This is a very important indicator in production. This is the difference between the revenue and the expenses. This indicator measures the efficiency directly, so the bigger the better.
Profit = Revenue – Total Costs
Profit
Profit margin =
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Total cost
How much profit will be earned from one dollar invested in production and business?
d) Income
Is an important indicator in household production. It reflects the income of each crop and each year to evaluate the living standards of farmers and household income.
Income = Profit + House Labor Cost
Income
Income ratio =
.
Return on capital =
Total cost Total cost Profit
Shows how much money is spent on each dollar of profit.
e) Production results
Single indicator, reflecting the results of a production process. Such as: production costs, total revenue, profit...
f) Production efficiency
Dual indicators, comparing two or more production performance indicators with each other, for example: profit margin, income margin, capital efficiency...
Result
Economic efficiency
=
Total cost of production
3.1.5. Concept of production function.
The production function is a function that shows the relationship between output and input. It describes the correlation coefficient of the factors of production transferred to the product. The production function is generally written as follows:
Y = f(X1, X2, X3,…, Xn-1, Xn)
In which: Y: Output
X1,…,Xn: Variable inputs
3.1.6. Factors affecting rice and vegetable productivity.
a) Regression model
X
The yield of rice depends a lot on input factors such as seeds, labor, fertilizer, experience, education level, cultivated land area, weather, etc. These input factors increase, causing the yield to increase and at a certain level it stops increasing and tends to decrease. Therefore, the Cobb-Douglas function is used to describe this phenomenon in the study.
5
X
1
Cobb-douglas function:
Y 0
e 8 DUMKN e 9 DUMTD X 5
4 3
X
4 3
In there:
X 2 2 X 1
6 7
X
6 7
Y: Rice yield (kg/1000m2) KN: Agricultural extension
TD: Credit
X1: Labor (work/1000m2) X 2: Fertilizer (kg/1000m2) X3: Experience (years)
X4: Number of years of schooling (years)
X5: Plant protection drugs (1000 VND) X6: Planting area (1000m2) X7: Seed quantity (kg)
0: Y-coordinate.
1…9 : Are the elasticity coefficients corresponding to the corresponding variables.
Because the production conditions of rice and vegetable crops are almost similar in terms of input factors, the general regression model is used for both.
model. There are many variables affecting rice and vegetable yields, but here we only mention the variables that affect yields the most as above.
This method is performed using Eview 3.0 software.
b) Basis for selecting variables and sign expectations
Variables showing the status of participation in agricultural extension:
When participating in agricultural extension, farmers will acquire new scientific and technical advances to apply in production, so productivity will be higher than when not participating. Therefore, the variable of not participating in agricultural extension is chosen as the base variable for comparison and the sign of the variable participating is expected to be positive (+). Then the dummy variable is set as follows:
DUMKN: DUMKN = 1: Participate in agricultural extension; DUMKN = 0: Do not participate.
Credit expression variable
Credit is also considered a factor affecting crop productivity.
planting, it seems that the borrowers are those with large production scale so they focus on production to bring the highest efficiency to solve the capital problem after harvest. So we can expect a (+) sign if borrowing credit. Then the dummy variable DUMTD=1: borrowing and vice versa DUMTD = 0.
Pesticide expression variables:
This variable includes herbicides, pesticides and growth regulators, when farmers use more, it will bring higher crop yields so it is expected to have a (+) sign. When running the model, this variable is run as a log-log function because it does not increase in the direction of a straight line.
Labor expression variable
Labor and fertilizer are related to each other. The more fertilizer, the higher the labor, so it is easy for multicollinearity to occur when running the model, so in the regression model, fertilizer application costs are removed from labor costs, only care costs are used to run the model. Labor costs 8 hours a day is one work. Labor costs include housework and hired labor. Normally, the more labor costs for care, the higher the productivity will be, so we expect a sign for this variable (+). Labor variables are run in log-log form.
Fertilizer expression variable
Here, people mainly use URE, NPK, DAP, POTASSIUM and KALI fertilizers and chemical fertilizers. Normally, the more fertilizer is applied, the better the crop will be, but if the fertilizer is applied excessively, the plant will die. However, in general, the more fertilizer is applied to the crop, the higher the yield will be, so we expect a (+) sign for this coefficient. The fertilizer variable is modeled in log-log form.
Experience variables
Experience also has a great impact on productivity. Farmers who grow rice for longer will accumulate experience in growing rice and vegetables and achieve higher efficiency, so we expect a sign for this variable (+). This variable is modeled in log-log form.
Variables showing cultural level
Educational level is expressed by the number of years of schooling. For people with less education, they are less receptive to scientific and technological advances and often produce according to traditional experience. On the contrary, households with higher education levels are more receptive to science and technology and thus productivity will be higher, so the expected sign is (+).
Area display variable
The larger the area, the more concentrated the farming household is in production, so the average yield per 1000m2 will be higher. A positive sign is expected for this variable and this variable is also expressed as a log-log function.
Variation in seed quantity
This is a variable that represents suitability. The more seeds are planted, the higher the yield. However, at a suitable level, because if the seeds are planted too densely, the plants will not have enough nutrients to grow and develop. Therefore, a positive sign is expected for this variable.
3.1.7. Model testing
After using Eviews 3.0 to estimate the factors affecting the productivity of the two models, it is necessary to analyze the following issues: Comment on the sign and magnitude of each regression coefficient of the models. Comment on the R-square coefficient. At the same time, perform the following tests:
a) Test the significance of regression coefficients
T-test: Set hypothesis:
H0: βi = 0.
H1: βi ≠ 0 (i = 1,n).
