Efficiency Analysis of Developing Country Agriculture a Review of the Frontier Function Literature

one. Introduction

Aquaculture is a central economic sector across Vietnam and especially in the Mekong River Delta. The shrimp industry has played an important office in Vietnam's seafood exports during the past two decades, with the two main species beingness blackness tiger shrimp and white leg shrimp. The shrimp industry annually contributes about 40–45% of the total seafood export value, equivalent to 3.5–four billion USD [one]. The full shrimp farming area is over 750,000 ha with an output of nigh 900,000 t of which the farming area in the Mekong Delta is over 600,000 ha with an output of over 750,000 t [two]. In recent years, white leg shrimp has gradually get the main farmed species in the coastal provinces of the Mekong Delta. In the menstruum 2015–2019, the production of white leg shrimp increased continuously and strongly by well-nigh 41%, with an average increase of 9% per year; while the output of black tiger shrimp increased by an average of 1.2% and after 5 years simply increased by 3.1% (the yield of black tiger shrimp increased insignificantly compared to that of white leg shrimp). In 2019, the export turnover of white leg shrimp reached nearly $3.v billion, accounting for 70% of the total shrimp export value, while black tiger shrimp accounted for only xx% of the total export value [ane].

The conversion to white leg shrimp farming is becoming a popular pick amidst farmers because white-leg shrimp tin exist intensively farmed and accept a reputation for being more disease-resistant and more adaptable [iii,four,v]. In addition, environmental atmospheric condition in mangrove areas are increasingly unsuitable for black tiger shrimp farming, such equally inappropriate pond construction, low-quality soil, low-quality h2o, high stress levels that effect in a higher chance of affliction [three]. Therefore, the transition from black tiger shrimp farming to white leg shrimp farming seems inevitable. In doing this, farmers have found that shrimp productivity has increased dramatically without converting boosted land or constructing new ponds, and that income has significantly increased [3]. The total price, acquirement, profit, and turn a profit margin for white leg shrimp are all college than for blackness tiger shrimp only product costs are lower, indicating that the white leg shrimp farming model is more than financially efficient than its counterpart [vi]. Most shrimp farms in the Mekong Delta coastal areas are small-scale; many subcontract-households lack capital letter for production activities, while access to loans is too express. They prefer white leg shrimp farming with depression input costs due to the lower financial risk [3,7].

The shift from rice monoculture to other agricultural products such every bit fruits and aquaculture has been popular in the Mekong Delta. The conversion from rice to shrimp farming has proven effective, offering farmers to overcome poverty and take a significantly college income, especially for farmers living in saline-affected land could typically cultivate only one low-yield rice ingather a year [viii]. Some agricultural areas in the Mekong River Delta will experience permanent salinity intrusion, and farmers would probable diversify their crops past growing shrimp cultures in the dry season [9]. Therefore, farmers have gradually switched from rice, sugarcane and other crops to super-intensive shrimp farming, especially white leg shrimp. Many sugarcane farmers in the Mekong Delta have switched to shrimp farming solely by growing white leg shrimp due to the unstable market and depression productivity of sugarcane. However, the conversion requires loftier investment and skillful preparedness for product techniques, which results in high risks [10].

There are few contempo studies on the input utilization of shrimp farming that gauge the technical efficiency of farmers' white leg shrimp culture in the Mekong Delta [xi]. It is especially true for farmers who recently converted from black tiger shrimp and other crops to white leg shrimp since this is a new conversion in the Mekong Delta provinces. Therefore, information on the efficiency in terms of technical aspects of white leg shrimp farming, particularly for subcontract-households that recently converted from other crops to white leg shrimp farming, is minimal. It leads to failure to make advisable policies to develop this converted shrimp farming sustainably.

