The experiments were conducted to evaluate the performance of crop system (DSSAT) OILCROP-SUN model simulating growth & development and achene yield of sunflower hybrids in response to nitrogen under irrigated con...The experiments were conducted to evaluate the performance of crop system (DSSAT) OILCROP-SUN model simulating growth & development and achene yield of sunflower hybrids in response to nitrogen under irrigated conditions in semi arid environment, Sargodha, Punjab. The model was evaluated with observed data collected in trials which were conducted during spring season in 2010 and 2011 in Sargodha, Punjab, Pakistan. Split plot design was used in layout of experiment with three replications. The hybrids (Hysun-33 & S-278) and N levels (0, 75, 150 and 225 kg.ha-1) were allotted in main and sub plots, respectively. The OILCROP-SUN model showed that the model was able to simulate growth and yield of sunflower with an average of 10.44 error% between observed and simulated achene yield (AY). The results of simulation analysis indicated that nitrogen rate of 150 kg.N.ha-1 (N3) produced the highest yield as compared to other treatments. Furthermore, the economic analysis through mean Gini Dominance also showed the dominance of this treatment compared to other treatment combinations. Thus management strategy consisting?of treatment 150 kg.N.ha-1 was the best for high yield of sunflower hybrids.展开更多
Decision support system for agro-technology transfer (DSSAT), OIL CROP-SUN Model was used to stimulate the phenology, growth, yield of different two sunflower hybrids. i.e. Hysun-33 and S-78 by applying different nitr...Decision support system for agro-technology transfer (DSSAT), OIL CROP-SUN Model was used to stimulate the phenology, growth, yield of different two sunflower hybrids. i.e. Hysun-33 and S-78 by applying different nitrogen levels. The effect of nitrogen (N) on growth and yield components of different sunflower (Helianthus annuus L.) hybrids were evaluated under agro-climatic conditions of Sargodha, Pakistan during spring 2013. The experiment was laid out in a randomized complete block design with split plot arrangement having three replications, keeping cultivars in the main plots and nitrogen levels (0, 45, 90,135 and 180 kg/ha) in sub plots. OIL CROP-SUN Model showed that the model was able to simulate the growth and yield of sunflower with an average of 10.44 error% between observed and simulate achene yield (AY). The result of simulation indicates that nitrogen rate of 180 kg/ha produced highest achene yield in S-78 hybrid as compared to other treatments and Hysun-33 cultivar.展开更多
Humankind is facing another deadliest pandemic of all times in history,caused by COVID-19.Apart from this challenging pandemic,World Health Organization(WHO)considers tuberculosis(TB)as a preeminent infectious disease...Humankind is facing another deadliest pandemic of all times in history,caused by COVID-19.Apart from this challenging pandemic,World Health Organization(WHO)considers tuberculosis(TB)as a preeminent infectious disease due to its high infection rate.Generally,both TB and COVID-19 severely affect the lungs,thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation.Therefore,the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases.As one of the preliminary smart health systems that examine three clinical states(COVID-19,TB,and normal cases),this study proposes an amalgam of image filtering,data-augmentation technique,transfer learning-based approach,and advanced deep-learning classifiers to effectively segregate these diseases.It first employed a generative adversarial network(GAN)and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise.Each pre-processed image is then converted into red,green,and blue(RGB)and Commission Internationale de l’Elcairage(CIE)color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50.Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network(RNN)classifiers for precise discrimination of threeclinical states.Comparative analysis showed that the proposed Bi-directional long-short-term-memory(Bi-LSTM)model dominated the long-short-termmemory(LSTM)network by attaining an overall accuracy of 98.22%for the three-class classification task,whereas LSTM hardly achieved 94.22%accuracy on the test dataset.展开更多
