Study on quality, grading and prices of maize in Northern Karnataka

Transcription

1 Internationl Research Journal of Agricultural Economics and Statistics Volume 3 Issue 1 March, Research Paper Study on quality, grading and prices of maize in Northern Karnataka SOMANAGOUDA I. PATIL, N. ASHOKA, CHIDANAND PATIL, GANESHAGOUDA I. PATIL AND K. VASUDEVA NAIK See end of the paper for authors affiliations Correspondence to : N. ASHOKA, Department of Agribusiness Management, University of Agricultural Sciences, DHARWAD (KARNATAKA) INDIA Paper History : Received : ; Revised : ; Accepted: ABSTRACT : Karnataka is one of the Indian states where agriculture is the main occupation of a majority of the population. In order to accomplish the objectives of the study, maize samples were collected during their peak seasons (January-February for maize). The total sample size was 120. In maize, two leading varieties were selected and 30 samples from each variety were selected. The step wise multiple regression analysis was used. In the first model, price has been used as the dependent variable and quality factors as independent variable. In the second model, price has been taken as the dependent variable and non-quality variables as independent variables. In the third model, eye-sight grade has been taken as the dependent variables and quality variables as independent variables. In Belgaum market, the regression coefficient of colour indicated that for yellow maize fetched Rs extra per quintal compared to white maize. KEY WORDS : Maize, Quality and non-quality characteristics, Pricing, Eye-sight grading HOW TO CITE THIS PAPER : Patil, Somanagouda I., Ashoka, N., Patil, Chidanand, Patil, Ganeshagouda I. and Naik, K. Vasudeva (2012). Study on quality, grading and prices of maize in Northern Karnataka, Internat. Res. J. agric. Eco. & Stat., 3 (1) : INTRODUCTION Karnataka is one of the Indian states where agriculture is the main occupation of a majority of the population. Cultivators and agricultural labourers form about per cent of the workforce in the state. Agriculture in the state is characterized by wide crop diversification. The northern part of the state, which covers 12 revenue districts of the state accounting for more than 50 per cent of the geographical area of the state, has vast dry tracts of farming. Among food crops grown in this region, cereals have a major share. Maize, wheat, jowar, and bajra are among the important cereals grown in this part of the state. The region has witnessed in the recent years several suicides of the farmers, who ended their lives on account of crop failures and price crashes for their produce. Thus, it is being increasingly realized in the present days that increased production is meaningful only when it fetches suitable price in the market. Grading is the process of sorting unlike lot of the produce into uniform classes according to certain intrinsic quality factors and physical characteristics that include moisture content, foreign matter, admixture, extent of damage, extent of immature produce, pest infestation, weevil attack, and extent of shriveled produce. The object of grading the produce is enabling the producers of quality produce to get premium prices and improve their earnings. It also aims at making available quality produce to the consumers according to their choice. In the absence of well developed grading, producers don t have the incentives for the production of quality produce. Further, buyers find it difficult to decide on the appropriateness of the quoted prices without the information of quality characteristics available in terms of grades. Thus, of the paramount importance is to develop marketing system on a more scientific basis with the adoption of grade specifications in the buying and selling of the farm produce. This calls for efforts to develop meaningful grade standards on scientific basis. Such a step would be an important milestone towards improving the marketing environment for farm produce, and making agriculture a more viable occupation

2 SOMANAGOUDA I. PATIL, N. ASHOKA, CHIDANAND PATIL, GANESHAGOUDA I. PATIL AND K. VASUDEVA NAIK economically. Maize is one of the important cereal crops of northern Karnataka, which is not only the most important staple food for a majority of people, but also is the source of income. Karnataka is the second largest producer of maize after Andhra Pradesh, fallowed by Rajasthan, Uttar Pradesh, Gujarat, Maharashtra, Himachal Pradesh, Jammu & Kashmir and Bihar. Majority of area and production of maize in northern Karnataka are occupied by Belgaum district, followed by Bagalkote, Bijapur, Haveri and Gadag. Maize is used for different purposes as human food, fodder, poultry feed, cattle feed, fuel purpose, baby corn, health food, beverage, sweet corn, pop corn, bread, pig