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ThesisItem Open Access FARM LEVEL PRODUCTION TECHNOLOGIES, POST HARVEST LOSSES AND MARKETING EFFICIENCY OF MAJOR VEGETABLES: A STUDY IN THE DARRANG DISTRICT OF ASSAM(AAU, Jorhat, 2015-07) Dutta, Sumi; Barman, R. N.Vegetables are important supplements to the human diet. India ranks 2nd after China in the world with total production of 162.19 million tones. In Assam area under vegetable is about 2.73 lakh hectares with annual production of 49.79 lakh MT.Vegetables are highly perishable food products in nature and in the process of supply from the farm level to the market level. The seasonal gluts and lack of infrastructure and marketing facilities in the developing countries have significant effect on the extent of post harvest losses of vegetables. In Assam not much information is available regarding the farm level adoption of production technologies and quantum of post harvest loss of vegetables at various stages of marketing and its impact on marketing efficiency. The present study was conducted in the Darrang district of Assam and was designed to collect information regarding the level of production technologies, assessment of productivity, technology gaps of some major vegetables, physical and value loss at farm level and at various stages of marketing and to examine the impact of post harvest loss on farmer’s net price, marketing costs, margins and efficiency of major vegetables. The study was based on both primary and secondary data. Bechimari and Kharupetia two major vegetable growing areas were identified.Both tabular and functional analysis was done in analyzing data. Cost concepts used in farm management studies were applied to calculate costs in the present study and results indicated that cost of production increased with increase in size of the farm and regardingknowledge on different parameters of vegetable production it was observed that almost all farmers (99.33%) had the knowledge about the ploughing and application of organic manures (99.00%).The extent of adoption gaps for selected vegetables is estimated and 7 technology components were found. More than 30 percent of technology adoption gaps were observed in terms of technology components T4, T5 and T6 i.e. Manuring and fertilization, Number of irrigations applied & Intercultural and weed control.As vegetables are perishable in nature so during the process of distribution and marketing substantial losses are incurred. The post harvest loss was estimated first at farm level and then at market level and in case of market level it can be clearly observed that the physical loss was highest in itinerant level followed by wholesaler and retailer level. 4 major marketing channels of vegetables were identified namely Channel I:Producer–Consumer,Channel II:Producer–Retailer–Consumer,ChannelIII:Producer–Wholesaler–Retailer-Consumer,ChannelIV:Producer–Itineranttraders–Wholesaler-Retailer-Consumer. Marketing efficiency was estimated in different marketing channels and it was found that Channel II was the most efficient channel and Channel IV was the least efficient channel.ButChannel IV was considered as the most effective one because farmers sold majority of their marketed surplus through Channel IV.ThesisItem Open Access TOTAL FACTOR PRODUCTIVITY IN ASSAM AGRICULTURE(AAU, Jorhat, 2016-07) Buragohain, Rinumoni; Deka, N.Technical change in agriculture increases production at the same level of input-use and enables it to avoid trapping into Ricardo’s law of diminishing returns to which the sector is more prone. Total Factor Productivity (TFP) is often seen as the real driver of growth within an economy. Many studies showed that, different factors of TFP like Policy support, production strategies, public investment in infrastructure, research and extension for crop, livestock and fisheries etc., have significantly helped in increasing the agricultural productivity, food production and its availability. Assam’s economy is predominantly agrarian. Agriculture and its allied activities play an important role in the socio- economic development of the State of Assam as this sector is the major contributor to the State economy as well as providing livelihood to a significant proportion of the population of the State. Assam accounts for a fairly significant share of the country’s acreage and output of many crops. In spite of having high inherent potentiality, Assam’s agriculture is yet to experience modernization in real sense. Agriculture in Assam exhibits most of the characteristics of underdeveloped/backward agriculture. During last few decades, Assam Government has made lots of investments in agricultural sector for the development of the sector. But, whether these investments have been contributing significantly towards the agricultural growth in the real scenes or not, it is very much important to know. Considering this, the present study was conducted with the three objectives to estimate the TFP growth and its contribution to Assam agriculture, to examine the determinants of TFP in Assam agriculture and to suggest policy measures for improving TFP in Assam agriculture. The study was conducted for three crops rice, jute and rapeseed and mustard both individually and collectively termed as total crops for the time period of two decades from 1991-92 to 2010-11. Further, for more convenience, the entire study period was divided into two sub periods viz., 1st period (191-92 to 2000-01) and 2nd period (2001-02 to 2010-11). The study was based on secondary data collected from different reputed published sources. Tornqvist Theil index was used for computing TFP indices of the three selected crops. The input, output and TFP indices were calculated both in current price and constant price (at price of 1991-92) of the inputs and outputs to find out whether there any nominal price effect was existed or not. Again, the indices were also computed for per hectare area and total area under the selected crops in Assam to know how efficiently inputs were used under both situations. In order to evaluate the determinants of TFP in Assam, the TFP index was regressed against the variables viz., rice area under flood, no. of villages