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1
Borovkova, Svetlana, and Helyette Geman. "Seasonal and stochastic effects in commodity forward curves." Review of Derivatives Research 9, no. 2 (August 18, 2007): 167–86. http://dx.doi.org/10.1007/s11147-007-9008-4.
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Gómez-Valle, L., Z. Habibilashkary, and J. Martínez-Rodríguez. "A multiplicative seasonal component in commodity derivative pricing." Journal of Computational and Applied Mathematics 330 (March 2018): 835–47. http://dx.doi.org/10.1016/j.cam.2017.05.014.
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DEWI, IDA AYU PUTU CANDRA, KOMANG DHARMAWAN, and NI MADE ASIH. "APLIKASI MODEL MEAN REVERSION DENGAN MUSIMAN DALAM MENENTUKAN NILAI KONTRAK OPSI TIPE EROPA PADA HARGA KOMODITAS KAKAO." E-Jurnal Matematika 6, no. 4 (November 28, 2017): 226. http://dx.doi.org/10.24843/mtk.2017.v06.i04.p170.
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Many literatures explain that commodity prices tend to follow the pattern of Mean Reversion models, commodity prices are controlled by seasonal supplies resulting in price fluctuations. To overcome the risk of fluctuations in the price, an investor can hedge with option contracts. The purpose of this research was to know the application of Mean Reversion model with seasonal in determining the value of European option contract from commodity ,by estimating the parameters and simulating the model in order to get the value of European option contract. Thus, the values of the options obtained with the model was compared with the value of the options calculated by the Black-Scholes model. The results of this study indicated that the value of contract option of Mean Reversion model with seasonal value was lower than the Black-Scholes model.
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Mirantes, Andrés García, Javier Población, and Gregorio Serna. "The stochastic seasonal behavior of energy commodity convenience yields." Energy Economics 40 (November 2013): 155–66. http://dx.doi.org/10.1016/j.eneco.2013.06.011.
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Arismendi, Juan C., Janis Back, Marcel Prokopczuk, Raphael Paschke, and Markus Rudolf. "Seasonal Stochastic Volatility: Implications for the pricing of commodity options." Journal of Banking & Finance 66 (May 2016): 53–65. http://dx.doi.org/10.1016/j.jbankfin.2016.02.001.
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Diewert, W. Erwin. "INDEX NUMBER APPROACHES TO SEASONAL ADJUSTMENT." Macroeconomic Dynamics 3, no. 1 (March 1999): 48–68. http://dx.doi.org/10.1017/s1365100599010020.
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A seasonal commodity is one that either (1) is not available during certain seasons or (2) is always available but its prices or quantities fluctuate with the season or time of year. The existence of type-1 seasonal commodities in consumer preference functions means that the usual economic approach to index number theory cannot be applied to construct a short-term month-to-month or quarter-to-quarter consumer price index. We postulate various separability assumptions on intertemporal preferences that can be used to justify various seasonal index number formulas. One of our approaches leads to an index number solution to the problem of seasonal adjustment.
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Wang, Xue Wu, Jiao Meng, and Ping Jiang. "The Ordering Strategy of ES Model and Option Contract." Advanced Materials Research 1037 (October 2014): 522–25. http://dx.doi.org/10.4028/www.scientific.net/amr.1037.522.
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In this paper, we incorporate retailer’s risk attitude by means of ES model into the ordering decision for seasonal product and discuss the optimal ordering decisions of the retailer under the option contract mechanism and the wholesale price contract mechanism. We show that the optimal ordering quantities of spot commodity and option commodity are exist and unique.And further analyze that the effect of retailer’s effected profit, option contract on the optimal ordering decision. Finally, we exam our theories by some numerical examples.
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Qadan, Mahmoud, David Y. Aharon, and Ron Eichel. "Seasonal patterns and calendar anomalies in the commodity market for natural resources." Resources Policy 63 (October 2019): 101435. http://dx.doi.org/10.1016/j.resourpol.2019.101435.
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Soni, Hari Prasad, Chitta Shyamsuder, and KDV Prasad. "Risk return profile of commodity derivatives: an investors perception." Revista de Gestão e Secretariado (Management and Administrative Professional Review) 14, no. 7 (July 25, 2023): 12004–16. http://dx.doi.org/10.7769/gesec.v14i7.2389.
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This article provides an overview of the literature on the commodity market, with a particular focus on the risk of commodity investment. The sources of return in the commodity market are examined, as well as the impact of various macroeconomic factors such as interest rates and inflation on the return. The commodity market is an increasingly popular investment opportunity for investors looking to diversify their portfolio. However, investing in commodities is not without risk. The volatility of commodity prices can lead to significant losses for investors, especially those who are not familiar with the market. One of the primary sources of return in the commodity market is through price appreciation. This is driven by supply and demand factors, such as changes in weather patterns, geopolitical events, and changes in consumer tastes. In addition, commodities can also generate returns through passive income, such as through rental income from real estate or dividends from stocks. Interest rates and inflation are two key macroeconomic factors that can have a significant impact on the return of commodity investments. Higher interest rates can lead to lower commodity prices, as investors seek out higher yield investments. Inflation can also impact the return of commodity investments, as it can lead to higher prices for raw materials and other inputs. For wheat, corn, and soybean futures, seasonality of return is an important consideration for investors. The prices of these commodities are often influenced by seasonal factors, such as weather patterns and harvest cycles. Understanding these seasonality patterns can help investors make more informed investment decisions. In conclusion, this study is mostly descriptive in nature, providing an overview of the literature on the commodity market and the risks associated with commodity investment. The impact of various macroeconomic factors, such as interest rates and inflation, on the return of commodity investments is also examined. Finally, the seasonality of return for wheat, corn, and soybean futures is discussed as an important consideration for investors.
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Diawara, Halimatou, Fadima Yaya Bocoum, Alassane Dicko, Ann Levin, Cynthia Lee, Fatoumata Koita, Jean Bosco Ouédraogo, et al. "Cost of introducing and delivering malaria vaccine (RTS,S/AS01E) in areas of seasonal malaria transmission, Mali and Burkina Faso." BMJ Global Health 8, no. 4 (April 2023): e011316. http://dx.doi.org/10.1136/bmjgh-2022-011316.
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BackgroundThe WHO recommends use of the RTS,S/AS01E(RTS,S) malaria vaccine for young children living in areas of moderate to highPlasmodium falciparummalaria transmission and suggests countries consider seasonal vaccination in areas with highly seasonal malaria. Seasonal vaccination is uncommon and may require adaptations with potential cost consequences. This study prospectively estimates cost of seasonal malaria vaccine delivery in Mali and Burkina Faso.MethodsThree scenarios for seasonal vaccine delivery are costed (1) mass campaign only, (2) routine Expanded Programme on Immunisation (EPI) and (3) mixed delivery (mass campaign and routine EPI)), from the government’s perspective. Resource use data are informed by previous new vaccine introductions, supplemented with primary data from a sample of health facilities and administrative units.FindingsAt an assumed vaccine price of US $5 per dose, the economic cost per dose administered ranges between $7.73 and $8.68 (mass campaign), $7.04 and $7.38 (routine EPI) and $7.26 and $7.93 (mixed delivery). Excluding commodities, the cost ranges between $1.17 and $2.12 (mass campaign), $0.48 and $0.82 (routine EPI) and $0.70 and $1.37 (mixed delivery). The financial non-commodity cost per dose administered ranges between $0.99 and $1.99 (mass campaign), $0.39 and $0.76 (routine EPI) and $0.58 and $1.28 (mixed delivery). Excluding commodity costs, service delivery is the main cost driver under the mass campaign scenario, accounting for 36% to 55% of the financial cost. Service delivery accounts for 2%–8% and 12%–23% of the total financial cost under routine EPI and mixed delivery scenarios, respectively.ConclusionVaccine delivery using the mass campaign approach is most costly followed by mixed delivery and routine EPI delivery approaches, in both countries. Our cost estimates provide useful insights for decisions regarding delivery approaches, as countries plan the malaria vaccine rollout.
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Michelfelder, Richard A., and Eugene A. Pilotte. "Information in Electricity Forward Prices." Journal of Financial and Quantitative Analysis 55, no. 8 (October 29, 2019): 2641–64. http://dx.doi.org/10.1017/s0022109019000930.
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We examine forward prices in a market where nonstorable inventory exacerbates the influence of seasonal and hourly variation in supply and demand, expected and unexpected, on the level and volatility of spot prices. We find strong evidence, unusual for a commodity, that the difference between contemporaneous forward and spot prices has power to forecast both the spot price change and the risk premium realized at delivery. Our evidence of a time-varying risk premium is consistent with expected hourly and seasonal variation in the needs of producers and retailers of electricity to hedge against extreme spot price decreases and increases, respectively.
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CHEN, Mang, and Cun-yu GONG. "Two-stage optimal ordering policy of joint inventory with backlogging for seasonal commodity." Journal of Computer Applications 32, no. 8 (May 8, 2013): 2356–59. http://dx.doi.org/10.3724/sp.j.1087.2012.02356.
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Stone, Roger C., and Holger Meinke. "Operational seasonal forecasting of crop performance." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1463 (October 24, 2005): 2109–24. http://dx.doi.org/10.1098/rstb.2005.1753.
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Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.
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Diewert, W. Erwin. "HIGH INFLATION, SEASONAL COMMODITIES, AND ANNUAL INDEX NUMBERS." Macroeconomic Dynamics 2, no. 4 (December 1998): 456–71. http://dx.doi.org/10.1017/s1365100598009031.
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This paper studies the problems of measuring economic growth under conditions of high inflation. Traditional bilateral index number theory implicitly assumes that variations in the price of a commodity within a period can be ignored. To justify this assumption under conditions of high inflation, the accounting period must be shortened to a quarter, a month, or possibly a week. However, once the accounting period is less than a year, the problem of seasonal commodities is encountered; i.e., in some subannual periods, many seasonal commodities will be unavailable and hence the usual bilateral index number theory cannot be applied. The paper systematically reviews the problems of index number construction when there are seasonal commodities and high inflation. Various index number formulas are justified from the viewpoint of the economic approach to index number theory by making separability assumptions on consumers' intertemporal preferences. We find that accurate economic measurement under conditions of high inflation is very complex.
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Benth, Fred Espen, Steen Koekkebakker, and Fridthjof Ollmar. "Extracting and Applying Smooth Forward Curves From Average-Based Commodity Contracts with Seasonal Variation." Journal of Derivatives 15, no. 1 (August 31, 2007): 52–66. http://dx.doi.org/10.3905/jod.2007.694791.
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Maitra, Debashish. "Do seasonality, break and spillover effects explain commodity price volatility." Journal of Agribusiness in Developing and Emerging Economies 8, no. 1 (March 12, 2018): 144–70. http://dx.doi.org/10.1108/jadee-04-2015-0019.
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Purpose The purpose of this paper is to understand the volatility in commodity futures and spot markets. The study starts with a few questions: first, the effect of seasonality on the volatility is studied. Thereafter, the presence of structural breaks in the variance is identified. At last the seasonality, structural shifts and spillover effects are examined together to find out their effects on volatility. Design/methodology/approach The methodology heavily employs econometric tools and techniques. The monthly seasonal dummies are incorporated to identify the effects of seasonality on volatility. Then, the presence of break in volatility is tested by cumulative sum of squares (CUSUM test), followed by generalized autoregressive conditional heteroscedastictity and EGARCH models are measured by including seasonal dummies, break dummies and the residuals of other market in the variance equation to determine spillover effects. Findings It is found that the effects of seasonality on volatility cannot be ignored as the effects are significant. The presence of asymmetry is detected in all the commodities. The presence of seasonality and structural breaks in the variance equation are statistically able to reduce the volatility but the magnitude is very negligible with an exception in cumin futures markets. Bi-directional volatility spillover between futures and spot markets is observed in all the commodities and the effect of spillover is more from spot markets to the futures markets. Research limitations/implications This study is limited to a few agro commodities which are well traded. This study could have been extended to the other thinly traded commodities. This study has also taken only near month futures contracts as it contains more information but the same could have been studied by taking far month contracts also. Originality/value The present study attempted to understand the conjugated effects of seasonality, structural breaks and spillover on volatility of commodity markets which is not apparent in the previous studies. This study has also employed methodological rigor to identify the breaks in the variance equation. In addition to this it has also investigated whether Indian commodity futures markets are informationally more efficient than the spot markets.
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Murray, Carolyn E., Csaba Varga, Rachel Ouckama, and Michele T. Guerin. "Temporal Study of Salmonella enterica Serovars Isolated from Fluff Samples from Ontario Poultry Hatcheries between 2009 and 2018." Pathogens 11, no. 1 (December 22, 2021): 9. http://dx.doi.org/10.3390/pathogens11010009.
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The objectives of this study were to determine the prevalence, temporal trends, seasonal patterns, and temporal clustering of Salmonella enterica isolated from fluff samples from poultry hatcheries in Ontario between 2009 and 2018. A scan statistic was used to identify clusters of common serovars and those of human health concern. A multi-level logistic regression model was used to identify factors (poultry commodity, year, season) associated with S. enterica presence. The period prevalence of S. enterica was 7.5% in broiler hatcheries, 1.6% in layer hatcheries, 7.6% in turkey hatcheries, 29.7% in waterfowl hatcheries, and 13.8% in game-bird hatcheries. An overall increasing trend in S. enterica prevalence was identified in waterfowl and game-bird hatcheries, while a decreasing trend was identified in broiler and turkey hatcheries. Overall, the most common S. enterica serovars were Kentucky, Enteritidis, Heidelberg, and Senftenberg. Salmonella enterica ser. Enteritidis was the most common serovar in waterfowl hatcheries. Temporal clusters were identified for all poultry commodities. Seasonal effects varied by commodity, with the highest odds of S. enterica occurring in the summer and fall. Our study offers information on the prevalence and temporality of S. enterica serovars that might guide prevention and control programs at the hatchery level.
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Jain, Sheenu, and Swati Soni. "Chikki in a New Avatar—Will There be Demand?" FIIB Business Review 8, no. 4 (November 22, 2019): 271–76. http://dx.doi.org/10.1177/2319714519876764.
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The case study is about Hector Beverages Private Limited (HBPL), a company that ventured into energy drink and non-carbonated ethnic beverages of the genre of aam panna under the name of its flagship brand Paper Boat. Four years of existence and HBPL decided to foray into the ethnic sweet segment with a very quintessential offering named Paper Boat Chikki. Chikki, as we all know, is a hyper-seasonal offering to be consumed in the winter season. It is a traditional peanuts and jaggery preparation. The case maps the journey of local and handmade commodity chikki to the brand Paper Boat Chikki. The case elaborates upon the challenges faced by the brand and the strategies adopted to become a commodity brand. Also worth pondering is the expansion of HBPL into the snacks category. Is the move from drinks portfolio to snacks portfolio judicious?
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Markus, S. B., J. L. Aalhus, J. A. M. Janz, and I. L. Larsen. "A survey comparing meat quality attributes of beef from credence attribute-based production systems." Canadian Journal of Animal Science 91, no. 2 (June 2011): 283–94. http://dx.doi.org/10.4141/cjas10082.
