Potato is one of the staple foods and cash crops in Bangladesh. It is widely cultivated in all of the districts and ranks second after rice in production. Bangladesh is the fourth largest potato producer in Asia and is among the world’s top 15 potato producing countries. The weather condition for potato cultivation is favorable during the sowing, growing and harvesting period. It is a winter crop and is cultivated during the period of November to March. Bangladesh is mainly an agricultural based country with respect to agriculture’s contribution to GDP, employment and consumption. Potato is a prominent crop in consideration of production, its internal demand and economic value. Bangladesh has a big economic activities related to potato cultivation and marketing, especially the economic relations among farmers, traders, stockers and cold storage owners. Potato yield prediction before harvest is an important issue for the Government and the stakeholders in managing and controlling the potato market. Advanced very high resolution radiometer (AVHRR) based satellite data product vegetation health indices VCI (vegetation condition index) and TCI (temperature condition index) are used as predictors for early prediction. Artificial neural network (ANN) is used to develop a prediction model. The simulated result from this model is encouraging and the error of prediction is less than 10%.
Rice production in Bangladesh is a crucial part of the national economy and providing about 70 percent of an average citizen’s total calorie intake. The demand for rice is constantly rising as the new populations are added in every year in Bangladesh. Due to the increase in population, the cultivation land decreases. In addition, Bangladesh is faced with production constraints such as drought, flooding, salinity, lack of irrigation facilities and lack of modern technology. To maintain self sufficiency in rice, Bangladesh will have to continue to expand rice production by increasing yield at a rate that is at least equal to the population growth until the demand of rice has stabilized. Accurate rice yield prediction is one of the most important challenges in managing supply and demand of rice as well as decision making processes. Artificial Neural Network (ANN) is used to construct a model to predict Aus rice yield in Bangladesh. Advanced Very High Resolution Radiometer (AVHRR)-based remote sensing satellite data vegetation health (VH) indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) are used as input variables and official statistics of Aus rice yield is used as target variable for ANN prediction model. The result obtained with ANN method is encouraging and the error of prediction is less than 10%. Therefore, prediction can play an important role in planning and storing of sufficient rice to face in any future uncertainty.
Rice is a dominant food crop of Bangladesh accounting about 75 percent of agricultural land use for rice cultivation and currently Bangladesh is the world’s fourth largest rice producing country. Rice provides about two-third of total calorie supply and about one-half of the agricultural GDP and one-sixth of the national income in Bangladesh. Aus is one of the main rice varieties in Bangladesh. Crop production, especially rice, the main food staple, is the most susceptible to climate change and variability. Any change in climate will, thus, increase uncertainty regarding rice production as climate is major cause year-to-year variability in rice productivity. This paper shows the application of remote sensing data for estimating Aus rice yield in Bangladesh using official statistics of rice yield with real time acquired satellite data from Advanced Very High Resolution Radiometer (AVHRR) sensor and Principal Component Regression (PCR) method was used to construct a model. The simulated result was compared with official agricultural statistics showing that the error of estimation of Aus rice yield was less than 10%. Remote sensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.
A better understanding of the relationship between satellites observed vegetation health, and malaria epidemics could help mitigate the worldwide increase in incidence of mosquito-transmitted diseases. This research investigates last 17- years association between vegetation health (condition) index and malaria transmission in Bikaner, Rajasthan in India an arid and hot summer area. The vegetation health (condition) index, derived from a combination of Advanced Very High Resolution Radiometer (AVHRR) based Normalized Difference Vegetation Index (NDVI) and 10-μm to 11-μm thermal radiances, was designed for monitoring moisture and thermal impacts on vegetation health. We demonstrate that thermal condition is more sensitive to malaria transmission with different seasonal malaria activities. The weekly VH indices were correlated with the epidemiological data. A good correlation was found between malaria cases and Temperature Condition Index (TCI) one at least two months earlier than the malaria transmission season. Following the results of correlation analysis, Principal Component Regression (PCR) method was used to construct a model of less than 10% error to predict malaria as a function of the TCI.
Information on current and anticipated moisture and thermal condition from satellite data represents a source of affordable yet careful information for malaria forecasters to implement and control of epidemic. During the last decades Orissa state in India suffered from highest level of malaria incidence. This situation requires frequent monitoring of environmental conditions and dynamics of malaria occurrence. During 1985 to 2004 the NOAA AVHRR global vegetation index (GVI) dataset and its vegetation health (VH) have been studied and used as proxy for malaria fluctuation. This paper discusses applications of VH for early detecting and monitoring malaria incidence in Orissa. A significant relationship between satellite data and annual malaria incidences is found at least three months before the major malaria transmission period.
This study examined the relationship between environmental factors and malaria epidemic. The
objective is to use NOAA environmental satellite data to produce weather seasonal forecasts as a
proxy for predicting malaria epidemics in Tripura, India which has the one of the highest
endemic of malaria cases in the country. An algorithm uses the Vegetation Health (VH) Indices
(Vegetation Condition Index( VCI) and Temperature Condition Index (TCI)) computed from
Advance Very High Resolution Radiometer (AVHRR) data flown on NOAA afternoon poler
orbiting satellite.. A good correlation was found between malaria cases and TCI two months
earlier than the malaria transmission period. Principal components regression (PCR) method was
used to develop a model to predict malaria as a function of the TCI. The simulated results were
compared with observed malaria statistics showing that the error of the estimates of malaria is
small. Remote sensing therefore is a valuable tool for estimating malaria well in advance thus
preventive measures can be taken.
A better understanding of the relationship between malaria epidemics, satellite data and the
climatic anomalies could help mitigate the world-wide increase in incidence of the mosquitotransmitted
diseases. This paper analyzes correlation between malaria cases and vegetation health
(VH) Indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI))
computed for each week over a period of 14 years (1992-2005). Following the results of correlation
analysis the principal components regression (PCR) method was performed on weather components
(TCI, VCI) of satellite data and climate variability during each of the two annual malaria seasons to
construct a model to predict malaria as a function of the VH. A statistically significant relation was
found between malaria cases and TCI during the month of June-July and September-October.
Furthermore the simulated results found from PCR model were compared with observed malaria
statistics showing that the error of the estimates of malaria is 5%.
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