feed, fish feed, emergency foods and starch. The major assembling markets of Karnataka state are Bangalore, Bellary, Jamkhandi, Hospet, Gangavati, Raichur, Naragunda, Kottur, Koppal, Gokak, Dhanwar, Hubli, Haveri Hirekerur, Ranibennur, Hanagal. Bidar, Hangal, Annagiri, Gadag, Rani benur, Belgaum, Yadgir, Gulbarga, Kushtagi, Bhagalkot, Badami. Though eye-sight grading is followed in a few crops like groundnut and cotton at least occasionally in a couple of markets, the concept of grading whether scientific or eyesight is totally missing for the cereal, i e. maize. As described above, development and adoption of suitable grade standards are the important steps in marketing of farm produce, which can improve farm incomes and give an impetus for quality production programme. The present study is such an attempt. It seeks to develop grade standards for maize, the important cereal of north Karnataka. Such an exercise would serve as a useful input for policy makers, who would seek to promote marketing reforms through the incorporation of grade standards, which serve as a common language both for producers and consumers. The specific objectives of the present study were to determine the quality and non-quality factors influencing the prices and grades of maize and to contrast scientific grading and eye-sight grading vis-à-vis quality and non-quality characteristics. MATERIALS AND METHODS For this study, an important cereal, maize grown in north Karnataka, was considered. Belgaum and Haveri are the two top maize producers of the state. Thus, for the study, the main markets in the four districts head quarters were considered. For accomplishing the objectives of the study, maize samples were collected during their respective peak seasons (January-February). The varieties considered were CP-818 and Alround in each of the two markets. Following the procedure adopted in the case of maize, 15 samples, each weighing 250 g, were collected in respect of each variety in each market. Thus, a total of 30 maize samples were collected each from Haveri and Belgaum markets. It may be noted that each sample was selected from a different lot offered for sale in the market. Thus, while collecting the produce samples, the prices at which the respective lots were disposed of in the market were noted down along with the eye-sight grade designation of each lot according to the buyers. Further, moisture percentage of grains in each sample was measured in the market itself with the help of a moisture measuring instrument. Information on eye-sight grades of lots of produce according to traders was collected from the respective traders buying the lot. Prices at which lots were sold were collected from market committees. Physical quality characteristics : Moisture percentage is the percentage of moisture in cereal grains. It is assumed that moisture percentage will inversely affect price and grade and it is therefore considered as a price discounting factor. The wet grains usually weigh more and after drying, their weight would come down. Hence this factor will have a negative effect on grade and price. Moisture percentage in maize samples was analyzed with the help of moisture analyzer. Foreign matter is the dust, dirt, stones, mud, chaff straw or any other impurity mixed with grains which is termed as foreign matter, and it is considered as a price discounting quality factor. Damaged grains percentage is a price discounting factor. The percentage of grains that are externally damaged or discoloured was calculated as: Weightofdamagedgrains Damagedgra ubs% = Slightly damaged grains percentage is the grains that are superficially damaged or discolored with damage and discoloration not materially affecting the quality are termed slightly damaged grains. These grains adversely affect prices, their percentage is calculated as: Weightofslightlydamagedgrains Slightlyda magedgrains% = Immatured grains are those, which are not properly developed. They reduce overall quality and act as price discounting factor: Weightofimmaturedgrains Immaturedg rains% = Weevilled grain percentage refers to grains that are partially eaten by weevils or insects and adversely affect the quality and grain price: Weightofweevilledgrains Weevilledg rains% = Admixture percentage is the presence of inferior varieties and other grains which will be considered as admixture. Their percentage (presence) is calculated as: Weightofadmixturegrains Admixtureg rains% = Test weight is the weight of hundred seeds drawn 24 Internat. Res. J. agric. Eco.& Stat. 3(1)March, 2012: 23-28