Minh [6] evaluated the efficiency of the white leg shrimp production in Soc Trang by directly interviewing 30 farm-households producing blackness tiger shrimp and 30 households converting from blackness shrimp to white leg shrimp farming, then comparing the efficiency of the two models. The research results show that white leg shrimp farming is more efficient than its counterpart in terms of the financial attribute such every bit total revenue, toll, turn a profit, turn a profit margin. Even so, the inquiry has not evaluated the technical efficiency of white leg shrimp farming, peculiarly for farms that switched from black tiger shrimp and other crops to white leg shrimp. Long et al. [12] estimated the technical and financial efficiency of white leg farming in Ca Mau province by straight interviewing 34 white shrimp farming households in Ca Mau. However, the study merely made some descriptive statistics on the indicators in the technical attribute of the model; the study did non estimate the level of technical efficiency of the farms. In addition, given the small sample size, the written report results may non be representative of the unabridged Mekong River Delta region.

Trang et al. [10] evaluated the technical and ecology efficiency of the conversion from sugarcane to white leg shrimp farming by collecting information from 90 households in Cu Lao Dung district, Soc Trang province. The research results evidence that the switch to white leg shrimp farming in this expanse has not achieved loftier technical efficiency. However, this is only a study to evaluate the technical efficiency of a particular converted model to white leg shrimp civilization.

From the earlier limitations, it is indicated that the technical efficiency of white leg shrimp farming, especially for farmers converting from black tiger shrimp and other crops to white leg shrimp at Mekong Delta provinces, has non been comprehensively studied. Information on the technical efficiency and the adaptability of this converted shrimp farming is minimal. Therefore, making appropriate policies to back up this conversion will not be articulate. By using the latest survey information on a larger calibration, especially in the areas that converted from black tiger shrimp and other crops to white leg shrimp farming, this report estimates the technical efficiency in white leg shrimp farming of farmers in the Mekong Delta provinces to accurately assess the efficiency related to the technical aspects of this cultivation. In improver, this written report compares the technical efficiency betwixt the traditional grouping and the converted group to evaluate the efficiency and adaptability of the converted group when switching to white-leg farming in recent years. Based on that, some policy implications are proposed for the sustainable development of this converted model.

two. Methodology

ii.1. Efficiency

Economic efficiency means to increase output without using more conventional inputs. The use of existing technologies is more than price-constructive than applying new technologies if farmers cultivate their products with the existing technology inefficiently [13,fourteen]. Economical efficiency can be classified into two parts: technical efficiency and allocative efficiency. Technical efficiency measures the power of a farmer to achieve the maximum output with given and obtainable engineering science, while allocative efficiency tries to capture farmer's ability to apply the inputs in optimal proportions with respective prices [fifteen,16,17].

2.ii. Technical Efficiency (TE)

Measuring technical efficiency is to use inputs and output quantity without introducing their prices. Technical efficiency tin can exist decomposed into 3 components such as scale efficiency (the potential productivity gain from achieving optimal size of a firm), congestion (increase in some inputs could subtract output) and pure technical efficiency [15]. A production plan is technically inefficient if a college level of output is technically accessible for the given inputs (output-oriented measure), or that the observed output level can exist produced using fewer inputs (input-oriented measure out) [17].

two.2.i. Input-Orientated Technical Efficiency

It is assumed that a firm uses ii inputs (X ₁ and X _ii) to produce a unmarried output (Q) under the assumption of constant returns to scale (Figure 1). The SS' isoquant curve that represents full efficiency firms could permit measurement of technical efficiency. At point A, if the business firm uses inputs to produce a unit of measurement of output, its technical inefficiency could be represented past the altitude AB, which is the amount by which all inputs demand could be proportionally reduced without a reject in output. It is usually expressed in percentage terms by the ratio BA/OA, representing the percentage by which all inputs need to be reduced to achieve technically efficient production. The ratio virtually normally used to measure the technical efficiency (TE) of a business firm is given:

Its value ranges between 0 and 1 and represents the level of technical efficiency. Suppose TE is equal to one, indicating that the house produces fully technical efficiency. The firm could be fully technically efficient at signal B because signal B lies in the efficient isoquant curve.