为了掌握农业转移支持决策系统(Decision Support System for Agrotechnology Transfer,DSSAT)模型在国内农业与气候变化领域的研究进展,更好地让模型在今后气候变化对农业生产影响评估和适应研究中应用,笔者以近年来国内的研究和实践...为了掌握农业转移支持决策系统(Decision Support System for Agrotechnology Transfer,DSSAT)模型在国内农业与气候变化领域的研究进展,更好地让模型在今后气候变化对农业生产影响评估和适应研究中应用,笔者以近年来国内的研究和实践为基础,通过梳理模型应用的相关研究案例、方法和成果,从DSSAT模型本地化适用性验证、数据库构建、参数订正和优化方案、气候变化影响评估及适应的应用等方面全面总结了模型的应用进展。结果表明:DSSAT模型在中国应用比较广泛,包括不同地区和不同作物之间;利用DSSAT模型研究气候变化对农业生产的影响的研究较多,研究结果比较丰富。但模型在应用中存在研究方法和结果比较分散、应用的作物种类有限、数据需求量大而试验数据有限等问题,这些都需要在今后的研究中不断完善解决。展开更多
文摘The experiments were conducted to evaluate the performance of crop system (DSSAT) OILCROP-SUN model simulating growth & development and achene yield of sunflower hybrids in response to nitrogen under irrigated conditions in semi arid environment, Sargodha, Punjab. The model was evaluated with observed data collected in trials which were conducted during spring season in 2010 and 2011 in Sargodha, Punjab, Pakistan. Split plot design was used in layout of experiment with three replications. The hybrids (Hysun-33 & S-278) and N levels (0, 75, 150 and 225 kg.ha-1) were allotted in main and sub plots, respectively. The OILCROP-SUN model showed that the model was able to simulate growth and yield of sunflower with an average of 10.44 error% between observed and simulated achene yield (AY). The results of simulation analysis indicated that nitrogen rate of 150 kg.N.ha-1 (N3) produced the highest yield as compared to other treatments. Furthermore, the economic analysis through mean Gini Dominance also showed the dominance of this treatment compared to other treatment combinations. Thus management strategy consisting?of treatment 150 kg.N.ha-1 was the best for high yield of sunflower hybrids.
文摘Decision support system for agro-technology transfer (DSSAT), OIL CROP-SUN Model was used to stimulate the phenology, growth, yield of different two sunflower hybrids. i.e. Hysun-33 and S-78 by applying different nitrogen levels. The effect of nitrogen (N) on growth and yield components of different sunflower (Helianthus annuus L.) hybrids were evaluated under agro-climatic conditions of Sargodha, Pakistan during spring 2013. The experiment was laid out in a randomized complete block design with split plot arrangement having three replications, keeping cultivars in the main plots and nitrogen levels (0, 45, 90,135 and 180 kg/ha) in sub plots. OIL CROP-SUN Model showed that the model was able to simulate the growth and yield of sunflower with an average of 10.44 error% between observed and simulate achene yield (AY). The result of simulation indicates that nitrogen rate of 180 kg/ha produced highest achene yield in S-78 hybrid as compared to other treatments and Hysun-33 cultivar.
文摘Humankind is facing another deadliest pandemic of all times in history,caused by COVID-19.Apart from this challenging pandemic,World Health Organization(WHO)considers tuberculosis(TB)as a preeminent infectious disease due to its high infection rate.Generally,both TB and COVID-19 severely affect the lungs,thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation.Therefore,the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases.As one of the preliminary smart health systems that examine three clinical states(COVID-19,TB,and normal cases),this study proposes an amalgam of image filtering,data-augmentation technique,transfer learning-based approach,and advanced deep-learning classifiers to effectively segregate these diseases.It first employed a generative adversarial network(GAN)and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise.Each pre-processed image is then converted into red,green,and blue(RGB)and Commission Internationale de l’Elcairage(CIE)color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50.Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network(RNN)classifiers for precise discrimination of threeclinical states.Comparative analysis showed that the proposed Bi-directional long-short-term-memory(Bi-LSTM)model dominated the long-short-termmemory(LSTM)network by attaining an overall accuracy of 98.22%for the three-class classification task,whereas LSTM hardly achieved 94.22%accuracy on the test dataset.
文摘为了掌握农业转移支持决策系统(Decision Support System for Agrotechnology Transfer,DSSAT)模型在国内农业与气候变化领域的研究进展,更好地让模型在今后气候变化对农业生产影响评估和适应研究中应用,笔者以近年来国内的研究和实践为基础,通过梳理模型应用的相关研究案例、方法和成果,从DSSAT模型本地化适用性验证、数据库构建、参数订正和优化方案、气候变化影响评估及适应的应用等方面全面总结了模型的应用进展。结果表明:DSSAT模型在中国应用比较广泛,包括不同地区和不同作物之间;利用DSSAT模型研究气候变化对农业生产的影响的研究较多,研究结果比较丰富。但模型在应用中存在研究方法和结果比较分散、应用的作物种类有限、数据需求量大而试验数据有限等问题,这些都需要在今后的研究中不断完善解决。