3 STUDY ON QUALITY, GRADING & PRICES OF MAIZE randomly from sample and was recorded in gms and expressed as hundred seed weight. Size was observed by naked eyes with the help of graders in the markets. Grains were classified as small and bold. Colour was observed by naked eyes with the help of graders in the markets. The two types of colours were yellow and white in maize. Appearance was observed by naked eyes with the help of graders in the markets. Grain appearance was classified as bright and dull. Chemical quality characteristics : Sixty samples for each crop were selected for chemical analysis. Analysis of chemical quality characteristics of maize was carried out in the grading laboratory of the Department of Biochemistry, University of Agriculture Sciences, Dharwad. The concepts analyzed were: Fat content was estimated as crude ether extract using moisture free sample. The solvent was removed by evaporation and the residue of fat was weighed. Fat content was calculated as : Weightftheetherextraction Fat% = 100 (moisture + fat)xweightoffibre Crudefibre = Weightofsample Weight of the ash Per cent total ash = Weight of the sample Carbohydrates content was estimated by deducting the protein, fat, moisture, ash and crude fibre from 100 gram of maize sample and represented as per cent carbohydrates. non-quality characteristics : Lot size of the produce is the size of the lot of maize which is on sale, and could be small, medium or large. Variety is meant variety of maize in a particular lot, which was sampled. For maize, they were CP-818 and Alround. Type of soil can influence the produce and hence the final price. Three different types of soils considered in the analysis were red soil, black soil and lateritic soil. Time of sale affects the prices received for the produce. In this study, lean and peak periods were considered as having on price. Type of buyers was hypothesized that the produce price could change depending on whether the buyer is local buyer, trader, commission agent, co-operative and broker. The stepwise multiple regression analysis was conducted to study the impact of quality and non-quality variables on the prices received by the farmers. Further, this technique was also used to study the relationship between grade and quality variables. The quality and non- quality variables considered in the analysis have been described under earlier sections. The non- quality variables are the one that are not inherently related to the quality, but still can influence the price. In order to include non-quality variables such as varieties, day of sale, type of soil, time of harvest, type of buyers etc., are run into the regression model. It was necessary to assign specific sub-classes to these variables. In other words, dummy variables were included for such variables. Similarly, for some quality variables like colour, appearance, size etc., also dummy variables were used. The five estimated stepwise regression models in respect of each crop were as follows: Model-1 and 3 : Y = a+β 1 + β 2 + β 3 + β 4 + β 5 + β 6 + β 7 + β 8 + β 9 + β β β β β β β β e In model-1 : Y= Price of maize In model-3 : Y= Eye-sight grade of maize, which takes value 1for lowest grade, 2 for medium grade, 3 for next best grade and 4 for best grade. = Moisture (%) = Fat (%) = Crude protein (%) = Carbohydrates (%) = Crude fibre (%) = Acid content (%) = Ash (%) = Foreign matter (%) = Admixture (%) 0 = Damaged grains (%) 1 = Immatured grains (%) 2 = Weevilled grains (%) 3 = Slightly damaged grains (%) 4 = Test weight (g) 5 = Size dummy variable, which takes value 0 for small grain and 1 for bold grain 6 = Colour dummy variable, which takes value 1 for white/whitish colour and 0 for yellow/yellowish colour 7 = Appearance dummy variable, which takes value 1 for dull appearance and 0 for bright appearance e = Random error Model-2: Y = á+â 1 + â 2 + â 3 + â 4 + â 5 + â 6 + â 7 + â 8 + â 9 + â â e where, Y= Price of maize = Variety dummy, which takes value 1 for Alround variety and 0 for CP-818 in respect of maize. = Red soil dummy, which takes value 0 for red soil and Internat. Res. J. agric. Eco. & Stat. 3(1) March, 2012:

4 SOMANAGOUDA I. PATIL, N. ASHOKA, CHIDANAND PATIL, GANESHAGOUDA I. PATIL AND K. VASUDEVA NAIK 1 otherwise. = Black soil dummy, which takes value 1 for black soil = Small lot dummy, which takes value 0 for small lot and 1 otherwise. = Medium lot dummy, which takes value 0 for medium lot and 1 otherwise. = Time of sale dummy, which takes value 1 for peak period harvest and 0 for lean period harvest. = Local buyer dummy, which takes value 1 for local buyer = Commission agent buyer dummy, which takes value 1 for commission agent and 0 otherwise = Broker buyer dummy, which takes value 1 for broker 0 = Trader buyer dummy, which takes value 1 for trader 1 = Co-operative buyer dummy, which takes value 1 for co-operative e = Random error RESULTS AND DATA ANALYSIS The different models based on stepwise multiple regression equations was used for maize. In the first model, price has been used as the dependent variable and quality