It is believed that due to the rapid progress of industrial development the problem of labour shortage will surface again and may be more acute for some small planters because labourers working with large estates are receiving higher benefits. Therefore there is immediate need to take socio-economic studies to be able to predict the rate at which labour shortage may develop and simultaneously deal with the mechanisation problem.
The main objective for sugar cane is to cope with the expected problem concerning labour while for crop production we need to lower the cost of production first because of existing poor labour because the period of cane harvest co insides with the greatest demand.
Mechanization in sugar cane
Except for land preparation, only mechanical loading has been successfully undertaken so far though it is not widely used. In order to look for other satisfactory operations, further studies must be pursued. The choice of equipment must also take into account work done on large plots as well as small plots. The following components of mechanization are mainly Harvesting and loading, Land preparation and Planting.
Get Help With Your Essay
If you need assistance with writing your essay, our professional essay writing service is here to help!
Harvesting and loading
A self-loader for cane baskets was fully developed and successfully put into operation which was later improved by adding an uploading system. On account to labour shortage as from 1973 a pushpiler loader and a self-propelled grab loader were used but these lacked a proper derocking and thus stones entering mills caused the work to stop. Later a modification including the placement of a cult stalk in field and trash disposal in the system of operation has successfully avoided these problems. Now the Bell loader has proved to be the most efficient loader in Mauritius.
Mechanical harvesters which included whole stalk and chopper were tested and these were very expensive that is its capital and regrowth of cane was affected. But as benefit their use improved care supply to mills specially the productivity in labour drops at the end of harvest.
Land preparation
Proper research in different ways of land preparation was used until the Second World War. High powered bull dozers were being imported after which derocking has been thoroughly worked out on large estates and lands while this same procedure did not produce satisfactory results on small planters’ land. It has been technically and economically proven that breakage of the top lava bed has been suitable for cane growth. First and foremost land preparation methods were developed to suit sugar cane production and now it must be approached to suit the mixed crop pint system. Proper land preparation is a must for mechanization of cultural operations like planting, harvesting of certain crops to work efficiently. For the above reasons the operations performed as well as equipments used must be viewed again. There is a need for appropriate choice of equipment for the removal of stones, destruction of cane stools and other crop residues and tillage and seed bed preparation for different crops.
Since objective is to increase efficiency in production and also lowering cost of production, further studies need to be carried out. Similarly a researcher (Dlamini,2005) also state that there is a need for the smallholder sugarcane farmers to adopt new technologies that are cost effective so that they can still continue to make profits from the product, but the small-scale sugarcane farmers are not familiar with these new technologies.
Literature Review
The efficiency of the Adoption decision
The two researcher Michael R. Rahm and Wallace E. Huffman’s objectives was first to present a model of adoption behavior and secondly to explain differences in farmer’s decisions to adopt reduced-tillage practices and the efficiency of farmers’ adoption decisions. The empirical result, obtained when the model is being fitted to a sample of Iowa farms, shows that the probability of adopting reduced tillage in Iowa corn enterprises differs widely across farms and depends mainly on soil characteristics, cropping system, and size of farming operation. Furthermore, it has been found that an investment in farmer formal schooling and continuing education enhances the efficiency of the adoption decision. The result of a similar study done by Fane and Khaldi, showed that farmers who have invested in more years in forming schooling are better cost minimizers.
A random sample of all Iowa farms was done in 1976 to those farmers having their farm value production greater than or equal to $2,500(Hoiberg and Huffman). The survey was designed by the Iowa State University and the detailed information about the farm business and household was collected using the method of face to face interview. Thus a sample of 869 farms data was fitted to the model below which was to determine the efficiency of adoption decision:
ln Ei = ln |Di – ρi| = γ0 + ∑ γizij + εi,
where the eight defined human capital variable (explanatory variable) according to the authors are years of formal schooling completed by farmers, any vocational training obtained in high school, number of years in farming, completed any subject or study concerning agricultural science at college, sources of information received, health condition, attended any meeting and finally the frequency of attending any field day or meeting.
In order to propose a model, first t was denoted to represent as technology index(where 1 for old technology and 2 for new one) and also a utility function U(Rti, Ati) was denoted in order to ranks the ith firm’s preference for these technologies. Thus a probit model use to model the adoption behavior since Utility function, that is, both Rti and Ati are unobserved and unavailable. Firstly, a linear relationship was postulated for the ith firm and also the utility derived from the tth technology where Xi (soil type, cropping system, farm size)
Uti = Xiαt + eti, t =1, 2: and i = 1,…, n
Hence base upon this an index Di was used to denote the adoption decision where:
1 if U1i < U2i new technology adopted
Di =
0 if U1i ≥ U2i old technology continued
Thus the probability that Di is equal one can be shown as:
Pi = Pr (Di = 1) = Pr (U1i < U2i)
= Pr (Xiα1 + e1i< Xiα2 + e2i)
= Pr (e1i – e2i< Xi (α2-α1))
= Pr (µ<Xiβ) = F (Xiβ), where it is the cumulative distribution function for µi evaluated at Xiβ. β is replaced by a consistent estimator Ь and hence ρi = F(Xi Ь).
