An Empirical Study on Gur (Jaggery) Industry (with special reference to operational efficiency & profitability measurement)

Gur (Jaggery) is a natural, traditional product of sugarcane. It can define as a honey brown coloured raw lump of sugar. Kushinagar district of Uttar-Pradesh has large number of Gur manufacturing units, mostly located in the rural areas and the manufacturers are following conventional methods for producing this. In the district the major clusters which are having more numbers of manufacturing units are Sukraouli, Kasia, Hata and Padarauna. Around half of the rural population is employed in gur making industry in this region. Although, there is no R & D assistance and marketing institutions for support. It is found that the manufacturers are producing majorly for distilleries and local licker producers, not for the food-plate or common man's consumption. The paper examines the cost-return analysis, profitability and operational efficiency of Gur manufacturing units in study area. The study revealed that units of medium and large sizes were able to cover their operating expenses with significant level of profit but small size units were earning a marginal profit. The profit earned by this category was very low as compared to other two sizes. The manufacturers are not interested in any new product of Gur, they just want to earn more profit through Gur only. This research will urged the policymakers to streamline strategies that promote stabilization of sugarcane economy and make the nation credible supplier of Gur in the International market, benefiting Gur makers, sugarcane growers and related stakeholders.

Herbicidal effect on the bio-indicators of soil health- A review

Soil microbial population, earth worms in soil, soil enzyme activity and organ carbon content in soil are considered as the bio indicators of soil health. They are used as indicators of soil health because of their active role in soil organic matter production, decomposition of xenobiotics and cycling of nutrients, ease of measurement and rapid response to changes in management practices. The assessment of soil health can be used to develop more sustainable crop production system. A number of herbicides have been introduced as pre and post emergence weed killer. The impact of herbicides on soil health depends on the soil type, type and concentration of herbicide used, sensitivity to non-target organisms and environmental conditions. The review elaborates the impact of herbicidal application on the biological indicators of soil health

Crop Yield Prediction Using Deep Neural Networks

Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive datasets and powerful algorithms. In the 2018 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the genotype and yield performances of 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016 and asked participants to predict the yield performance in 2017. As one of the winning teams, we designed a deep neural network (DNN) approach that took advantage of state-of-the-art modeling and solution techniques. Our model was found to have a superior prediction accuracy, with a root-mean-square-error (RMSE) being 12% of the average yield and 50% of the standard deviation for the validation dataset using predicted weather data. With perfect weather data, the RMSE would be reduced to 11% of the average yield and 46% of the standard deviation. We also performed feature selection based on the trained DNN model, which successfully decreased the dimension of the input space without significant drop in the prediction accuracy. Our computational results suggested that this model significantly outperformed other popular methods such as Lasso, shallow neural networks (SNN), and regression tree (RT). The results also revealed that environmental factors had a greater effect on the crop yield than genotype.Comment: 9 pages, Presented at 2018 INFORMS Conference on Business Analytics and Operations Research (Baltimore, MD, USA). One of the winning solutions to the 2018 Syngenta Crop Challeng

DETERMINATION OF MOISTURE CONTENT OF BAGASSE OF JAGGERY UNIT USING MICROWAVE OVEN

In jaggery making furnaces, sugarcane bagasse is used as fuel. Moisture content of bagasse affects its calorific value. So burning of bagasse at suitable level of moisture is essential from the viewpoint of furnace performance. Moisture content can also be used for indirect calculation of fibre content in sugarcane. Normally gravimetric method is used for moisture content determination, which is time consuming. Therefore, an attempt has been made to use microwave oven for drying of bagasse. It took about 20 to 25 minutes for the determination as compared to 8-10 hours in conventional hot air drying method and the results were comparable to the values obtained from hot air drying method

Primary Leiomyosarcoma of Ovary

Primary ovarian leiomyosarcomas is a rare neoplasm which comprises less than 3% of ovarian tumors. Their origin, etiology, histologic features, clinical behavior, and optimal treatment are still obscure. We report a case of leiomyosarcoma of ovary, diagnosed on histopathology in a 60 year old female and discuss the literature

CMFRI Newsletter No.133 April - June 2012

Abstract not availabl

Perbandingan Teknik Resampling pada Citra Hasil Pan-Sharpening untuk Pemetaan Penutup Lahan dengan Menggunakan Klasifikasi Terselia Maximum Likelihood

Penelitian ini bertujuan untuk mengetahui penggunaan metode pan-sharpend guna ekstraksi penutup/penggunaan lahan khususnya klasifikasi berbasis piksel menggunakan metode maximum likelihood. Hal ini didasari karena penggunaan metode pan-sharpend dalam ekstraksi penutup/penggunaan lahan umumnya sebatas interpertasi visual saja, sehingga tujuan penelitian ini untuk melihat seberapa baik akurasi dari berbagai metetode pan-sharpend untuk klasifikasi berbasis piksel. Metode pan-sharpend yang digunakan dalam penelitian ini yaitu Hue Saturation Value (HSV), Brovey, Gram-Schmidt dan ‘Principal Component' selain membandingkan metode pan-sharpend penelitian ini juga melihat pengaruh pemilihan metode interpolasi/resampling saat proses pan-sharpend. Metode resampling yang digunakan antara lain nearest neighbour, bilinear interpolation dan cubic convolution. Untuk data yang digunakan dalam penelitian ini yaitu citra ALOS AVNIR-2 dan ALOS PRISM yang direkam pada tanggal 20 Juni 2009. Hasil citra pan-sharpend dievaluasi seara kuantitatif menggunakan parameter bias of mean dan koefisien korelasi, hasil menunjukan bahwa metode Gram-Schmidt dan ‘Principal Component' memiliki kualitas citra yang baik. Kelas penutup/penggunaan lahan yang dihasilkan melalui citra ALOS AVNIR-2 sebanyak 11 kelas sedangkan pada citra hasil pan-sharpend dihasilkan 15 kelas penutup lahan. Dari uji akurasi interpretasi dihasilkan metode yang memiliki akurasi interpertasi paling tinggi yaitu metode ‘Principal Component' dengan teknik interpolasi resampling cubic convolution sebesar 81,697% sedangkan hasil uji akurasi interpretasi citra asli ALOS AVNIR-2 sebesar 82,23 %