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Markus, S. B., Aalhus, J. L., Janz, J. A. M. and Larsen, I. L. 2011. A survey comparing meat quality attributes of beef from credence attribute-based production systems. Can. J. Anim. Sci. 91: 283–294. Two branded beef programs based on producer-defined production systems differentiated by intangible credence attributes (Organic and Natural) were compared with Commodity beef to determine meat quality and assess consumer acceptability. In each of four slaughter seasons (winter, spring, summer and fall) longissimus lumborum muscle samples were collected from two industry slaughter plants; Organic n=30, 30, 27 and 31; Natural n=30, 27, 29 and 25; Commodity 1 n=12 and 18 for spring and summer, respectively; Commodity 2 n=14 and 12 for spring and fall, respectively. Samples were vacuum packaged and aged for 16±2 d at 2°C. Seasonal effects (P<0.01) were evident for mean shear force, composition, drip loss, colour and pH. While all mean shear values were classified as being tender (<5.6 kg), a smaller proportion of steaks were classified as tender in the Organic beef compared with the Natural and Commodity beef (55.9 vs. 70.3 and 78.6%; P<0.01), indicating that even after industry normal ageing times there was higher tenderness variability in the Organic beef. Fat content (SEM=0.23; P<0.01) was lowest for the Organic line (3.98%) with Natural (5.34%) and Commodity being intermediate (5.73%). Some statistically significant differences (P<0.05) in mean scores for aroma, juiciness, flavour, tenderness and overall acceptability of cooked beef steaks were observed amongst the three production systems when samples were not matched on the basis of intramuscular fat (IMF). Clearly there are measureable differences in quality between “credence” based production systems and commodity beef with an overall better quality in Commodity beef. However, if the consumer is willing to pay for credence-based attributes then there is an opportunity for these production systems to improve the quality of their product, specifically in respect to age at slaughter and content of IMF.
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Webster, Michael, and Rory C. Tarnow-Mordi. "Decomposing Multilateral Price Indexes into the Contributions of Individual Commodities." Journal of Official Statistics 35, no. 2 (June 1, 2019): 461–86. http://dx.doi.org/10.2478/jos-2019-0020.
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Abstract This article describes methods for decomposing price indexes into contributions from individual commodities, to help understand the influence of each commodity on aggregate price index movements. Previous authors have addressed the decomposition of bilateral price indexes, which aggregate changes in commodity prices from one time period to another. Our focus is the decomposition of multilateral price indexes, which aggregate commodity prices across more than two time periods or countries at once. Multilateral indexes have historically been used for spatial comparisons, and have recently received attention from statistical agencies looking to produce temporal price indexes from large and high frequency price data sets, such as scanner data. Methods for decomposing these indexes are of practical relevance. We present decompositions of three multilateral price indexes. We also review methods proposed by other researchers for extending multilateral indexes without revising previously published index levels, and show how to decompose the extended indexes they produce. Finally, we use a data set of seasonal prices and quantities to illustrate how these decomposition methods can be used to understand the influence of individual commodities on multilateral price index movements, and to shed light on the relationships between various multilateral and extension methods.
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Shang, Jin, and Shigeyuki Hamori. "Do Large Datasets or Hybrid Integrated Models Outperform Simple Ones in Predicting Commodity Prices and Foreign Exchange Rates?" Journal of Risk and Financial Management 16, no. 6 (June 9, 2023): 298. http://dx.doi.org/10.3390/jrfm16060298.
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With the continuous advancement of machine learning and the increasing availability of internet-based information, there is a belief that these approaches and datasets enhance the accuracy of price prediction. However, this study aims to investigate the validity of this claim. The study examines the effectiveness of a large dataset and sophisticated methodologies in forecasting foreign exchange rates (FX) and commodity prices. Specifically, we employ sentiment analysis to construct a robust sentiment index and explore whether combining sentiment analysis with machine learning surpasses the performance of a large dataset when predicting FX and commodity prices. Additionally, we apply machine learning methodologies such as random forest (RF), eXtreme gradient boosting (XGB), and long short-term memory (LSTM), alongside the classical statistical model autoregressive integrated moving average (ARIMA), to forecast these prices and compare the models’ performance. Based on the results, we propose novel methodologies that integrate wavelet transformation with classical ARIMA and machine learning techniques (seasonal-decomposition-ARIMA-LSTM, wavelet-ARIMA-LSTM, wavelet-ARIMA-RF, wavelet-ARIMA-XGB). We apply this analysis procedure to the commodity gold futures prices and the euro foreign exchange rates against the US dollar.
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Wanjuki, Teddy Mutugi, Adolphus Wagala, and Dennis K. Muriithi. "Forecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Models." European Journal of Mathematics and Statistics 2, no. 6 (December 21, 2021): 50–63. http://dx.doi.org/10.24018/ejmath.2021.2.6.80.
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Price stability is the primary monetary policy objective in any economy since it protects the interests of both consumers and producers. As a result, forecasting is a common practice and a vital aspect of monetary policymaking. Future predictions guide monetary and fiscal policy tools that that be used to stabilize commodity prices. As a result, developing an accurate and precise forecasting model is critical. The current study fitted and forecasted the food and beverages price index (FBPI) in Kenya using seasonal autoregressive integrated moving average (SARIMA) models. Unlike other ARIMA models like the autoregressive (AR), Moving Average (MA), and non-seasonal ARMA models, the SARIMA model accounts for the seasonal component in a given time series data better forecasts. The study relied on secondary data obtained from the KNBS website on monthly food and beverage price index in Kenya from January 1991 to February 2020. R-statistical software was used to analyze the data. The parameter estimation was done using the Maximum Likelihood Estimation method. Competing SARIMA models were compared using the Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE),.and Mean Absolute Percentage Error (MAPE). A first-order differenced SARIMA (1,1,1) (0,1,1)12 minimized these model evaluation criteria (AIC = 1818.15, BIC =1833.40). The forecasting ability evaluation statistics MAE = 2.00%, MAPE = 1.62% and MASE = 0.87%. The 24-step ahead forecasts showed that the FPBI is unstable with an overall increasing trend. Therefore, the monetary policy committee ought to control inflation through monetary or fiscal policy, strengthening food security and trade liberalization.
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Agussabti, Agussabti, Romano Romano, Rahmaddiansyah Rahmaddiansyah, and Rohayati Mohd Isa. "Factors affecting risk tolerance among small-scale seasonal commodity farmers and strategies for its improvement." Heliyon 6, no. 12 (December 2020): e05847. http://dx.doi.org/10.1016/j.heliyon.2020.e05847.
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Purnomo, Nugroho Hari, S. Sutikno, S. Sunarto, and Luthfi Muta'ali. "Landslide Risk on the Farmland at the Arjuno Volcano Complex of East Java." Forum Geografi 27, no. 1 (July 20, 2013): 45. http://dx.doi.org/10.23917/forgeo.v27i1.5081.
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The purposes of the study are (1) studying the characteristics of the landslide hazard level, vulnerability, and capacity dealing with the seasonal plant farmland, and (2) developing the conceptual model disaster risk of landslide for the seasonal plant farmland at the stato volcano area. Sampling of the land factor was carried out in a purposive way and the sampling of the people was carried out accidentally. The analysis was conducted descriptively, parametric and non-parametric statistics, and spatial analysis of ecological map, land shape, slope, soil and land use. The findings showed that the extremely high landslide risk occurred at the cone shape of the incised volcano and the slope of the incised volcano. The economic vulnerability of one-seasonal crop farmland was about Rp. 8,879,310 ha/year-Rp. 44,036,061 ha/year. While, the socio-economic factor of the farmers was generally characterized by a transition into a periurban area. The conceptual model tated that the resources of seasonal plant farmland with the high risk of landslide can be cultivated with the acceptable risk if the vulnerability of agricultural commodity is low in economic value and the capacity of farmers cultivating it is high.
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Borowski, Krzysztof. "Should Investors in Commodity Markets Be Superstitious (Based on the Example of 29 Commodities)?" Acta Universitatis Lodziensis. Folia Oeconomica 4, no. 337 (September 20, 2018): 69–84. http://dx.doi.org/10.18778/0208-6018.337.05.
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The issue of efficiency of financial markets has always fascinated scientists. It is significant from the point of view of assessing portfolio management effectiveness and behavioural finance. In the first part of this paper, the hypothesis of the unfortunate dates effect was tested upon 29 commodity prices in relation to the following four approaches: close‑close, overnight, open‑open, and open‑close. The rates of return were calculated for the sessions falling on the 13th and 4th day of the month, Friday the 13th and Tuesday the 13th. The study proved the occurrence of seasonal effects on the so‑called unlucky dates.
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Murray, Carolyn E., Csaba Varga, Rachel Ouckama, and Michele T. Guerin. "Temporal Study of Salmonella enterica Serovars Isolated from Environmental Samples from Ontario Poultry Breeder Flocks between 2009 and 2018." Pathogens 12, no. 2 (February 8, 2023): 278. http://dx.doi.org/10.3390/pathogens12020278.
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This study’s goal was to determine the prevalence, temporal trends, seasonal patterns, and temporal clustering of Salmonella enterica isolated from environmental samples from Ontario’s poultry breeding flocks between 2009 and 2018. Clusters of common serovars and those of human health concern were identified using a scan statistic. The period prevalence of S. enterica was 25.3% in broiler breeders, 6.4% in layer breeders, and 28.6% in turkey breeders. An overall decreasing trend in S. enterica prevalence was identified in broiler breeders (from 27.8% in 2009 to 22.1% in 2018) and layer breeders (from 15.4% to 4.9%), while an increasing trend was identified in turkey breeders (from 12.0% to 24.5%). The most common serovars varied by commodity. Among broiler breeders, S. enterica serovars Kentucky (42.4% of 682 submissions), Heidelberg (19.2%), and Typhimurium (5.4%) were the most common. Salmonella enterica serovars Thompson (20.0% of 195 submissions) and Infantis (16.4%) were most common among layer breeders, and S. enterica serovars Schwarzengrund (23.6% of 1368 submissions), Senftenberg (12.9%), and Heidelberg and Uganda (9.6% each) were most common among turkey breeders. Salmonella enterica ser. Enteritidis prevalence was highest in submissions from broiler breeders (3.7% of 682 broiler breeder submissions). Temporal clusters of S. enterica serovars were identified for all poultry commodities. Seasonal effects varied by commodity, with most peaks occurring in the fall. Our study provides information on the prevalence and temporality of S. enterica serovars within Ontario’s poultry breeder flocks that might guide prevention and control programs at the breeder level.
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Rahayu, Silvia. "Penentuan Agribisnis Unggulan Komoditi Pertanian Berdasarkan Nilai Produksi di Kabupaten Kerinci." J-MAS (Jurnal Manajemen dan Sains) 6, no. 1 (April 24, 2021): 154. http://dx.doi.org/10.33087/jmas.v6i1.242.
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Regional development discusses very strategically in the completion of the implementation of national development. The economic development of each region through economic growth, which is at the same time an indicator, provides an overview of where regional economic development in a given period has resulted in an increase in income for the community which increases per capita income. Agribusiness still has a bigger role in Kerinci Regency, because most of the population of Kerinci Regency supports their lives from the agribusiness sector. The strategic role of agribusiness in the economy of Kerinci Regency includes: food providers for the population of Indonesia, foreign exchange earners through exports, providers of industrial raw materials, increased employment and business opportunities, increased regional income, poverty alleviation and drivers of other economic sectors. In fact, a large number of agribusinesses have considerable production value in Kerinci Regency. This study aimed was to: (1) Analyze the types of superior agricultural commodities, and (2). Review the growth structure of agricultural commodities in Kerinci Regency. The design of this study used descriptive research methods that were descriptions of goals. The aim was that the author wanted to analyze the types of superior agribusiness commodities with the Location Quotient (Location Question) method and examine the structure of the growth of commodity agribusiness in Kerinci Regency. Based on the results of the study, it was found that: the leading agribusiness was the superior agricultural commodity of Kerinci Regency in the period of 2012-2016 based on the calculation of Location Quotient (LQ) Analysis are food crops, horticulture plants and seasonal plantations. Based on Klassen Analysis The typology of the structure of the growth of agricultural commodities is divided into four parts. Advanced and fast developing commodities, in Kerinci Regency are annual and other food crops and horticulture plants. Advanced but depressed commodities consist of annual horticultural crops sub-sector commodities and seasonal plantations. Rapidly developing commodities consist of commodity livestock, forestry and logging sub-sectors. Relatively lagging commodities consist of agricultural services sub-sector and hunting
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Potgieter, A. B., Y. L. Everingham, and G. L. Hammer. "On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts." International Journal of Climatology 23, no. 10 (2003): 1195–210. http://dx.doi.org/10.1002/joc.932.
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Pourhaji, Nazila, Mohammad Asadpour, Ali Ahmadian, and Ali Elkamel. "The Investigation of Monthly/Seasonal Data Clustering Impact on Short-Term Electricity Price Forecasting Accuracy: Ontario Province Case Study." Sustainability 14, no. 5 (March 6, 2022): 3063. http://dx.doi.org/10.3390/su14053063.
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The transformation of the electricity market structure from a monopoly model to a competitive market has caused electricity to be exchanged like a commercial commodity in the electricity market. The electricity price participants should forecast the price in different horizons to make an optimal offer as a buyer or a seller. Therefore, accurate electricity price prediction is very important for market participants. This paper investigates the monthly/seasonal data clustering impact on price forecasting. To this end, after clustering the data, the effective parameters in the electricity price forecasting problem are selected using a grey correlation analysis method and the parameters with a low degree of correlation are removed. At the end, the long short-term memory neural network has been implemented to predict the electricity price for the next day. The proposed method is implemented on Ontario—Canada data and the prediction results are compared in three modes, including non-clustering, seasonal, and monthly clustering. The studies show that the prediction error in the monthly clustering mode has decreased compared to the non-clustering and seasonal clustering modes in two different values of the correlation coefficient, 0.5 and 0.6.
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Wanjuki, Teddy M., Adolphus Wagala, and Dennis K. Muriithi. "Evaluating the Predictive Ability of Seasonal Autoregressive Integrated Moving Average (SARIMA) Models using Food and Beverages Price Index in Kenya." European Journal of Mathematics and Statistics 3, no. 2 (April 8, 2022): 28–38. http://dx.doi.org/10.24018/ejmath.2022.3.2.100.
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Price instability has been a major concern in most economies. Kenya's commodity markets have been characterized by high price volatility affecting investment and consumer behaviour due to uncertainty on future prices. Therefore, precise forecasting models can help consumers plan for their expenditure and government policymakers formulate price control measures. Due to the seasonality of Kenya's food and beverage price indices, the current study postulates that the Seasonal Autoregressive Integrated Moving Average (SARIMA) model can best be the best fit model for the data. The study used secondary data on Kenya's monthly food and beverage prices index from January 1991 to February 2020 to examine the predictive ability of the possible SARIMA models based on the minimisation of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). A first-order differenced SARIMA (1,1,1) (0,1,1)12 minimized these model evaluation criteria (AIC = 1818.15, BIC =1833.40). The cross-validation test results of 6, 12, 18, 24, 30, and 36 step-ahead forecasts demonstrated that SARIMA models are unstable for use in forecasting over a long-time period with a tendency of increasing prediction errors with an increase in the forecast period. It is anticipated that the findings of the current study will provide necessary valuable information to the policymakers and stakeholders to understand future trends in commodity price
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Suprianto, A., A. S. Atmadipoera, and J. Lumban-Gaol. "Seasonal coastal upwelling in the Bali Strait: a model study." IOP Conference Series: Earth and Environmental Science 944, no. 1 (December 1, 2021): 012055. http://dx.doi.org/10.1088/1755-1315/944/1/012055.
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Abstract Bali Strait is part of fisheries management zone (WPP 573), where abundant fishery potential, of lemuru fish commodity. Here, physical oceanographic setting such as upwelling event plays an important role on maintaining high primary productivity and lemuru fish distribution. This study aims to describe physical process and dynamics of seasonal coastal upwelling using time-series datasets (2008 and 2014) of temperature, salinity, current velocity, surface chlorophyll-a (chl-a) from INDESO model and satellite imagery. The results showed that upwelling in the Bali Strait only during the southeast monsoon period when the south-easterly wind force surface Ekman drift of about 5.5 × 10−3 Sv flowing south-eastward (toward offshore). Upwelling event is characterized by minimum parameter of sea surface temperature (24.93 °C), and sea level anomaly (0.75 m), but maximum of surface chlorophyll-a (1.33 mg/m3). Furthermore, isotherm of 26 °C and Isohaline 33.7 psu are outcropped at sea surface in the center of upwelling zone. In contrast, during the nortwest monsoon period these isolines remain at deeper layer of about 80-90 m depth. Mean temperature-based upwelling index during peak of upwelling in August (1.19±0.19 °C). Upwelling impact on high abundance of lemuru fish (Sardinella sp.) production two month later after peak of chl-a.
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Sanusi, Olajide I., Samir K. Safi, Omotara Adeeko, and Mosab I. Tabash. "Forecasting agricultural commodity price using different models: a case study of widely consumed grains in Nigeria." Agricultural and Resource Economics: International Scientific E-Journal 8, no. 2 (June 20, 2022): 124–40. http://dx.doi.org/10.51599/10.51599/are.2022.08.02.07.
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Purpose. This study highlights the specific and accurate methods for forecasting prices of commonly consumed grains or legumes in Nigeria based on data from January 2017 to June 2020.
Methodology / approach. Different models that include autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), seasonal decomposition of time series by loess method (STLM), and a combination of these three models (hybrid model) were proposed to forecast the sample grain price data. This study uses price data on widely consumed grains, such as white maize, local rice, imported rice, and white beans, in Nigeria from January 2017 to June 2020.
Results. Our result indicates that ARIMA is the best applicable model for white maize and imported rice because it is well fitted to stationary data, as demonstrated in the sample period. The STLM is more appropriate in forecasting white beans. As white beans are highly seasonal in Nigeria, it further explains why the STLM model fits better in forecasting prices. The production of local rice is inconsistent in Nigeria because of erratic rainfall and stiff competition from the importation of rice from other countries. Therefore, and consistent with the analysis, the hybrid model is the best model applicable to local rice because it captures varying trends exhibited in the data.
Originality / scientific novelty. This study suggests most accurate forecasting techniques for specific agricultural commodities in sub-Saharan African countries. It considers forecasting prices of commonly consumed grains and legumes in Nigeria and traded worldwide, such as imported rice, local rice, beans, and maize.
Practical value / implications. The study highlights the importance of appropriate forecasts for policymakers, producers, and consumers to enhance better decision making and serve as an underlying incentive to guide the allocation of financial resources to the agricultural sector, which determines the structure and degree of sectoral growth.
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Sanusi, Olajide I., Samir K. Safi, Omotara Adeeko, and Mosab I. Tabash. "Forecasting agricultural commodity price using different models: a case study of widely consumed grains in Nigeria." Agricultural and Resource Economics: International Scientific E-Journal 8, no. 2 (June 20, 2022): 124–40. http://dx.doi.org/10.51599/are.2022.08.02.07.
Full textAbstract:
Purpose. This study highlights the specific and accurate methods for forecasting prices of commonly consumed grains or legumes in Nigeria based on data from January 2017 to June 2020.
Methodology / approach. Different models that include autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), seasonal decomposition of time series by loess method (STLM), and a combination of these three models (hybrid model) were proposed to forecast the sample grain price data. This study uses price data on widely consumed grains, such as white maize, local rice, imported rice, and white beans, in Nigeria from January 2017 to June 2020.
Results. Our result indicates that ARIMA is the best applicable model for white maize and imported rice because it is well fitted to stationary data, as demonstrated in the sample period. The STLM is more appropriate in forecasting white beans. As white beans are highly seasonal in Nigeria, it further explains why the STLM model fits better in forecasting prices. The production of local rice is inconsistent in Nigeria because of erratic rainfall and stiff competition from the importation of rice from other countries. Therefore, and consistent with the analysis, the hybrid model is the best model applicable to local rice because it captures varying trends exhibited in the data.
Originality / scientific novelty. This study suggests most accurate forecasting techniques for specific agricultural commodities in sub-Saharan African countries. It considers forecasting prices of commonly consumed grains and legumes in Nigeria and traded worldwide, such as imported rice, local rice, beans, and maize.
Practical value / implications. The study highlights the importance of appropriate forecasts for policymakers, producers, and consumers to enhance better decision making and serve as an underlying incentive to guide the allocation of financial resources to the agricultural sector, which determines the structure and degree of sectoral growth.
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Arismawati, Paramaditya, and Wahyu Andy Prastyabudi. "AN INVENTORY POLICY ON AGROINDUSTRY SUPPLY CHAIN: A CASE STUDY OF FRUIT SEASONAL IN EAST JAVA." Food & Agribusiness Management 2, no. 2 (March 10, 2021): 46–50. http://dx.doi.org/10.26480/fabm.02.2021.46.50.
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To forestall the overstock or understock, inventory policy often considers thoroughly the optimal order quantity and order time. It is considerably getting more complex in the case of agroindustry as its commodity mostly is classified as perishable goods. The increase of order at a particular season sometimes is not counterbalanced with the number of stocks. The prevalent rationale of this problem is due to none of the appropriate inventory policy is implemented. The purpose of this research is to develop an inventory policy system in the case of agroindustry considering the inventory cost, deterioration rate of perishable fruits, and seasonal variable. Thus, it can be used to maintain the stability of demand while considering those variables. This study employs the periodic review (R,s,S) to construct an inventory policy. The periodic review is determined by order interval (R), a combination of reorder point (s), and maximum level (S). The experimental case study is presented to provide an example of an inventory policy. The inventory policy can alleviate the stocking problem of perishable and seasonal fruits encountered by the agroindustry. The method is able to minimize inventory cost by controlling the inventory systems. This paper offers a model of inventory policy on agroindustry to control and optimize the stocks while reckoning the inventory cost, deterioration rate, and seasonal to fulfil the demand.
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Mohammed, A. E. S., E. A. I. Elkhalil, E. M. Mohamed, H. A. H. Osman, A. E. M. elzein Elzein, and E. E. A. Ahmed. "Socio-demographic relationships and management regimes on use of Frundu as fermented famine food in urban Northern Darfur in Sudan." African Crop Science Journal 30, s1 (May 25, 2022): 37–43. http://dx.doi.org/10.4314/acsj.v30is1.3s.
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Frundu, which in a local term in Darfur for fermented seeds of roselle (Hibiscus sabdariffa L.), is a traditional Sudanese food often used as a meat substitute during famine times. The objective of this study was to assess the relationships between socio-demographic and management regimes on the use of Frundu as a food security commodity in urban Darfur in Sudan. A survey involving 140 respondents was conducted in El-Fashir city markets, where Frundu is a popular commodity in Darfur markets. A semi-structured questionnaire and Chi square analysis were used. It was found that the period of fermentation of Frundu ranged from 3 to 7 days, depending on seasonal temperature. The storage period of Frundu was on average one year. Over 55% of the respondents acknowledged Frundu as critical a coping strategy when famine struck Darfur and the majority of respondents were familiar with Frundu. Frundu is becoming less popular in Darfur for several reasons; including difficulty in obtaining Roselle seeds, competition from the cheapest food items especially during non-famine time; and lack of familiarity with Frundu among young generations. To boost its production, processing and utilisation, farmers should be encouraged to increase its cultivation particularly because it is an important cash crops in Sudan.
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Raza’i, Tengku Said, Viktor Amrifo, Imam Pangestiansyah Putra, Try Febrianto, and Aidil Fadhli Ilhamdy. "Natural Productivity, Morphometrics and Seasonal Distribution of Caulerva Racemosa." E3S Web of Conferences 324 (2021): 03010. http://dx.doi.org/10.1051/e3sconf/202132403010.
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Caulerva racemosa seaweed is a superior commodity with both ecological and socio-economical benefits. It is technologically developed into different products which include antioxidants, antibiotics, medicinal ingredients, cosmetics as well as other organic products and also used conventionally as food ingredients for human’s consumption. Meanwhile, the presence of C. racemosa is observed to decrease as the exploitation rate is one of the factors affecting its stock in the nature. Changes in environmental conditions contribute majorly to its availability in aquatic ecosystems. Furthermore, natural factors in the form of seasonal changes that cause fluctuations in water dynamics are the main focus affecting its lifespan. The results showed that C. racemosa growth parameters, which include percentage cover, productivity, together with morphometrics, failed to be significantly affected by seasonal changes. Meanwhile, the highest percentage cover was found during the northern monsoon, which has an average value of 37.99 ± 7.67 (Average±STDEV), while the lowest was during the eastern monsoons with 28.03 ± 9.09 respectively. The best morphometric size was during the northern monsoon with a tallus dimension of 0.25 with an average of 0.201 ± 0.03 and length of 0.825 ± 0.16, a stolon length of 2.09 with an average of 1.95 ± 0.08, 9 stolon grains with an average of 8 ± 0.2. However, the grain diameter, as well as the biomass, was 0.85 gr and 0.054 gr with an average of 0.825 ± 0.22 gr and the grain biomass was with an average of 0.040 ± 0.01 and 0.041 gr with the best average stolon was 0.031 ± 0.01 g in the western season. The results showed that C. racemosa, which grows up in different seasons, having its highest productivity during the northern season and the lowest one was during the eastern season with an average value of 0.439 ± 0.36 kg/m2 as well as 0.326 ± 0.37 Kg/m2, respectively
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Infante, Saba, Luis Sánchez, Aracelis Hernández, and José Marcano. "Sequential Monte Carlo Filters with Parameters Learning for Commodity Pricing Models." Statistics, Optimization & Information Computing 9, no. 3 (June 22, 2021): 694–716. http://dx.doi.org/10.19139/soic-2310-5070-814.
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In this article, an estimation methodology based on the sequential Monte Carlo algorithm is proposed, thatjointly estimate the states and parameters, the relationship between the prices of futures contracts and the spot prices of primary products is determined, the evolution of prices and the volatility of the historical data of the primary market (Gold and Soybean) are analyzed. Two stochastic models for an estimate the states and parameters are considered, the parameters and states describe physical measure (associated with the price) and risk-neutral measure (associated with the markets to futures), the price dynamics in the short-term through the reversion to the mean and volatility are determined, while that in the long term through markets to futures. Other characteristics such as seasonal patterns, price spikes, market dependent volatilities, and non-seasonality can also be observed. In the methodology, a parameter learning algorithm is used, specifically, three algorithms are proposed, that is the sequential Monte Carlo estimation (SMC) for state space modelswith unknown parameters: the first method is considered a particle filter that is based on the sampling algorithm of sequential importance with resampling (SISR). The second implemented method is the Storvik algorithm [19], the states and parameters of the posterior distribution are estimated that have supported in low-dimensional spaces, a sufficient statistics from the sample of the filtered distribution is considered. The third method is (PLS) Carvalho’s Particle Learning and Smoothing algorithm [31]. The cash prices of the contracts with future delivery dates are analyzed. The results indicate postponement of payment, the future prices on different maturity dates with the spot price are highly correlated. Likewise, the contracts with a delivery date for the last periods of the year 2017, the spot price lower than the prices of the contracts with expiration date for 12 and 24 months is found, opposite occurs in the contracts with expiration date for 1 and 6 months.
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Syrovátka, P., and I. Lechanová. "Price transmission and estimations of price elasticity of secondary demand functions: application on commodity market for food grains." Agricultural Economics (Zemědělská ekonomika) 51, No. 7 (February 20, 2012): 293–303. http://dx.doi.org/10.17221/5110-agricecon.
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The paper is focused on the quantitative analysis of the price transmission and on its use for the estimations of the direct price elasticity of the vertical-derived demand functions. The price transmissions were examined between the commodity markets for the food grain and the consumer markets for the bakery products and flour. The data (1995–2002) were taken over from the Czech Statistical Office (CSO), the Price Statistics (PS) and the Statistics of Family Budgets (SFB). The intensity of the inter-market price transmission was assessed by means of the coefficients of the price transmission elasticity (EPT). For enumerating of EPT, the regression linear models were developed. The explicit as well as the implicit time-definition in the models was tested. The explicit dynamic construction was carried out on the basis of the stationary process with the parabolic trend. After the determination of trend functions, the seasonal component in used time-series was thoroughly investigated by means of the harmonic analysis (G-tests of the individual extremes of the developed periodograms). The implicit dynamization of the linear models was solved on the basis of the first differences of appropriate commodity prices, respectively price levels on the consumer market. For the quantification of the price transmission elasticities, directly dynamized models there were only used only because the model unambiguously achieved better values of characteristics of the statistic verification (correlation index, F-test, T-test). These models also satisfied the economic assumptions in the sense of the vertical price transmissions between the observed market levels and the preservation of the law of diminishing demand. Based on the linear models of the price transmission with parabolic-trend stationarization, it was found out that within the observed period (1995–2002) EPT between commodity market with the food wheat and consumer market with the bakery products and flour reached the average level of +0.1602%. Within the same period, the value of EPT between commodity market with the rye and consumer market with the bakery products and flour reached the average level of +0.1067%. These coefficients were subsequently used together with coefficients of the own price elasticity of consumer demand for the bakery products and flour (ε) to the estimations of the own price elasticity of the commodity demand for food wheat and rye (e). In accordance with the construction of these estimations: e = ε × EPT, it was found out that the average level of the own price elasticity of the demand for food wheat (respectively rye) is about –0.0659% (respectively –0.0441%). Both observed secondary demand functions are therefore strongly inelastic with respect to the reaction on the direct price changes. The commodity demand for the rye seems to be more inelastic.
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Martin, Philip. "COVID-19 and International Labor Migration in Agriculture." DEMIS. Demographic research 1, no. 1 (2021): 38–44. http://dx.doi.org/10.19181/demis.2021.1.1.4.