The absolute difference between Di and ρi was proposed as an indicator to the efficiency of adoption in this study.
Ei = ln |Di – ρi| = g (γizij + εi,),g’>0.
Only 797 of sample farms were fitted to the model as 72 observations were lost due to missing of data. Three regression equations were run where the first one was regressed on all of the defined human variables but they are not significantly different from zero. The second regression equation that was done with only Education and Continue show that the coefficient of Education and that of Continue are negative and significantly different from zero at one percent significance level. Thus these two variables tend to increase the adoption efficiency. However, the report does not mention how come the selection of explanatory variables was chosen while doing the second regression for the model. A small R2 was obtained and where the two researchers (Rahm M and Huffman W) were expecting to have a small R2 for regression equations where it showed normally a small share of the variance in this measure of adoption efficiency explained by variation in the human variables. An F-test was done on Equation two was 6.31 while the critical value of F-test with 4 and 490 degrees of freedom at one percent significance level was 3.32. Thus this concludes that farmers schooling do enhances the efficiency of farmer’s choice. They also conclude that the other variables beside these two variables Education and Continue, the other variables that was chosen might affect the adoption decision but no further analysis was done. The authors make the fact that the pother would be explored in future research.
A study on Technical Efficiency in Swaziland
A stochastic frontier production function method was adopted to estimate the efficiency of the small scale farmers between Vuvulane and Big bend farmers. It is report that this method is used to fit separate stochastic production between the two farms and it also argued (Binam et al. 2004) as long as interest relied on the efficiency measurement and not on the analysis of general structure, thus this method provides adequate representation of technology.
Find Out How UKEssays.com Can Help You!
Our academic experts are ready and waiting to assist with any writing project you may have. From simple essay plans, through to full dissertations, you can guarantee we have a service perfectly matched to your needs.
View our academic writing services
A stratified random sample of seventy five was used where it represented at about 80% of the total farmers of both Vuvulane and Big bend (94 farmers). Out of the 75 farmers, 35 of them were from the Big bend where representing 90% of the total farmers population and the remaining 40 representing the Vuvalane famers (73% of the total farmers). The data was collected in year 2006/2007 through a structured questionnaire where it was done face to face.
Based on Battese and coelli (1995), the inefficiency model was specified as:
µi = δ0 + δ1Z1i + δ2Z2i + δ3Z3i + δ4Z4i
Where the four explanatory variables according to this article for the i-th farmer are:
Z1= Age of the farmer (1 if <50 and 0 if >50 years)
Z2= Number of school years
Z3= off farm income (1 if yes, 0 otherwise)
Z4= total number of hectares held by the farmer
The technical efficiency in sugarcane production for both Vuvalane and Big bend show a decline as the age of the sugar cane growers increases where according to Dlamini et al., it is believed that they are experienced and also they dispose more resources like cattle and capital. This result at Big bend indicates that the more educated sugarcane growers are more likely to be efficient as compared to their less educated counterparts, perhaps as a result of their better access to information and good farm planning (Dhungana et al., 2004). Similar results were also reported by Kumbhakar et al. (1991). It was found that the farm size had a negative effect on the technical inefficiency effects in sugarcane production for both the Vuvulane and Big bend farmers showing that as the farm size increases, the technical inefficiency declines. The farm size variable is not significantly different from zero at 10% level using a t-test on both the Vuvulane and Big bend farmers. Thus educating the small scale sugarcane farmer could reduce the technical efficiency and also this improves techniques and proper use of available resources to develop their experience in sugarcane production.
Implements Adoption on Alluvial Soil Sugarcane
This study was designed by three researcher namely I.J TEKWA, G.M BUNU and M.MAKAMA to investigate the economic benefits achievable from adopting modern technologies by sugarcane farmers in the Mubi area as this area is characterized by patched fertile grounds conducive for profitable sugarcane production, where in fact only little attention has been devoted to adoption of modern technologies known for optimizing crop production in the past.
The survey was carried between September, 2006 and May, 2007 among the four sub-locations namely; Bahuli, Muchalla, Mijilu and Kirya, all within Mubi area, Nigeria. There were two methods of purposive data collection where firstly, a farm visit was conducted to view and sample field information through oral interviews from eighty (80) sugarcane farmers on the impacts of modern technologies on their farm incomes. Similarly, a questionnaire was designed and purposively administered to sugarcane farmers in the study area. Information of whole population was not reported in the study and also the sample method used was not clearly stated. According the authors, the data collected were validated using the test-re-test method of reliability test (Dixon-Ogbechi, 2002; Asika, 2008), with a strong correlation (r =0.80) between the multistage responses to the field questionnaires within same farmer population.
