Table 11 - uploaded by Dilip Kumar Kundu
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Field capacity and permanent wilting point of soils in different depths at different sites/ cropping systems under the Kuanria command area
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... Dabral et al. (2014) employed the Markov chain model for studying dry and wet spells in North Lakhimpur of Assam and found that irrigation supplementations is required for improved crop production. Annual and seasonal rainfall analysis will provide a general picture of the region's rainfall pattern; however, weekly rainfall analysis would be extremely useful for agricultural planning (Mandal et al., 2013). The evaluation of rainfall pattern on monthly basis is helpful and essential for the purpose of crop planning. ...
... Farmers can be benefited from forecasting the dry and wet spells rainfall frequency analysis during SMW season for advanced crop planning models in future (Halder et al., 2016). Mandal et al. (2013) studied the rainfall pattern and soil characteristic of Kuanria Canal irrigation systems by using the Markov chain probability model and found that agricultural operations can be planned in advance, and corrective and contingency actions can be performed during dry periods to avoid crop loss or yield reduction owing to soil moisture stress. Alam et al. (2015) studied the rainfall patterns by using different probability distributions for 21 SMW at Shivalik region for rainfed crop planning and noticed that the 40 percent probability level of minimum promised weekly rainfall was determined to be more indicative of long-term average rainfall data. ...
... By rearranging the ranks in increasing order and picking the highest rank allotted for a given week, the percentage of probability of each rank was computed. The following formula (11) was used to calculate the percentage possibility of onset and withdrawal using Weibull's formula and it has been used in earlier studies [Mandal et al. (2013); Admasu et al. (2014)], ...
Knowledge of the weekly dry and wet spell rainfall analysis is an important aspect for successful agricultural planning as well as water conservation techniques. The rainfall data during the period 1996-2018 from Jorhat station has been analyzed and the average, maximum, minimum, standard deviation and coefficient of variation for each 52 Standard Meteorological Weeks (SMWs) were calculated. The initial and conditional probabilities of dry and wet weeks were investigated by employing Markov Chain probability model. It was observed that, during the first 12 SMWs, the chance of two and three consecutive dry weeks was ranged from 40 to 100% and from 15 to 96%, respectively, while the chance of two and three consecutive wet weeks was ranged from 0 to 20% and 0 to 16%, respectively. The 20 th SMW (14 th to 20 th May) is the earliest and the 23 rd SMW (4 th to 10 th June) is the most delayed week of the onset of rainy season. Also, the 40 th SMW (1 st to 7 th Oct) is the earliest and the 47 th SMW (19 th to 25 th Nov) is the latest of withdrawal of southwest monsoon. The length of the rainy season is 161 days (21 st May to 28 th Oct). The 41 st to 43 rd SMWs were vulnerable to 50% probability for dryness. The monthly effective rainfall (ER) of the station was calculated and it was observed that, the total annual ER accounts only 47% of the average annual rainfall of this region.
... The main weather parameter affecting crop growths are rainfall [10]. Having knowledge on sequences of rainfall variability, events can assist acquiring specific information for agricultural planning [11]. Within variable seasonal rainfall patterns, understanding the events of the occurrence of rain features like; onset and end date of rainy season, dry spells are crucial to decrease the adverse effects and exploit opportunities [12]. ...
... Long-term rainfall data are very useful for making decisions about agriculture water management and crop planning for drought-prone areas (Valipour, 2012). Indian agriculture is predominantly influenced by rainfall, specifically by the south-west monsoon rainfall (June-October) (Mandal et al., 2013). Under a climate variability scenario, the rainfall distribution pattern has become erratic and unpredictable; subsequently increasing the probability of occurrence of a long dry spell or drought-like situation in the country. ...
... Sharma et al. (2010) reported that India stands first among the countries that practice rainfed agriculture both in terms of extent (86 M ha) and value of production. Ghildyal (1989) reported in his study that, in Eastern India, more than 70% of the net cultivated area is under rainfed farming with lowland rice as the principal rainy season crop with productivity of 1,586 kg ha -1 as compared to the country's average productivity of 1,921 kg ha -1 (Mandal et al., 2013). But, it contributes only about 50% compared to irrigated ecosystems. ...
... Similarly, early withdrawal of rains causes a reduction in yield due to severe moisture stress at the critical growth stages like grain formation and seed development (Dixit et al., 2005). The annual and seasonal analysis of rainfall will provide a general idea about the rainfall pattern of the region, whereas the weekly analysis of rainfall will be of much use as far as agricultural planning is concerned (Mandal et al., 2013). Keeping all the scenarios in mind the following objectives were set for the study: ...
Rainfall analysis is essential for agricultural crop planning and water resources management, especially under water scarcity conditions. The 36 years (1977-2013) of rainfall data for Kharagpur were analyzed for characterization of different seasonal events. The Weibull's formula predicted the probability of mean onset on 23rd standard meteorological weeks (SMW) (3rd-9th June) and withdrawal on 43rd SMW (21st-27th October). There was 80-83% probability of a wet week [P(W)] occurring within 25th-35th SMW. The first order Markov chain process shows the conditional probability of one wet week preceded by another wet week [P(W/ W)] varied between 0 and 86%; whereas, dry week preceded by another dry week [P(D/D)] varied in the range of 70 to 100%. The stochastic analysis of successive wet or dry weeks formulates the adaptation strategies to avoid the possible effect of wet or dry spell during cropping seasons. The wet spell analysis suggests rainwater harvesting to control soil erosion and maximization of water use efficiency. The probability of getting consecutive wet [P(W/W)] and dry days [P(D/D)] were varied in the range of 40%-70% and 50%-90%, respectively. The probability of getting different magnitude of rainfall (10 to 40 mm) during the monsoon weeks (25th-39th week) were found to be more than 50% probability level, which suggest for harvesting of excess runoff water for future supplemental irrigation.