With the input price ratio, represented by the slope of the II' isocost line, allocative efficiency (AE) at A can exist calculated and identified by the ratio:

The production costs will decrease by the altitude from B to C if a firm performs at the allocatively and technically efficient point E instead of at the technically efficient but allocatively inefficient signal B. The total economic efficiency (EE) is divers to be the ratio:

The altitude from A to C too represents the cost cut in production if a business firm produces at point C with the technical efficiency and allocative efficiency instead of at indicate A with technical inefficiency and allocative inefficiency. Economic efficiency is to combine technical efficiency and allocative efficiency.

ii.2.ii. Output-Orientated Technical Efficiency

The input-orientated technical efficiency indicates how much input needs to be proportionally cut without changing the output. While the output-orientated technical efficiency answers the question of how much the output tin be proportionally increased without irresolute the amount of input [16,17]. Suppose that a firm uses one input (X) to produce two outputs (Q₁ and Q₂) under the assumption of abiding returns to calibration (Figure 2). The PP' production possibility curve represents the upper spring of the production possibilities, at point D where the firm is technically inefficient considering information technology is below the PP' curve. The altitude from D to E represents technical inefficiency, which is the corporeality of the output that could exist extended without increasing the inputs. Hence, the ratio measuring the output-oriented technical efficiency is given:

The revenue efficiency (RE) tin be divers for any observed output price represented by the RR' line. Suppose the practical output of the given firm is represented at point D. In that case, point Eastward represents the technically efficient production and the revenue efficiency associated with point East', and then the revenue efficiency is measured as follows:

If we take price information, so we can depict the isorevenue line DD'; the allocative efficiency is measured by:

2.3. Analytical Framework

The method widely used in the literature to gauge technical efficiency is an econometric approach [18]. It aims to develop stochastic frontier models based on the deterministic parameter borderland of Aigner et al. [xix]. Since shrimp production is an case of single output and multiple-input production, this report focuses on the use of an econometric approach for measuring technical efficiency based on the production frontier model in Cobb–Douglas function form, which was commonly applied to estimate the stochastic product frontier in agricultural production [10,16,20,21,22,23,24,25,26]. The stochastic production function of the i-th farmer in Cobb–Douglas form is given equally follows:

$$\ln Yi={\beta }_0+{\displaystyle \sum }_{\mathrm{one}}^j{\beta }_{ij}\ln X_{ij}+{\epsilon }_i\mathrm{i}=\mathrm{ane},\mathrm{ii},\dots \mathrm{North}$$

(seven)

where all farms are indexed with a subscript i; j is numbers of explanatory variables; β_ij are parameters to be estimated; ε_i is a composed error term with ε_i = v_i − u_i , where v_i is symmetric, independently and identically distributed as (v_i ∼N[0,

${\sigma }_5^{\mathrm{ii}}$

]), represents the exogenous furnishings such every bit impacts of adverse weather, natural disasters, measurement errors and other statistical noises; and u_i is one-half-normal and nonnegative random error (u_i ≥ 0), distributed as u_i ∼N ⁺ (0,

${\sigma }_u^2$

), represents the technical inefficiency effect of the i-th farmer. The used inputs including feed (kg/ha), medicine (thousand VND/ha/crop), fuel (thousand VND/ha/ingather), seed (larvae/ha/ingather), labors for farming activities (human being-twenty-four hour period/ha/ingather) are commonly used in recent studies to gauge the technical efficiency of white leg shrimp farming [6,x,27].

The output-oriented TE of the i-th farmer is obtained by multiplying

${\mathrm{eastward}}^{-v_i}$

on both sites of Equation (1) and replacing the estimated parameters β with maximum likelihood estimates (MLE). This manipulation yields the measure of output-oriented TE as follows:

$${\mathrm{OTE}}_i=e^{-u_i}=\frac{y_i}{f\left({\mathrm{ten}}_{ij},{\beta }_j^{*}\right)e^{v_i}}$$

(viii)

According to Jondrow et al. [28], u_i is predicted by the conditional expectation of u_i , given the value of random composed error variable ε_i . The expression of u_i is given past

$$\mathrm{E}\left(u_i|{\epsilon }_i\right)={\sigma }^{*}\left[\frac{\varnothing \left({\epsilon }_i\lambda /\sigma \right)}{\left(1-\mathsf{\Phi }\left({\epsilon }_i\lambda /\sigma \right)\right)}-\left(\frac{{\epsilon }_i\lambda }{\sigma }\right)\right]$$

(9)

where σ* = (

${\sigma }_u^{\mathrm{two}}{\sigma }_5^2$

/σⁱⁱ)^ane/2; and ϕ(.) and Φ(.) stand for the standard normal density and cumulative distribution functions.