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Two thirds of the 272 million international migrants in 2019 were employed in the destination country. Demographic and economic inequalities between countries, combined with globalization that reduced barriers to migrants, were expected to continue to increase the number of international migrant workers. Covid-19 closed many national borders to non- essential travelers, with limited exceptions. Seasonal farm workers were one of the notable exceptions, suggesting that many governments do not expect local workers to fill seasonal farm jobs despite record-high unemployment rates. For agriculture, the longer term effects of the pandemic include faster mechanization, more guest workers, and rising imports. Responses are likely to vary by commodity and be shaped by government policies. This article provides a review of the distribution and activities of the world’s 164 million international migrant workers in 2017, including the 111 million in high-income countries. The analysis focuses on the North American migrant worker and the differences between their integration in the agricultural industries. American agricultural systems are integrating in the sense that Canadian blueberries, Mexican avocados and U.S. meat trade freely, but the farm workforces in each country are increasingly Mexican.
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Pebiloka, Sarah, Alfian Johansyah, and Lutfi Aggadhania. "Production volume of mackarel tuna (Euthynnus affinis) as a one of fisheries commodity in Singkawang City." Journal of Fisheries and Marine Applied Science 1, no. 1 (June 25, 2023): 26–32. http://dx.doi.org/10.58184/jfmas.v1i1.36.
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The purpose of this study was to observe the production volume and production value of mackerel tuna (Euthynnus affinis) during 2018 – 2020 in Singkawang City. Mackerel tuna (Euthynnus affinis) is one of the main fisheries commodities in Singkawang City that is included in the pelagic fish species in the Scombridae family. The capture of this fish species occurs throughout the year which is marked by the volume of production that is always there every month. The production volume of mackerel tuna (Euthynnus affinis) fluctuates every year, both monthly and annually. Where, the value depends on seasonal conditions and is dominantly high during the western and transitional seasons, which is as much as 12% and 13%. Meanwhile, during the western season, mackerel tuna (Euthynnus affinis) production volume is always low with a dominance of 3%. The values range from 2,836 kg – 38,963 kg which is found in August and November. The production value of mackerel tuna (Euthynnus affinis) also follows the pattern of production volume. The production value will be high if the production volume is high. The production value ranges from IDR 66,936,000 – IDR 839,534,000.
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Nahorna, L. V., and V. I. Risovaniy. "DISTRIBUTION OF DICTIOCAULOSIS OF CATTLE IN COMMODITY FARMS OF SUMY REGION." Scientific and Technical Bulletin оf State Scientific Research Control Institute of Veterinary Medical Products and Fodder Additives аnd Institute of Animal Biology 21, no. 1 (June 1, 2020): 135–40. http://dx.doi.org/10.36359/scivp.2020-21-1.17.
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The article presents data on the establishment of the spread of dictyocaulosis of cattle in farms of different production facilities of Sumy region. According to researchers, a prominent place among the pulmonary helminthiasis of domestic and wild ruminants belongs to dictyocaulosis. Currently, nematodes are an unresolved problem in cattle farms. The work was performed during 2019 on the basis of the laboratory of the Department of Epizootology and Parasitology of Sumy National Agrarian University, as well as in the conditions of livestock farms of Sumy region, which specialize in the production of marketable milk. Feces were collected from animals systematically throughout the year in different seasons, followed by their study by standardized methods of Berman and Orlov. The intensity of the invasion was determined by counting the number of larvae in 1 g of feces at low magnification. Determination of the intensity of contamination of environmental objects by dictyocaul larvae was performed using the method of M.O Romanenko.
In the course of research and analysis of statistical reporting, it was found that dictyocaulosis is one of the three most common invasive diseases diagnosed in cattle in Sumy region. It has been proven that the frequency of diagnosing cases of respiratory pathology of various etiologies has increased in livestock farms that are unfavorable for dictyocaulosis. A clear seasonal dynamics of dictyocaulosis in livestock of different age categories was established: in calves under one year of age, the maximum lesion of Dictyocaulus viviparus was registered in June, with an invasion extent of 69.21%. In calves under two years of age, dictyocaulosis was registered in spring and autumn (the extent of invasion in April was 62.1%, in late September-early October - 51.4%). In a study of cattle older than two years, the highest extent of dictyocaulous invasion was found in spring and autumn, respectively, 59.7% and 35.1%. The peak of the outbreak was recorded in late autumn, in contrast to the animals of younger age categories. The highest intensity of infestation is found in calves under one year of age in summer, in older age groups - in autumn. When determining the contamination of environmental objects with larvae of Dictyocaulus viviparus, the highest rates were observed on the vegetation of pastures, near water sources with insufficient levels of insolation. Contamination by larvae of Dictyocaulus viviparus of the surface layers of soil and water sources on pastures was lower and amounted to 52.5 and 32.5 %, respectively.
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Xiong, Tao, Chongguang Li, and Yukun Bao. "Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: Evidence from the vegetable market in China." Neurocomputing 275 (January 2018): 2831–44. http://dx.doi.org/10.1016/j.neucom.2017.11.053.
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Nonsoong, Piyapoom, Khamron Mekchay, and Sanae Rujivan. "An analytical option pricing formula for mean-reverting asset with time-dependent parameter." ANZIAM Journal 63 (October 2, 2021): 178–202. http://dx.doi.org/10.21914/anziamj.v63.15172.
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We present an analytical option pricing formula for the European options, in which the price dynamics of a risky asset follows a mean-reverting process with a time-dependent parameter. The process can be adapted to describe a seasonal variation in price such as in agricultural commodity markets. An analytical solution is derived based on the solution of a partial differential equation, which shows that a European option price can be decomposed into two terms: the payoff of the option at the initial time and the time-integral over the lifetime of the option driven by a time-dependent parameter. Finally, results obtained from the formula have been compared with Monte Carlo simulations and a Black–Scholes-type formula under various kinds of long-run mean functions, and some examples of option price behaviours have been provided. doi:10.1017/S1446181121000262
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Widiastuti, Indah, Herpandi Herpandi, Muhammad Ridho, and Nafa Ya’la Arrahmi. "Effects of Liquid Smoke Concentrations on The Characteristics of Smoked Cuttelfish." Jurnal Pengolahan Hasil Perikanan Indonesia 22, no. 1 (April 30, 2019): 24. http://dx.doi.org/10.17844/jphpi.v22i1.25871.
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Cuttlefish is a seasonal and highly perishable fishery commodity. Accordingly, processing is needed to increase the shelf life of the cuttlefish. Fish smoking using liquid smoke has been widely used for processing. This study was aimed to determine the effect of liquid smoke concentration on the quality of smoked cuttlefish product. Four liquid smoke concentrations (0%, 6%, 12%, and 18%) were compared and the product quality was evaluated based on the chemical properties and sensory evaluation. The results showed that liquid smoke significantly affected the moisture and phenol content but the ash, protein, lipid and cholesterol content were not different. The different in liquid smoke concentration also did not significantly affect the sensory properties of the smoked cuttlefish. The chemical analysis revealed the polyunsaturated fatty acids were dominant in the smoked cuttlefish as compared to the monounsaturated fatty acids and the saturated fatty acids (34.4% vs 22.7% vs, 27.0%, respectively)
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Abdallah, Mohamed A. B., Nicole Durfee, Ricardo Mata-Gonzalez, Carlos G. Ochoa, and Jay S. Noller. "Water Use and Soil Moisture Relationships on Western Juniper Trees at Different Growth Stages." Water 12, no. 6 (June 4, 2020): 1596. http://dx.doi.org/10.3390/w12061596.
