Perbandingan Estimasi Produksi Metode Sensus Buah Hitam dengan Metode Indeks Musiman

BBC (Black Bunch Census) dan Estimasi Produksi Dengan Metode Seasonal Index (Indeks Musiman) adalah dua metode yang dapat digunakan dalam menentukan estimasi produksi tandan buah segar kelapa sawit dimasa yang akan datang. Tujuan dari penelitian ini adalah untuk mengetahui metode estimasi manakah yang tingkat akurasi estimasi produksinya lebih tinggi atau mendekati realisasi. Penelitian ini menggunakan data primer, yaitu data yang diambil di tiga blok yang berbeda yaitu A 18, A 19, dan A 20, di mana sampel diambil dari setiap blok sebanyak 10 % dari total pokok untuk perhitungan black bunch census, sedangkan untuk estimasi produksi dengan metode seasonal index hanya menggunakan jumlah pokok pada Blok tersebut serta menggunakan tabel standar produksi berdasarkan klasifikasi lahan dan umur tanaman sebagai perhitungan dasar dalam menentukan estimasi produksi. Hasil penelitian ini menunjukkan bahwa penggunaan metode estimasi produksi yang dikombinasikan dengan seasonal index memberikan hasil yang lebih akurat dari pada metode estimasi produksi menggunakan black bunch census.

Epidemiology and Risk Factors for Notifiable Scrub Typhus in Taiwan during the Period 2010–2019

Scrub typhus is a zoonotic disease caused by the bacterium Orientia tsutsugamushi. In this study, the epidemiological characteristics of scrub typhus in Taiwan, including gender, age, seasonal variation, climate factors, and epidemic trends from 2010 to 2019 were investigated. Information about scrub typhus in Taiwan was extracted from annual summary data made publicly available on the internet by the Taiwan Centers for Disease Control. From 2010 to 2019, there were 4352 confirmed domestic and 22 imported cases of scrub typhus. The incidence of scrub typhus ranged from 1.39 to 2.30 per 100,000 from 2010–2019, and peaked in 2013 and 2015–2016. Disease incidence varied between genders, age groups, season, and residence (all p < 0.001) from 2010 to 2019. Risk factors were being male (odds ratio (OR) =1.358), age 40 to 64 (OR = 1.25), summer (OR = 1.96) or fall (OR = 1.82), and being in the Penghu islands (OR = 1.74) or eastern Taiwan (OR = 1.92). The occurrence of the disease varied with gender, age, and place of residence comparing four seasons (all p < 0.001). Weather, average temperature (°C) and rainfall were significantly correlated with confirmed cases. The number of confirmed cases increased by 3.279 for every 1 °C (p = 0.005) temperature rise, and 0.051 for every 1 mm rise in rainfall (p = 0.005). In addition, the total number of scrub typhus cases in different geographical regions of Taiwan was significantly different according to gender, age and season (all p < 0.001). In particular, Matsu islands residents aged 20–39 years (OR = 2.617) and residents of the Taipei area (OR = 3.408), northern Taiwan (OR = 2.268) and eastern Taiwan (OR = 2.027) were affected during the winter. Males and females in the 50–59 age group were at high risk. The total number of imported cases was highest among men, aged 20–39, during the summer months, and in Taipei or central Taiwan. The long-term trend of local cases of scrub typhus was predicted using the polynomial regression model, which predicted the month of most cases in a high-risk season according to the seasonal index (1.19 in June by the summer seasonal index, and 1.26 in October by the fall seasonal index). The information in this study will be useful for policy-makers and clinical experts for direct prevention and control of chigger mites with O. tsutsugamushi that cause severe illness and are an economic burden to the Taiwan medical system. These data can inform future surveillance and research efforts in Taiwan.

A Grey Seasonal Index Model for Forecasting Groundwater Depth of Ningxia Plain

Forecasting the depth of groundwater in arid and semiarid areas is a great challenge because these areas are complex hydrogeological environments and the observational data are limited. To deal with this problem, the grey seasonal index model is proposed. The seasonal characteristics of time series were represented by indicators, and the grey model with fractional-order accumulation was employed to fit and forecast different periodic indicators and long-term trends, respectively. Then, the prediction results of the two were combined together to obtain the prediction results. To verify the model performance, the proposed model is applied to groundwater prediction in Yinchuan Plain. The results show that the fitting error of the proposed model is 2.08%, while for comparison, the fitting error of the grey model of data grouping and Holt–Winters model is 3.94% and 5%, respectively. In the same way, it is concluded that the fitting error of groundwater in Weining Plain by the proposed model is 2.26%. On the whole, the groundwater depth in Ningxia Plain including Yinchuan Plain and Weining Plain will increase further.