Output loss (OL) is the yield loss caused by technical inefficiency in product or the attainable output gain if farmers achieve optimal technical efficiency. The higher the technical inefficiency is, the greater the loss in output. Output loss is calculated based on TE and real output (RO)—the actual productivity of farm-households in farming. Estimating the real loss provides farmers an evidently electric current condition of how efficient their production is. The output loss is calculated as follows:

OL_i = (1 − TE_i) × RO_i

(ten)

3. Information

Figure 3 shows the allocation of the Mekong Delta includes 13 provinces of Long An, Tien Giang, Ben Tre, Vinh Long, Tra Vinh, Can Tho, Soc Trang, Bac Lieu, Ca Mau, Kien Giang, An Giang, Hau Giang, and Dong Thap. This region has a total natural area of 39,734 km², bookkeeping for 12.2% of the country's natural size. Agriculture is the region's primary economic activity, including fisheries, livestock, and rice farming. Aquaculture activities, mainly shrimp farming, are full-bodied mainly in littoral provinces based on suitable environmental conditions. Soc Trang, Bac Lieu, Ca Mau, and Kien Giang are four provinces with long coastlines and proper ecology atmospheric condition for shrimp farming. Therefore, these are the four provinces with significantly big shrimp farming areas in the region.

According to the statistics from the Ministry of Agriculture and Rural Development in 2019 [29] presented in Table one, the shrimp farming expanse in the Mekong Delta is nearly 700 one thousand ha with two principal species, black tiger shrimp and white leg shrimp. Ca Mau, Bac Lieu, Kien Giang, and Soc Trang are four provinces in the region with large shrimp farming areas. In recent years, the pond expanse has been gradually converted to white leg shrimp farming. Specifically, the area for white leg shrimp farming in Ca Mau was 84 ha in 2010; this number increased to more than lxxx thou ha in 2018. This shift also happened in the other three provinces, especially in Soc Trang: the white leg shrimp farming surface area surpassed the black tiger shrimp farming expanse in 2018 with an surface area of 32,678 ha, while the expanse for black tiger shrimp farming reached 23,482 ha.

From the above data, it tin be seen that the shift to white leg shrimp farming in the Mekong River Delta is taking place quite strongly in the above four provinces. Therefore, it is entirely reasonable to choose the above 4 provinces as the report sites to evaluate the technical efficiency of this model in these areas in particular and to generalize to the whole Mekong River Delta region in full general.

Chief data were collected by direct interviewing farmers in cultured white leg shrimp farming areas and areas recently converted from black tiger shrimp and other crops to white leg shrimp farming in the to a higher place 4 provinces. The questionnaire was designed to collect data related to the socioeconomic characteristics of farmers, data on inputs and productivity of white leg shrimp farming, and the advantages and disadvantages in production. The total number of observations was 200 farm-households (99 farmers in cultured areas and 101 farmers in converted areas) in 4 provinces of Soc Trang, Bac Lieu, Ca Mau, and Kien Giang. The number of observations collected in each region was stratified based on the farming area in each province. Therefore, the largest number of observations was collected in Soc Trang. Specifically, the samples in the four sections of Soc Trang, Bac Lieu, Ca Mau, and Kien Giang numbered 113, 22, 37, and 28, respectively.

4. Results and Give-and-take

This study applied stochastic frontier analysis to judge the technical efficiency of white leg shrimp farms in the study site. In addition, nosotros compare the technical efficiency between the traditional group and the converted grouping to consider how successfully converted farmers adapt to the new model. The inputs and output of white leg shrimp civilisation were described in Table two. White leg shrimp culture has three environmentally detrimental inputs, including food (X ₁), medicine (Ten _two), and fuel (10 ₃), equally well equally two normal inputs such as seed (10 ₄) and labor (Ten ₅).