... Understanding the events of occurrences of dry spells and intensive rainfall are crucial to decrease the adverse effects of dry spells at sensitive crop development stage and occurrences of runoff in the middle of the season from damaging the crops in the field respectively (Yemenu et al., 2013). Many studies on agricultural planning were performed by Markov Chain probability model (Pandharinath, 1991;Reddy, 1991;Dash and Senapati, 1992;Babuan and Lakshminarayana, 1997;Reddy et al., 2008;Shahraki et al., 2012;Panigrahi et al., 2002;Senthilvelan et al., 2012;Mangaraj et al., 2013;Mandal et al., 2013;Singh et al., 2013) used weekly rainfall values as a standard to determine initial, conditional and consecutive dry and wet spells analysis. Determination of dry and wet spells can prove to famers its usefulness regarding improving crop productivity and intensity (Mandal et al., 2013). ...
... Many studies on agricultural planning were performed by Markov Chain probability model (Pandharinath, 1991;Reddy, 1991;Dash and Senapati, 1992;Babuan and Lakshminarayana, 1997;Reddy et al., 2008;Shahraki et al., 2012;Panigrahi et al., 2002;Senthilvelan et al., 2012;Mangaraj et al., 2013;Mandal et al., 2013;Singh et al., 2013) used weekly rainfall values as a standard to determine initial, conditional and consecutive dry and wet spells analysis. Determination of dry and wet spells can prove to famers its usefulness regarding improving crop productivity and intensity (Mandal et al., 2013). ...
... Time of onset, withdrawal and length of rainy season were selected as key parameters to characterize the rainfall season for crop production (Punyawardena, 2002). Determination of onset, withdrawal and length of rainy season is important for crop planning, choice of suitable crop varieties and to plan compressive strategies for efficient rain water management (Mandal et al., 2013). Dash and Senapati (1992) suggested a procedure to compute the onset and withdrawal of rainy season from weekly rainfall data by forward and backward accumulation method. ...
Knowledge of rainfall pattern is very important for making decision on crop planning and water management. In the present study, Markov Chain probability model was performed to explain the long term frequency behavior of wet or dry weather spells during the main rainy season at Dhera, Central Rift Valley Region of Ethiopia. The study used 24 years (1984-2010) of rainfall data and weekly rainfall data was considered as standard to study the probabilities of occurrences of dry and wet weeks. Some reasonable and significant conclusions regarding specific time for land preparation, supplementary irrigation and soil conservation measures were obtained. The main rainy season starts on the 26 th week (25 th June -1 st July) and remains active upto the 40 th week (1 st -7 th October) this shows a total of 105 days of main rainy season that could occur. The coefficients of variation at the onset and withdrawal week are 69.4 and 99.2% respectively for the study site during the main rainy season. The probability of occurrences of initial and conditional probability is more than 50% on the 26 th week at 10 mm per week threshold limit and 28 th week at 20 mm per week threshold limit, therefore land preparation for planting could be undertaken in 26 th and 28 th weeks respectively for the main rainy season crop cultivation. Initial and conditional probabilities at 20 mm threshold limit per week showed that, supplementary irrigation and moisture conservation practice need to be practiced between 38 th and 40 th week for short duration crops and if the crop duration extend after 40 th week it's evident that supplementary irrigation is needed. In addition, harvesting runoff water for supplementary irrigation and construction of soil erosion measures need to be practiced between 28 th and 33 rd weeks for better rain water management.
This study was undertaken in oromia special zone of Amhara National Regional State to analyze past climate variability. Gridded climate data were obtained from National Meteorology Agency of Ethiopia. Standard stastical descriptors and statistical software like Instat V3.37, MAKESENS, XLSTAT 2014 and Arc GIS 10.1 were employed for the analysis and interpretation for results. The results indicated that the OSZ experienced moderate rainfall variability in kiremt (JJAS) season and very high variability in belg (FMAM) season. Increasing trend in kiremt rainfall while decreasing trend in belg rainfall. Except belg minimum temperature, also temperature trend increased. The median of the start of belg season did not between March and May. The risk of getting 5,7,10 and 15 days of dry spells based on the fitted first order Markov chain probability values indicate in belg season the probability of dry spell occurrence of 10 days' length was 60%. In kiremt season, the dry spell risk of being greater than10 day consecutive dry spells was less than 50%, and it is expected to occurred beteewen 178 DOY (June 26) to 262 DOY (Sept 18). District level of upcoming season climate information in terms of climate variability and encourage farmers to benefit from these services, apply adaptation and mitigation strategy are important for reduce the challenges of cropping practices in the study area.
The objective of this study was to determine the pattern of plant calendar of three types of crops; namely, palawija, rice, andbanana, based on rainfall in Deli Serdang Regency. In the first stage, we forecasted rainfall by using time series analysis, and obtained appropriate model of ARIMA (1,0,0) (1,1,1)¹². Based on the forecast result, we designed a plant calendar pattern for the three types of plant. Furthermore, the probability of success in the plant types following the plant calendar pattern was calculated by using the Markov process by discretizing the continuous rainfall data into three categories; namely, Below Normal (BN), Normal (N), and Above Normal (AN) to form the probability transition matrix. Finally, the combination of rainfall forecasting models and the Markov process were used to determine the pattern of cropping calendars and the probability of success in the three crops. This research used rainfall data of Deli Serdang Regency taken from the office of BMKG (Meteorologist Climatology and Geophysics Agency), Sampali Medan, Indonesia.