factors as independent variable. In the second model, price has been taken as the dependent variable and non-quality variables as independent variables. In the third model, eyesight grade has been taken as the dependent variable and quality variables as independent variables. There were totally 28 quality and non-quality variables considered in the study. In order to include quality and non-quality variables such as foreign matter, damaged grains, immature grains, weevilled grains, test weight, size, colour, moisture per centage, variety, date of sale, type of soil, time of sale, etc., it was necessary to assign specific sub classes to some of these variables. The variables of this type are usually called dummy variables. Out of 17 quality variables included in this model, as many as six variables were found to be highly significant in different markets. They were colour dummy, carbohydrates, slightly damaged grains, test weight, crude fibre and immature grains. Step wise multiple regression functions were separately run for each selected market. The direction of their association between the dependent variable prices and quality variables as independent variables was in conformity with the hypotheses that can be seen in Table 1. In Belgaum market, the regression coefficient of colour indicated that for yellow maize fetched Rs extra per quintal compared to white maize. Slightly damaged grains coefficient indicated that one unit increase in slightly damaged grains caused price of maize to decrease by Rs per quintal. The regression coefficient of carbohydrates indicated that for every one unit increase in the carbohydrates, price of maize increased by Rs per quintal. The R 2 value was 0.73 indicating 73 per cent of the total variation, explained by the independent variables (Biradar, 2007). In Haveri market, the regression coefficient of test weight indicated that as the weight of 100 seeds increased by one unit, price of maize increased by Rs per quintal. The crude fibre content showed the negative sign which indicated that for every one unit increase in fibre content, the price decreased by Rs per quintal. The regression coefficient of immature grains was indicating that maize price fell as the immature grain percentage increased. The R 2 value was 0.57 indicating 57 per cent of the total variation in price of maize as was explained by the quality variable. In overall situation, the regression coefficient of colour dummy indicated that maize price increased by Rs for yellow maize compared to white grains. The coefficient of test weight showed positive that is, as the weight of 100 seeds increased, maize fetched higher price in the market. The regression coefficient of crude fibre indicated that for one unit increase in the fibre content, the maize price decreased by Rs per quintal. The R 2 value was 0.47 indicating 47 per cent of the total variation in price of maize was explained by the 26 Table 1: Estimated coefficients of quality characteristics influencing the price of maize Model-1 Belgaum market Haveri market Overall Variables Sr. No. Regression co-efficient Regression co-efficient Regression co-efficient R Colour dummy 62.77** (8.50) ** (9.57) 2. Carbohydrates 19.27* (7.97) Slightly damaged grains * (28.60) Test weight * (18.07) 39.85** (11.1) 5. Crude fibre ** (30.88) ** (20.32) 6. Immatured grains * (48.71) - * and ** indicate significance of values at p=0.05 and 0.01, Figures in the parenthesis indicates standard error Internat. Res. J. agric. Eco.& Stat. 3(1)March, 2012: 23-28

5 STUDY ON QUALITY, GRADING & PRICES OF MAIZE quality variable. In order to study the effect of different non-quality variables in each market, dummy variables were generated and used in model-2. In this model, price was the dependent variable and non-quality variables were independent variables (Table 2). In Belgaum market, regression coefficient of the black soil dummy variable had a significant positive coefficient of 28.7 indicating that, maize grains grown on black soil fetched a premium price of 28.7 compared to maize grain grown on red soil. The coefficient of time of sale dummy (-51.61) was associated with negative sign indicating that maize produce fetched around Rs. 35 less during peak period compared to lean period. Similarly, local buyer dummy was also associated with negative coefficient, which indicated that the price was less by Rs. 52 per quintal on an average when the produce was bought by local buyer. The R 2 value was 0.81 indicating 81 per cent of the total variation in price of maize was explained by the quality variable. In the case of Haveri market, the result showed that maize price was mainly influenced by time of sale and variety ( and 27.00). In particular the maize produce fetched on an average Rs. 63 less compared to lean season. The