impact of removing grasses and broadleaved weeds in selective manual weeding in rainfed maize cultivation which could be practiced by smallholder farming units to obtain higher yields are presented.

Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn

This study evaluates the potential of decision tree classification algorithms for the classification of hyperspectral data, with the goal of discriminating between different growth scenarios in a cornfield. A comparison was also made between decision tree and artificial neural networks (ANNs) classification accuracies. In the summer of the year 2000, a two-factor field experiment representing different crop conditions was carried out. Corn was grown under four weed management strategies: no weed control, control of grasses, control of broadleaf weeds, and full weed control with nitrogen levels of 60, 120, and 250 N kg/ha. Hyperspectral data using a Compact Airborne Spectrographic Imager were acquired three times during the entire growing season. Decision tree technology was applied to classify different treatments based on the hyperspectral data. Various tree-growing mechanisms were used to improve the accuracy of classification. Misclassification rates of detecting all the combinations of different nitrogen and weed categories were 43, 32, and 40% for hyperspectral data sets obtained at the initial growth, the tasseling and the full maturity stages, respectively. However, satisfactory classification results were obtained when one factor (nitrogen or weed) was considered at a time. In this case, misclassification rates were only 22 and 18% for nitrogen and weeds, respectively, for the data obtained at the tasseling stage. Slightly better results were obtained by following the ANN approach. However, the advantage with the decision tree was the formulation of simple and clear classification rules. The highest accuracy was obtained for the data acquired at tasseling stage. The results indicate the potential of decision tree classification algorithms and ANN usage in the classification of hyperspectral data for crop condition assessment.

Effect of cultural practices on weed management in rainfed upland rice

Abstract The results of field experiments showed that an improved crop package caused considerable reduction in density and dry weights of all categories of weeds and increased yield four times over traditional farmers’ practices in rainfed upland rice. Better land preparation (two ploughings at 15 d.b.s. and two at sowing), timely sowing (last week of June), application of fertilizer and an additional hand weeding markedly decreased the infestation of all categories of weeds in rainfed upland rice and helped in producing high grain yield. Improved variety and organic manuring had no effect on weed infestation but seemed to be essential for the yield improvement of upland rice.

The problem-oriented method of health care administration.

Abstract In 1968, Dr. Lawrence Weed1,2 described the problem-oriented medical record as a basic tool in medical education. The authors attempted to adapt the “Weed System” for use in health care administration. The aims of the modification were to facilitate participative management in problem solving, and to formulate quality decisions by the consensus of an interdisciplinary group of managers. Thirteen Department Heads were introduced to the Problem-Oriented System as described by Weed.1,2 The Problem-Oriented Method of Health Care Administration (POM/HCA) was described by the Director as an extension of Weed's system.3 Weed's categories of Subjective, Objective, Assessment, and Plan (SOAP), were given for each problem presented at Department Heads' Meetings. The subjective information for the particular problem was derived from the felt or perceived needs, or the immediate pressures that so often arise in a multidisciplinary health care setting. Objective data came from identification and analysis of i...