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An enhanced understanding of plant water uptake is critical for making better-informed management decisions involving vegetative manipulation practices aimed to improve site productivity. This is particularly true in arid and semiarid locations where water is a scarce, yet precious commodity. In this project, we evaluated the interannual and seasonal variability of soil moisture and transpiration in sapling, juvenile, and mature western juniper (Juniperus occidentalis) trees in a semiarid rangeland ecosystem of central Oregon, USA. Transpiration levels were greatest in mature juniper trees in an untreated juniper watershed (Jensen WS), while the lowest transpiration levels were observed in juniper saplings in a treated watershed (Mays WS) where most mature juniper trees were removed in 2005. Significant differences (p ≤ 0.05) in leaf water potential levels observed between predawn and midday readings for all juniper growth stages indicated water is lost over the course of the day. Results showed seasonal precipitation was highly variable over the course of the study (2017 through 2019) and this was reflected in soil water available for tree uptake. This resulted in considerable intra- and inter-annual variation in transpiration. In years with greater winter precipitation amounts (2017 and 2019), juniper transpiration rates were highest during the summer, followed by spring, autumn, and winter. On average, transpiration rates during the summer in the wettest (329 mm) year 2017 were 115 and 2.76 L day−1 for mature and sapling trees, respectively. No data were collected for juvenile trees in 2017. In the drier (245 mm) year 2018, higher transpiration rates were observed in the spring. On average, spring transpiration rates were 72.7, 1.61, and 1.00 L day−1 for mature, juvenile, and sapling trees, respectively. Study results highlight the sensitivity of western juniper woodlands to variations in seasonal precipitation and soil moisture availability.
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Radukić, Snežana, Milan Marković, and Milica Radović. "The Effect of Food Prices on Inflation in the Republic of Serbia." Journal of Central Banking Theory and Practice 4, no. 2 (May 1, 2015): 23–36. http://dx.doi.org/10.1515/jcbtp-2015-0007.
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Abstract In the Republic of Serbia, food accounts for a significant share in the consumer price index through which the inflation is statistically expressed. Therefore, in considerations of the basic factors of increase in the general price level, a special emphasis is placed on the specific features of the market of agricultural-food products. The aim of this research is to peruse the effect of the characteristics of the food market in Serbia on the inflation rate. High volatility of food prices is present because of the instability of this market, mainly due to seasonal fluctuations of supply and the effect of natural factors. Bearing in mind that the increase in food prices is the main determinant of the increase in the inflation rate, the indirect state control is very important so as to maintain price stability. Special importance is attached to the following instruments of economic policy: commodity reserves, storage policy, and fiscal and foreign trade policy.
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Gonzales, Michael J. "Chinese Plantation Workers and Social Conflict in Peru in the late Nineteenth Century." Journal of Latin American Studies 21, no. 3 (October 1989): 385–424. http://dx.doi.org/10.1017/s0022216x00018496.
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As the world capitalist system developed during the nineteenth century non-slave labour became a commodity that circulated around the globe and contributed to capital accumulation in metropolitan centres. The best examples are the emigration of millions of Asian indentured servants and European labourers to areas of European colonisation. Asians replaced emancipated African slaves on plantations in the Caribbean and South America, supplemented a declining slave population in Cuba, built railways in California, worked in mines in South Africa, laboured on sugarcane plantations in Mauritius and Fiji, and served on plantations in southeast Asia. Italian immigrants also replaced African slaves on coffee estates in Brazil, worked with Spaniards in the seasonal wheat harvest in Argentina, and, along with other Europeans, entered the growing labour market in the United States. From the perspective of capital, these workers were a cheap alternative to local wage labour and, as foreigners without the rights of citizens, they could be subjected to harsher methods of social control.1
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Solichah, Tri Utami, and Kordiyana K. Rangga. "Shallot Farmers’ Adaptation Towards Climate Change in Larangan Village, Brebes." Digital Press Life Sciences 1 (2018): 00007. http://dx.doi.org/10.29037/digitalpress.21335.
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This research examined the issue related to farmers’ adaptation towards climate change phenomenon, specifically for farmers whose main commodity was shallot, in Larangan Village, Larangan Sub District, Brebes District. The purpose of this research was to understand the adaptation strategies, which were done by the shallot farmers in their planting field, toward the climate change phenomenon. The study used a combined method (qualitative and quantitative method). The result of this research showed that the adaptation strategies of shallot farmers toward climate change on direct case consisted of several aspects; cultivation aspect (changing the planting pattern, using the pump, changing the land management, and increasing the utilization of pesticide), agricultural financing aspect (storing shallot in<i> para-para</i>), and cultural diversification aspect (horizontal diversification/planting other commodities and seasonal transmigration). While on the indirect case, there were several strategies grouped by aspects of; cultivation aspect (using the superior seed), and Agricultural diversification aspect (<i>merantau</i>).
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Kaeomuangmoon, Thewin, Attachai Jintrawet, Chakrit Chotamonsak, Upendra Singh, Chitnucha Buddhaboon, Panu Naoujanon, Sahaschai Kongton, Yasuyuki Kono, and Gerrit Hoogenboom. "Estimating seasonal fragrant rice production in Thailand using a spatial crop modelling and weather forecasting approach." Journal of Agricultural Science 157, no. 7-8 (October 2019): 566–77. http://dx.doi.org/10.1017/s0021859619000881.
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AbstractFragrant rice is an important export commodity of Thailand and obtaining seasonal production estimates well in advance is important for marketing and stock management. Rice4cast is a software platform that has been developed to forecast rice yield several months prior to harvesting; it links a rice model with a Minimum Data Set (MDS) and Weather Research Forecast (WRF) data. The current study aimed to parameterize and evaluate the model and to demonstrate the use of the Rice4cast platform in forecasting seasonal KDML 105 rice yield and production with local data set. The study area encompassed 77 districts in Thailand, covering 0.94 of the total area of KDML 105 in the country. Minimum Data Sets for the 2013–2015 growing seasons were used for model parameterization and evaluation. The annual statistics from the Office of Agricultural Economics (OAE) were used as a reference basis and planted areas from the Geo-Informatics and Space Technology Development Agency (GISTDA) was used for production estimation. Model evaluation showed good to fairly good agreement between the predicted and reported OAE yield. Production forecasts, however, over-estimated the OAE values considerably, primarily because of the use of GISTDA planted areas that were larger than the harvested areas in the production estimates. Adjustment of the planted areas to account for damaged areas need to be explored further. Nevertheless, the results demonstrated the capability of yield predictions with the Rice4cast, making it a valuable tool for in-season estimates for fragrant rice yield and production.
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Martin, Philip. "Immigration Policy and Agriculture: Possible Directions for the Future." Journal on Migration and Human Security 5, no. 2 (June 2017): 252–62. http://dx.doi.org/10.1177/233150241700500202.
Full textAbstract:
Presidential candidate Trump in 2016 promised to prevent unauthorized migration and deport unauthorized foreigners in the United States, and President Trump issued executive orders after taking office in January 2017 that could lead to a 2,000-mile wall on the Mexico-US border and the removal of many of the 11 million unauthorized foreigners, including one million who work in US agriculture. This paper emphasizes that, especially agriculture in the western United States, has long relied on newcomers to fill seasonal farm jobs. The slowdown in Mexico-US migration since 2008–09 means that there are fewer flexible newcomers to supplement the current workforce, which is aging and settled. Farm employers are responding by offering bonuses to satisfy current workers, stretching them with productivity-increasing tools, substituting machines for workers, and supplementing current workforces with legal H-2A guest workers. Immigration policy will influence the choice between mechanization, guest workers, and imports. Several factors suggest that the United States may be poised to embark on another large-scale guest worker program for agriculture. If it does, farmers should begin to pay Social Security and Unemployment Insurance (UI) taxes on the wages of H-2A workers to foster mechanization and development in the workers' communities of origin by dividing these payroll taxes equally between workers as they depart and commodity-specific boards. Worker departure bonuses could be matched by governments in migrant-sending areas to promote development, and commodity-specific boards could spend monies to reduce dependence on hand labor over time. The economic incentives provided by payroll taxes could help to usher in a new and better era of farm labor.