coefficient of variety dummy indicated that the price per quintal of allround variety of maize was more than that of CP-818 variety by Rs. 27 per quintal. The other variables were not retained in the model, since it did not contribute in price determination. The R 2 value was 0.89 indicating 89 of the total value variation in price of maize as explained by the explanatory variables. In overall regression the coefficient of time of sale dummy was and it was statistically significant at 1 per cent level. Small lot dummy also turned out to be significant at 5 per cent level. Both had expected signs. The R 2 value was 0.52 indicating 52 per cent of total variation in price was explained by the explanatory variables. The stepwise multiple regression function was estimated separately for each market and for aggregate market, to study effect of quality variables on eye sight grade. In this model, eye sight grade was the dependent variable and quality factors were independent variables and the regressions co efficient are presented in the Table 3. In Belgaum market, regressions coefficients of colour dummy and moisture percentage were and -0.57, respectively and were statistically significant. The colour dummy coefficient as found to significant at 1 per cent level and moisture percentage was found to be significant at 5 per cent level. The R 2 value was 0.81 indicating 81 per cent of the total variations in eye sight grade as explained by quality factors. These results indicated that yellow grains and low moisture content increased the eye-sight grade standards (Bockstael et al., 1987). In Haveri market, regression coefficients of moisture percentage, crude protein and admixture percentage were -0.47, 1.20 and -1.27, respectively are statistically significant. The crude protein and Admixture percentage are statistically significant at 1 per cent level and moisture percentage is statistically significant at 5 per cent level. The R 2 value was 0.89 indicating 89 per cent of the total variation in the eye sight Table 2: Estimated coefficients of non-quality and characteristics influencing the price of maize Model- 2 Belgaum market Haveri market Overall Variables Sr. No. Regression co-efficient Regression co-efficient Regression co-efficient R Black soil dummy 28.7* (13.1) Time of sale dummy ** (12.38) ** (12.60) ** (10.11) 3. Local buyer dummy * (21.65) Variety dummy * (12.35) - 5. Small lot dummy * (10.68) * and ** indicate significance of values at P= 0.05 and 0.01, Figures in the parenthesis indicate standard error Table 3: Estimated coefficients of quality characteristics influencing the eye-sight grade of maize Model- 3 Belgaum market Haveri market Overall Sr. No. Variables Regression co-efficient Regression co-efficient Regression co-efficient R Colour dummy -1.40** (0.14) ** (0.16) 2. Moisture % -0.57* (0.24) -0.47* (0.20) - 3. Crude protein ** (0.30) - 4. Admixture % ** (0.40) - 5. Foreign matter % ** (0.61) * and ** indicate significance of values at p= 0.05 and 0.01, Figures in the parenthesis indicate standard error Internat. Res. J. agric. Eco. & Stat. 3(1) March, 2012:

6 SOMANAGOUDA I. PATIL, N. ASHOKA, CHIDANAND PATIL, GANESHAGOUDA I. PATIL AND K. VASUDEVA NAIK grade of maize was explained by the quality variables in Haveri market. The coefficients indicated that high moisture, high admixture and low crude protein would reduce grade. In overall case, the regression co efficient of colour dummy and foreign matter percentage and -1.78, respectively were statistically significant at 1 per cent level indicating that white colour and high foreign matter would reduce grade. The other variables were not statistically significant. The R 2 value was 0.52 indicating 52 per cent of the total variation in the eye sight grade of maize in aggregate market as explained by the quality characteristics. Authors affiliations: SOMANAGOUDA I. PATIL, CHIDANAND PATIL, GANESHAGOUDA I. PATIL AND VASUDEVA NAIK K., Department of Agribusiness Management, University of Agricultural Sciences, DHARWAD (KARNATAKA) INDIA LITERATURE CITED Biradar, Sangmesh (2007). On development of picking wise grade standards and study of price behaviour of cotton in Raichur and Hubli markets of Karnataka. MBA (Agribusiness) Thesis, University of Agricultural Sciences, DHARWAD, KARNATAKA (India). Bockstael, N. E., Kilmer, L. L., Nichols, J. C. and Armbruster, W. J. (1987). Economic efficiency issues of grading and minimum quality standards. Econ. Effi. Agri.and Food Mktg,, University Maryland, College Park, USA, pp Murthy, C. (2005), Study on quality and non-quality characteristics influencing price for vegetables in north Karnataka. Ph.D. (Agribusiness) Thesis,University of Agricultural Sciences, DHARWAD, KARNATAKA (India). * * * * * * * * 28 Internat. Res. J. agric. Eco.& Stat. 3(1)March, 2012: 23-28