Calculate the test value:
S
i
n
i
T i bi
In which: Ti is the T-statistic value of the ith independent variable, βi is the ith estimated parameter, n: number of observed samples, Sβi: standard deviation of the parameter.
Decision: The value of Ti will be calculated based on the regression results using Eview software on the computer. The value of Ttra is based on the STUDENT distribution table.
Reject H0 with significance level α when /test/ > thypothesis
With Thypothesis = Tn-k-1,α/2 , where: n is the total number of observations, k: number of independent variables
F-Fisher test (testing the overall significance of the model): Hypothesis:
H0 : β1 = β2 = …= βn = 0 (the variation of the dependent variable is not explained by the independent variables)
H1 : ∂!βj ≠ 0 (the variation of the dependent variable is explained by the independent variables)
Test value:
Ftest = (SRR/k) /(SEE/ nk) With:
SRR: sum of squares regression SEE: sum of squared errors
k: number of independent variables, n: total number of observations
Decision: Table lookup value: Ftra table = Fk-1, nk. Use the Fisher distribution table to find the result. Reject H0 with significance level α if: Ftest > Fk,nk-1,α
b) Testing for hypothesis violations of the OSL method
Heteroscedasticity
The phenomenon of heteroscedasticity or heteroscedasticity is the phenomenon in which the variance of the overall regression line corresponding to the values of the independent variable is different (ie the variance is not a constant).
When the phenomenon of uneven variance occurs, it will cause bad results such as: the estimated coefficients are linear, unbiased and consistent, the estimated coefficients are no longer the best (not Best), meaning they do not have the smallest variance. This causes a lack of reliability of the relationship between the dependent variable and the independent variable in the model, making the hypothesis tests, significance level tests and confidence intervals according to the T and F distributions no longer reliable.
Using the White test to test for the phenomenon of unequal variance, perform the following steps: Collect the error terms of the original regression model. Build an artificial regression model with input variables as independent variables and dependent variable as the error term ε2 of the original model.
R
2
AUX
is the coefficient of determination of the artificial regression model.
AUX
Calculate the White-statistic: Wstat = n* R2 , n is the number of observations.
Identify
2
,df
, with significance level α through the chi-square distribution table,
df = k : number of independent variables in the artificial regression model.
Hypothesis: H0: there is no phenomenon of unequal variance.
H1: there is a phenomenon of heteroskedasticity.
Decision: . Wstat
2
>
,df
reject the null hypothesis H0
and vice versa.
Multicollinearity
Multicollinearity phenomenon: Is a phenomenon where at least one independent variable is a linear combination of other variables.
λ1X1 + λ2X2+λ3X3+…+λkXk = 0
In which λi, with i =1,2,3…,k are constants, and there is at least one λi different from 0
Consequences: Variance and standard error will be large. Confidence interval will be wider. T and F tests will not be significant. Sign of regression coefficient is wrong.
To detect multicollinearity, additional regression models are established in turn. If the coefficient of determination R2 of the additional regression model is greater than the coefficient of determination R2 of the original regression model, multicollinearity occurs and vice versa.
Autocorrelation phenomenon
Autocorrelation phenomenon: Is the phenomenon that the value of the error in this period is not independent of the value of the error in the next period. Consequence: The estimates
Least squares is still an unbiased estimator but it is no longer an efficient estimator. The estimated variance of least squares estimators is often biased, so that the T and F tests are no longer reliable. To detect this phenomenon, use the Durbin-Watson test.
Hypothesize:
H0 : = 0 or there is no autocorrelation phenomenon H1 : ≠ 0 there is autocorrelation phenomenon
With = (2 – d)/2
Statistical value: Durbin – Watson:
n
t t 1
n
d t 2
t
t 1
In which: εt: error of period t, εt-1: lag error (period t-1), n: number of observations.
Table 3.1 Autocorrelation test table
H1: > 0 = 0 H1: = 0
Reject H0 No conclusion can be made.
Accept H0 Cannot conclude
discussion
Reject H0
0
Master Tran Anh Kiet (2006)
3.2. Research method
3.2.1. Descriptive method.
Using information from the internet, newspapers, radio, and related data, describe the current situation of agricultural production in general and rice and vegetable production in particular in Tan Nhut commune, Binh Chanh district, Ho Chi Minh City.
3.2.2. Data collection method
From the purposeful random sampling, two groups specializing in vegetable and rice growing in the locality were selected. Then, a random household survey was conducted to collect the necessary information and data for the topic. Specifically, 35 rice growing households and 35 vegetable growing households with a pre-prepared questionnaire.
3.2.3. Data processing procedures and techniques
From the initial primary data we will convert the data into quantitative form.
qualitative variables, data filtering, and removing unreliable data. Then, use Excel and Word programs to synthesize and draw conclusions from the available data. Use regression analysis methods running on Eview 3.0 to confirm the influence and level of influence of factors on the productivity of two crops, rice and vegetables, in the entire Tan Nhut commune, Binh Chanh district, Ho Chi Minh City in 2008.
3.2.4 Sensitivity analysis method
This is an analysis of the rate of change of two indicators of revenue and profit from two models of rice and vegetable when related factors change. In order to make the right decision for the future about the level of safety in agricultural production, helping farmers feel secure in choosing the production model as well as the level of investment for these factors.
For example, when the price changes by 10%, how much does the profit change, corresponding to the two research models, or if the price of the input factor changes by 20%, how much does the corresponding profit change? And when both of these factors fluctuate, how will the revenue and profit fluctuate?
From there, appropriate and practical recommendations are made for these two crop models in the future in the locality.