PRICE BEHAVIOR ANALYSIS OF BRAZILIAN YERBA MATE EXPORTED TO URUGUAY BETWEEN 1997 AND 2018

The objective of this study was to evaluate price seasonality of the Brazilian Yerba mate exported to the Uruguayan market, serving as a basis for decision-making to the producers. For this purpose, we collected data on Brazilian yerba mate exports to Uruguay, available in the ALICE-WEB System, which, after deflation, were used to determine the Seasonal Index for different periods between 1997 and 2018 The results showed that the market for yerba mate does not present regular historical cycles, the prices of the Brazilian product in the Uruguayan market suffer constant falls and increases. Nevertheless, it is possible to observe a long-term trend of increasing the real price of the product at an average rate of 0.33% per month. It is possible to conclude that the price of Brazilian yerba mate in the Uruguayan market was in the ascendancy until the beginning of 2015, when the Uruguayan Justice instituted an investigation to investigate the presence of cadmium and lead at levels above the allowed ones in the Brazilian product. At the end of the study period, there was a slight tendency to recover prices, however, only new studies will be able to verify if the price of Brazilian yerba on the Uruguayan market will reach levels similar to those registered before the judicial embargo.Keywords: international trade; seasonality; competitiveness.

A new seasonal cycle GM (1,1) model and its application in railway passenger volume forecasting

PurposeTo predict the passenger volume reasonably and accurately, this paper fills the gap in the research of quarterly data forecast of railway passenger volume. The research results can also provide references for railway departments to plan railway operation lines reasonably and efficiently.Design/methodology/approachThis paper intends to establish a seasonal cycle first order univariate grey model (GM(1,1) model) combing with a seasonal index. GM (1,1) is termed as the trend equation to fit the railway passenger volume in China from 2014 to 2018. The railway passenger volume in 2019 is used as the experimental data to verify the forecasting effect of the proposed model. The forecasting results of the seasonal cycle GM (1,1) model are compared with the traditional GM (1,1) model, seasonal grey model (SGM(1,1)), Seasonal Autoregressive Integrated Moving Average (SARIMA) model, moving average method and exponential smoothing method. Finally, the authors forecast the railway passenger volume from 2020 to 2022.FindingsThe quarterly data of national railway passenger volume have a clear tendency of cyclical fluctuations and show an annual growth trend. According to the comparison of the modeling results, the authors know that the seasonal cycle GM (1,1) model has the best prediction effect with the mean absolute percentage error of 1.32%. It is much better than the other models, reflecting the feasibility of the proposed model.Originality/valueAs the previous grey prediction model could not solve the series prediction problem with seasonal fluctuation, and there are few research studies on quarterly railway passenger volume forecasting, GM (1,1) model is taken as the trend equation and combined with the seasonal index to construct a combination forecasting model for accurate forecasting results in this study. Besides, considering the impact of the epidemic on passenger volume, the authors introduce a disturbance factor to deal with the forecasting results in 2020, making the modeling results more scientific, practical and referential.

Trend and Seasonality Analysis in Prices and Arrivals of Selected Agricultural Commodities in India

The study was conducted to find the fluctuation in the prices and arrival of the selected high value agricultural commodities. The study was conducted for the period 2004-2020 by collecting secondary data from CMIE (Centre for Monitoring Indian Economy) database. The major agricultural commodities were selected purposively on the basis of high price fluctuatiion and arrivals in the market. The commodities selected for the study include, castor seed, coriander, jeera, soybean and turmeric. The results of the study showed that there is a positive and significant relationship between growth rate and the prices of selected commodities with Jeera showing the highest growth rate of (9.87%) in arrival followed by turmeric (9.05%) and the lowest in coriander (5.06%) as against the prices where it showed all together with a different scenario with highest in case of coriander (7.55%) followed by soybean (7.24%) and lowest in jeera with (5.69%) respectively. The results of the findings revealed that in all the commodities high seasonal indices were observed from March to June indicating high post-harvest arrivals during these months. In the case of soybean and turmeric, the seasonal index for prices is high in April to September, which reveals that there is a lack of storage facilities and the production during these months is very low. This study suggested improvement in the infrastructure, storage, and postharvest techniques so that the arrivals of these commodities get increased and availability should be throughout the year so that the price fluctuation can be minimized.