Only Explanatory Data Analysis was used to analyze the data collected. It is based from tables where Table 1(appendix) results on the inventory of farming technologies practiced by these farmers the four different areas. It indicated that more farmers used traditional implement than the modern implements. The traditional implements used among sugarcane farmers accounted the Indian hoes (24%), as the most widely spread, followed by axe (20%) and planting rods (19%). However, Ox-drawn ploughs exhibited low percentage (4%) concentration in the locations studied. Among the four different areas, Mijilu had the largest concentration (28%) of traditional farm tools in use prior to adoption process. It is followed by Kirya (26%), Bahuli (24%) and Muchalla (22%) with the least estimates. Somehow this leaves Mijilu location farmers as the laggards in the adoption process as there is few number of person who any modern technologies. Knapsack sprayer adoption rates recorded about twice the cumulative size of other modern technologies in all the locations studied.
Table 2. Shows farmers’ income accruable from the adoption of these modern technologies on their small farm sizes, ranging between 2 to 10 ha. The result showed, that in general, the numerical size of farmers that earlier used the traditional technologies declined sharply on realization of increasing income among the early adaptors of the newer technologies. Eventually before the adoption, it was reported that about 63% of the farmers earned below (N10, 000) from the sales of their sugarcane produce. After the adoption process, there is a net increase in the percentage of farmer s( 40%) generated between (N20, 000) and (N50,000) seasonally. While, larger percentage (43%) of the farmers earned between (N50, 000.) and (N70,000) during the period under study. However, it was observed that only 8% of the farmers realized between ( N70,000) and (N100,000) and where only 6% of them generated beyond (N100,000) from their sugarcane sales. It was stated that a chi-square analysis was done and recorded a significant (P<0.05) rise in farmers’ income as a result of the adoption of modern technologies on their farms but there was information about the degree of freedom used to calculate the chi-square test.
Table 2: Distribution of farmer income derived from
adoption of modern technologies
Farmer income Before adoption process After adoption process
(N) Frequency Percentage (%) Frequency Percentage (%)
< 10,000 50.0 62.50 00.0 00.0
20,000-50,000 20.0 2.50 30.0 40.0
50,000-70,000 08.0 10.0 35.0 43.0
70,000-100,000 02.0 2.50 10.0 08.0
>100,000 00.0 00.0 05.0 6.30
Total 80.0 100 80.0 100
(reference: Tekwa, I.J., G.M. Bunu and M. Makama, 2010. Impacts of modern farm machinery and implements adoption on sugarcane (Saccharrum officinarum) farmers’ income in Mubi, Northeastern Nigeria. J. Agric. Soc. Sci., 6: 101-104
Adoption of Agricultural Machinery in Union Council of Palosi
This study was conducted in January 2005, where it evaluates the adoption of modern agricultural machinery among small, medium and large farmers in Union Council Palosi, District Peshawar, where it consists of four villages namely Palosi Talrazai, Palosi Autozai, Palosi Maghderzai and Palosi Piran . Thus, the primary objective of the study was the identification of factors affecting the adoption level of agricultural machinery among the three groups of farmers. The estimated population of the union council is from 13-15 thousands. All the full time farmers of the area were included in the study. It can be observed that only 80 farmers were selected from the total population where normally factors like limitation of study, time factor and financial problems were taken into considerations. Therefore 20 respondents from each of the four villages that was selected in the survey.
Since the study was dealing also with three different categories of farmers namely small. Medium and large, so the author made three type of definition in order to classify the planters. Those possessing from 1-8acres were classified as small, from 9-16 were said to be medium and finally the one beyond 16 acres are viewed as large farmers. After collection of data, 35 respondents out of 80 were small farmers (43.75%), 27 (33.75%) medium farmers and the remaining (18) were larger farmers. Only Simple percentages were carried out in this study and t-test was applied for results and discussion in order to draw conclusion.
Only 9(11.25%) owned tractor, while 71(88.75) farmers did not possess any tractor. Among the owners of tractor, 22.22% were small, 33.33% were medium and remaining were large farmers. Dhawan (1980) observed that tractorization on farm improved the cropping pattern in Ludhiana District Punjab state. The farmers stated that during first ten years in farming, they did not use any agricultural machinery (mainly tractor and thresher), but after this period 25%, 30%, 31.25% and 13.75% had started their use as from 11-15 years, 16-20 years, 21-25 years and above 25 years, respectively. At first it was mainly the small farmers(60%) who started making use of the machinery compare to that of medium(30%) and only 10% of large farmers were using. Later on, as the years in farming increases there was an increase in the use of machinery by the large farmers up to 45.45%, whereas there was a declined in the usage of machinery by small farmers. According to Surendra Singh et al., (1992) the area per tractor was 10 ha for large farmers and more than 12 ha for small and medium farmers. The authors conclude that that the majority of the farmers had started making usage of tractor as from 11-25 years in farming and also those tractors owners must be educated about proper use, maintenance and operation of it. Compare to a similar researcher Hussain (1987) where he pointed out from this survey and stated that majority of the respondents were aware of the farm machinery/implements, but their adoption was not encouraging due to small land holdings, lack of interest, non-availability of implements, high costs and lack of technical knowledge.
Cite This Work
To export a reference to this article please select a referencing style below:
