Rice Production Essay Example

Rice Production Essay I. Introduction The most important food crop in the Philippines is rice, a staple food in most of the country. It is produced all throughout Luzon, the Western Visayas, Southern Mindanao, and Central Mindanao. 9. 5 billion tons of palay were produced in 1989 almost. In 1990 palay, which caused a 27 percent value added in agriculture and 3. 5 percent of GNP. Per hectare yields have generally been low in comparison with other Asian countries. Since the mid-1960s, however, yields have increased substantially as a result of the cultivation of high-yielding varieties developed in the mid-1960s at the International Rice Research Institute located in the Philippines. The proportion of miracle rice in total output rose from zero in 1965-66 to 81 percent in 1981-82. Average productivity increased to 2. 3 tons per hectare (2. 8 tons on irrigated farms) by 1983. By the late 1970s, the country had changed from a net importer to a net exporter of rice, albeit on a small scale. In the 1980s, however, rice production encountered problems. Average annual growth for 1980-85 declined to a mere 0. 9 percent, as contrasted with 4. 6 percent for the preceding fifteen years. Growth of value added in the rice industry also fell in the 1980s. Tropical storms and droughts, the general economic downturn of the 1980s, and the 1983-85 economic crisis all contributed to this decline. Crop loans dried up, prices of agricultural inputs increased, and palay prices declined. Fertilizer and plant nutrient consumption dropped 15 percent. We will write a custom essay sample on Rice Production specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on Rice Production specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on Rice Production specifically for you FOR ONLY $16.38 $13.9/page Hire Writer Farmers were squeezed by rising debts and declining income. Hectarage devoted to rice production, level during the latter half of the 1970s, fell an average of 2. 4 percent per annum during the first half of the 1980s, with the decline primarily in marginal, nonirrigated farms. As a result, in 1985, the last full year of the Marcos regime, the country imported 538,000 tons of rice. The situation improved somewhat in the late 1980s, and smaller amounts of rice were imported. However, in 1990 the country experienced a severe drought. Output fell by 1. 5 percent, forcing the importation of an estimated 400,000 tons of rice. In few years, we may have to squeeze out whatever is left of the country’s rice stock. This paper aims to find out what factors affect the production of rice in the Philippines to be able to formulate policies which may give the Filipinos more than enough hope and promise to help Filipinos on the way not only to rice sustainability, but also to national food security. II. Statement of the problem and object of the analysis General: This paper attempts to analyze Palay Production in the Philippines from the first semester of 1991 to the second semester of 2002 as affected by the size of land used for planting palay, amount of rain, and advancement of technology. Specific: More specification, this paper answers the following questions: 1. Does each of the following variables has significant effect on Palay Production. a. Area of land allotted for planting palay. b. Amount of rainfall. c. Advancement of technology. 2. Is there a significant effect on rice production given that the area of land allotted for planting palay, amount of rainfall, and advancement of technology are combined. III. Specification of the model This paper utilized a multiple linear regression model which can be described as follows: PROD = b0 + b1AREA + b2RAIN + b3TECH Where: b = parameters estimates PROD = volume of rice produced AREA = area of land allotted for planting palay RAIN = amount of rainfall in the areas TECH = advancement of technology in the agricultural sector Furthermore, to determine the individual level of significance of every ndependent variable, the t-statistic will be used. In order to estimate the values of coefficient, the Least Square Method was used, with a confidence level of 95 percent, while to probe into to over-all significance to these variables to the dependent one, the f-statistic was used. Finally, the results are to be validated through the usage of R2 which determines the degree of influen ce of all variables to the dependent component. The level of significance used in this study is 5 percent, while the degrees of freedom is 21, derived by deducting the number of observations (24) by the number of independent variables in consideration (3). IV. Hypothesis To Be Tested That the variables such as area allotted for planting palay, amount of rainfall, advancement of technology in the agricultural sector have no significant effect on rice production in the Philippines. V. Presentation of Data The Table below shows the data from the first semester of 1991 to the second semester of 2002, showing figures about palay production, area harvested, amount of rainfall and the advancement of technology. Palay ProductionArea HarvestedRainfallTechnology (All Ecosystem, In Metric Tons)(All Ecosystem, In Hectares)(in millimeters)Trend variable PRODAREARAINTECH 1991. 1 4,047,513 1,418,640 6,674. 0 1 1991. 2 5,625,749 2,006,320 13,124. 0 2 1992. 1 3,505,984 1,282,330 6,623. 7 3 1992. 2 5,622,956 1,915,740 16,278. 0 4 1993. 1 3,890,149 1,320,700 6,319. 8 5 1993. 2 5,544,059 1,961,650 18,756. 5 6 1994. 1 4,378,533 1,483,330 10,362. 5 7 1994. 2 6,159,521 2,168,200 13,995. 8 1995. 1 4,317,331 1,501,408 7,510. 3 9 1995. 2 6,223,318 2,257,283 21,217. 0 10 1996. 1 4,950,910 1,666,483 10,704. 3 11 1996. 2 6,332,658 2,284,653 16,258. 0 12 1997. 1 4,846,461 1,624,241 7,303. 0 13 1997. 2 6,422,502 2,218,029 11,052. 9 14 1998. 1 3,558,976 1,283,197 4,974. 4 15 1998. 2 4,995,848 1,886,845 24,330. 5 16 1999. 5,272,053 1,743,026 19,011. 6 17 1999. 2 6,514,572 2,256,813 26,291. 3 18 2000. 1 5,442,496 1,737,623 17,458. 1 19 2000. 2 6,946,916 2,300,462 27,202. 0 20 2001. 1 5,567,831 1,729,096 13,767. 5 21 2001. 2 7,387,039 2,336,345 23,721. 0 22 2002. 1 5,672,369 1,753,200 10,637. 3 23 2002. 2 7,598,284 2,293,118 20,401. 0 24 Prod = f(area, r ain, tech) VI. Summary of Findings After processing the gathered data into information, through the regression analysis, AREA, RAIN, TECH, and PROD, gave off the following estimated regression equation and other regression results: PROD = -282911 + 2. 959577AREA – 14. 77746RAIN + 37848. 18TECH The equation states that at every 1hectar increase in Area, there would be a 2. 959577 metric ton increase in rice production. Ceteris paribus. It also shows a negative relation between rainfall and rice production, were an increase of 1mm. Of rain fall would cause a decrease of 14. 7746hectars of rice, ceteris paribus. Finally, the equation also indicates a positive relation between technological advancements to rice production. As technology has a 1 unit increase, rice production would increase by 37848. 18. Dependent Variable: PROD Method: Least Squares Date: 07/12/04 Time: 23:04 Sample: 1991:1 2002:2 Included observations: 24 VariableCoefficientStd. Errort-StatisticProb. C-282911. 0334520. 1-0. 845 7220. 4077 AREA2. 9595770. 24752211. 956820. 0000 RAIN-14. 7774613. 73005-1. 0762860. 2946 TECH37848. 188842. 3054. 2803520. 0004 R-squared0. 954369 Mean dependent var5451001. Adjusted R-squared0. 947524 S. D. dependent var1137085. S. E. of regression260478. 8 Akaike info criterion27. 92944 Sum squared resid1. 36E+12 Schwarz criterion28. 12578 Log likelihood-331. 1533 F-statistic139. 4325 Durbin-Watson stat1. 048045 Prob(F-statistic)0. 000000 The regressed data show that only the amount of rainfall has no significant effect on the dependent variable, as shown by the variables’ t-statistic. It did not meet the critical value of 2. 080 with a t-statistic of 1. 76286. All in all, the whole estimated equation is highly significant as shown by the f-statistic of 0. 0000. There is a 0. 954369 R-squared data, which shows that there is a 95. 44% influence of independent variables to the dependent variable. This graph shows the trend of Rice Production in the Philippines and amount of Rainfall during 1991 to 2002. As one can observe, rice production and rainfall are almost going at the same trend, but when there was a massive amount of rainfall during 1998, rice production slowed it’s ascend to it’s peak. This graph shows the trend of rice production and area for planting palay from the first semester of 1991 to the second semester of 2002. as we can observe, the graphs are almost overlapping each other, except during 1998, where rice production slowed during the beginning of the second semester. VII. Conclusion With the country’s fast increase in population and the slow pace of our technology and limited land area, the rice our farmers are producing are not sufficient to feed all of our â€Å"kababayans† and have more to sell throughout the world. Policy makers should try to formulate policies that would help increase rice production. The input requirements of the new technologies were skewed, in the direction of capital inputs, mainly irrigated land, fertilizers and other forms of capital. By definition, capital is scarce, and therefore the implementation of the new technologies stretched over a long period of time. This is on the supply side, whereas on the demand side, the countries have to expand their export in order to supplement the growing domestic demand in absorbing the growing supply. The message for the future is clear, for the growth to continue, the available technologies must continue to grow. Without such growth, the impact of input growth will eventually decline; we see some evidence to this effect already in the estimated regression equation. But this is not the only determinant of future growth. In order to take a full advantage of new techniques, there must be a smooth flow of the required resources into agriculture. VIII. Bibliography Economic Development 8th edition, by Michael s. Todaro and Stephen C. Smith, page 418-454 Agricultural Statistics c/o San Beda College Prof. Harold Glenn Valera