electrified, rainfall, share of irrigated area to total cropped area, expenditure in Agricultural research and education, investment in Agriculture and allied activities, share of HYV area to total rice area, rural literacy and cropping intensity by using Cobb Douglas production function. The results of the study revealed that, all the three crops rice, jute and rapeseed and mustard of the state had experienced negative TFP growth at current price, but at constant price, it was estimated to be positive in both per hectare area and total area for the study period. It revealed the presence of the nominal effect of increased input costs resulted in a lower (negative) TFP at current price pointing out the occurrence of gap between the farm harvest prices of the farm outputs and costs of inputs incurred in production of those three major crops in the state. Again, except rice, for other two crops, jute and rapeseed and mustard, TFP index were estimated to be higher in per hectare area rather than their total area. It implied that, in jute and rapeseed and mustard both, inputs were more efficiently allocated and utilized per hectare area rather than total area in the state. Only rice was found to have highly significant TFP growth (at constant price). Other two crops were reported to have positive TFP with very lower growth rate, but not significantly in Assam. Expenditure in Agricultural research and education, rural literacy, irrigation and cropping intensity were found to have positive impact on TFP of all three crops both individually and collectively. However, none of the selected variables was found to have significant impact on TFP of jute as well as rapeseed and mustard. Investment in agriculture and allied activities also was an important source of TFP for all selected crops except jute. In rice, HYV area also contributed positively in TFP. Villages electrified and rainfall exhibited no any effect towards TFP growth of all selected crops individually as well as collectively also. The findings of the study have important policy implications for construction of proper price structure, improving input-use efficiency in total area under these crops, allocating scarce public resources to agricultural research, education, irrigation etc. and increasing HYV area and cropping intensity for enhancing the TFP in the state for better sustainable growth in agriculture.ThesisItem Open Access An Economic Analysis of Crop Production Risks and Measures Adopted by Farmers of Riverine Area in the Upper Brahmaputra Valley Zone of Assam(AAU, Jorhat, 2014) Boruah, Luhit Kumar; Barman, R.N.The present study was designed to assess the factors influencing crop production risks along withanalyzing the quantum of crop production risks and measures adopted by the farmers of riverine areas in the Upper Brahmaputra Valley (UBV) zone of Assam. The study also attempted to suggest feasible risk minimized optimal crop production plans to the farmers of the riverine areas. The important objectives in the study were (i) To study the factors influencing risks associated with crop production in the riverine areas of Upper Brahmaputra Valley zone (ii) To measure the quantum of risks and various risk minimization strategies adopted by the farmers (iii) To suggest appropriate risk minimization crop production plans for the farmers of the study area. A multistage stratified random sampling technique was used to select the ultimate sample unit. The farmers were categorized into three size groups that is small (less than 2 ha), medium (2-less than 4 ha) and large (4 ha and above 4 ha). Rice (31.08%) is the major crop in the study area, followed by summer vegetables (24.44%), rabivegetables (15.81%), pulses (13.01%), potato (5.23%), sugarcane (4.65%), oilseeds (3.40%) and chilli (2.38%). The riverine areas, because of its critical locations are always subjected to high risks of crop loss. The most important risk factor for the three groups of farmers in the study area was the flood and excessive rainfall (49.43%),followed by other factors like soil erosion (12.29%), insufficient rainfall/drought & drought like situation (9.17%), pest and diseases (5.41%), government and agricultural policy (4.24%), input costs (3.42%), insufficient and non availability of farm machinery in time (2.53%), insufficient family labour and difficulties in finding labour(3.40%), lack of contract growing (2.05%),interest rates and debt situation (1.47%), economic condition (1.24%), health problem (1.10%), climatic conditions (1.11%), lack of keeping farm record (0.97%),theft (1.49%),crop prices (0.41%) and crop yields (0.27%). Altogether 18 risk management strategies were listed out of which 17 strategies were followed by the farmers of the study areas. Some of these risk management strategies were ex-ante and some were ex-post. The most effective risk management strategy for the three groups of farmers in the study area was the growing more than one crop (10.11%) followed by strategies like bunding (9.43%), manure and fertilizer application (8.42%), spraying and drenching of pesticides (8.30%), irrigation (8.30%), drainage (8.30%), Intercultural operation including mulching (8.29%), growing more than one variety and adjusting sowing time (7.81%), use of plant growth regulator (4.54%), planning expenditure (4.37%), doing off farm works (4.33%), avoiding high risk farm land (3.40%), ITK (3.18%), arranging resource use (3.12%), reducing debt burden (2.92%) keeping/maintaining farm records (2.74%) and contract growing (2.44%). The MOTAD model was used to suggest appropriate optimal crop production plans by minimizing risks for all size groups of farms of the study area. Ten optimal crop production plans viz., plan-1 through plan-10 were suggested for the three groups of farmers of the study area for adoption. Plan- 1 is a risk minimum plan with lowest expected income while Plan- 10 is high risk plan with highest expected income. Increases in cropping intensities have been observed in the suggested optimal plans as compared to the existing plans. The highest cropping intensity was observed in case of small farms in the maximum expected income plan -10 as compared to medium and large farms.