Market Arrivals and Price Behaviour of Potato: A Study of Selected Markets of Uttar Pradesh

With a share of 28.70 percent in the total area in the country, Uttar Pradesh is one the largest producer of potatoes in the country. Potato is a semi-perishable crop, and every year the fluctuations can be seen in the arrivals and prices of the crop. The present study was conducted in the selected five markets of Uttar Pradesh to analyse the behaviour of arrivals and prices, including the Agra market, as Agra is the largest potato-producing district. The annual growth rate in the area and production of the potato was found to be positive and significant statistically. The variability in the monthly arrivals and potato prices was highest in Lucknow and Bareilly markets. The effect of the monthly arrivals on prices was negative and significant statistically. The seasonal index of the arrivals showed that arrivals were maximum from December to March, and prices were lower during these months. The correlation among all market prices was statistically significant and positive, depicting moderate to high market integration. Thus, the study suggested that establishing a scientific storage structure, market information dissemination, and proper check on market arrivals and prices would reduce their variability.

Time series evaluation of ascitic syndrome condemnation at poultry abattoirs under Federal Inspection Service of Brazil (2010-2019)

ABSTRACT: Over the last decade, Brazil registered a 10.56% increase in the number of poultry slaughtered in establishments registered under the “Serviço de Inspeção Federal” (SIF - Federal Inspection Service), as a result of technological advances in management, health and genetics applied to national aviculture. At slaughter, during post-mortem inspection, carcasses can be totally or partially condemned for various reasons, including ascitic syndrome. This syndrome has economic implications for the industry, in addition to being a problem for the health and welfare of poultry. The objective of this work was to evaluate the historical series (2010-2019) of partial and total condemnations of poultry carcasses due to ascitic syndrome in slaughterhouses registered under the SIF and located in the main poultry-producing states. Through official data, the condemnation occurrence index (COI) and the adjusted seasonal index (ASI) were calculated. The condemnation rate was 1,140 carcasses condemned, totally or partially, for ascitic syndrome for every 1,000,000 poultry slaughtered. The smallest and largest COIs were found in São Paulo (February 2010) and in Goiás (January 2017), respectively. The occurrence of condemnations for this syndrome was cyclical throughout the historical series, showing peaks of condemnation in all years evaluated, with the highest ASIs in July, August and September, and with rates varying between 1.24 and 1.54 in these months. Considering the period of pre-slaughter housing, the highest ASIs coincide with the coldest period of the year (May to August) for all analyzed states. The results show that ascitic syndrome is a growing problem in Brazil, with greater occurrence during the coldest months of the year, having a negative impact on animal health and the profitability of producers and industries in the poultry production chain.

Impacts of climate and vegetation on actual evapotranspiration in typical arid mountainous regions using a Budyko-Based framework

It is important to understand how actual evapotranspiration (ETa) changes and what the dominant contributing factors are. This study investigated the impacts of climatic factor and vegetation coverage on the variations of ETa using a Budyko-based framework. Climatic seasonal index and vegetation coverage index were selected as indicating factors. Two reservoir watersheds, i.e. the Wangkuai Reservoir Watershed and the Xidayang Reservoir Watershed, of the Daqing River Basin were selected as case studies. Also, relationships between the ETa and climatic and vegetation factors were analyzed. Results showed that the improved vegetation conditions positively contributed to the ETa changes, leading to an increase of 42.15 and 58.56 mm of ETa in the two watersheds, while the increasing climate seasonality had a negative effect, resulting in a drop of 11.48 and 13.47 mm of ETa. Vegetation coverage was recognized as the dominant factor to the changes of ETa, compared to the climatic factor. Our research could offer supporting information for water resources management, agricultural production improvement and eco-environment construction in arid regions.

PREDIKSI KREDIT DAN INDEKS MUSIMAN (SEASONAL INDEX) PADA BANK PERKREDITAN RAKYAT DI KABUPATEN JEMBER

This research aims to analyze the credit's predictive value, the development pattern of credit distribution, and the credit fluctuations of 13 Rural Banks in Jember, influenced by seasonal index variables, credit interest, NPL, LDR, ROA, CAR, and operational efficiency ratio. This study used an explanatory research approach. The sample consisted of all Rural Banks' quarterly financial reports in 2014-2019 taken by a purposive sampling method. Data were analyzed using three methods, i.e., double exponential smoothing, moving average ratio, and multiple linear regression analysis methods. Results showed that, according to each data analysis method, ten Rural Banks experienced increased credit distribution. However, the other three Rural Banks experienced a decrease in credit distribution. The study results also indicated an increasing trend in the development pattern of credit distribution. Meanwhile, the NPL and LDR variables partially influenced credit fluctuations. Keywords: credit prediction, rural bank, seasonal index