This study only focuses on shrimp yield per hectare in the year. Other outputs in farming such as fish and crab can exist termed as a event of ecological atmospheric condition, which does not reflect the product engineering of the household will non be considered in this study because they are very few [ten]. Table 2 shows that the output and input of shrimp farming models in the written report area have a considerable variation. The reason is due to the influence of weather and the difference in management level and seed source of the farms [10]; the level of success and failure of the farms likewise varies.

To test for the presence of technical inefficiency in the report, we use the log-likelihood pregnant parameter in the half-normal model

$\lambda ={\sigma }_u/{\sigma }_v$

. If λ = 0, in that location were no effects of technical inefficiency, and all deviations from the frontier were due to dissonance [26]. According to Table 3, the estimated value of

$\tilde{\lambda }$

= 0.8058 significantly differed from nothing. The null hypothesis that in that location is an absenteeism of inefficiency upshot was rejected at the one percent level using the Z-statistic, suggesting the being of inefficiency effects for white leg shrimp farming in the Mekong River Delta.

Based on the borderland production model'due south estimation and using Equations (8), nosotros can calculate the output-oriented technical efficiency of white leg shrimp farms. The distributions of the efficiency were presented in Tabular array 4. Table 4 shows that the average output-oriented technical efficiency of white leg shrimp farms in the study area was 72.9%, suggesting the farms could increase 27.1% of white leg shrimp yield with current inputs used. In addition, with an average of 72.ix%, information technology indicates that the average household in the sample could salve 16.5% (i.eastward., one − [72.9/87.3]) of costs, and the most technically inefficient could realize a 49.five% cost saving (i.e., 1 − [44.one/87.3]) compared with the TE level of his about efficient counterpart. Technical efficiency scores besides largely varied amid households. The maximum efficiency level was 87.iii%, while the minimum was merely 44.1%. These results indicate that many shrimp households face up high risks in production, so the output-oriented technical efficiency is low. Well-nigh of the shrimp farms accomplish technical efficiency in the range from 70% to 90%, bookkeeping for nearly 71% of total sample observations. Households with efficiency in the field from lx% to seventy% occupied approximately 23.5% of the total sample, and households with efficiency levels less than 60% occupied only 5.v% of the total sample.

The T-test was used to compare the difference in technical efficiency between the cultured area (the farms that have been producing white leg shrimp and have not changed to other agricultural farming) and the converted area (the surface area converted from blackness tiger shrimp or other crops). The issue is presented in Table 5, the p-value of the T-exam is 0.1979, i.due east., there is no difference in technical efficiency between the two farming areas in the study sites. The average technical efficiency of farmers in cultured farming areas is more often than not equal to the converted group. This is quite consequent with the reality in the written report area. Since in that location are many farmers converting to white leg shrimp farming that used to produce black tiger shrimp, they already have experience in farming techniques and the conversion to white leg shrimp farming is relatively favorable for them. In add-on, white leg shrimp farming is relatively uncomplicated, and farmers switching from other crops to white leg shrimp can too chop-chop grasp shrimp farming techniques by learning from other households in the area. Therefore, nearly of the converted farmers achieve most the same efficiency equally the traditional ones.

To determine the factors affecting the technical efficiency of white-leg shrimp farming in the study sites, the OLS regression model was used with TE as the dependent variable and some key socioeconomic and technical independent variables. The results are presented in Tabular array six. Farming density, pond size, and labor are three variables affecting the technical efficiency, in which the swimming size variable has a nonlinear relationship with TE. Increasing the pond size positively affects TE; however, it reduces TE when the swimming size is too big. This result is quite like to the recent report of [30], which shows that the density and size of the pond are critical factors affecting the TE of shrimp farming.

Farmers' experience does not affect TE, which is also relatively suitable with the actual state of affairs in the written report sites, this result is the same with the written report of Le et al. [31]. Since the farmers mostly choose elementary farming methods and have not yet applied advanced techniques to production activities, they tin learn from each other rapidly most these simple farming methods.

Whether or not subcontract-households have purifying ponds is not related to TE. Since near of the farming ponds in the areas are shut to big rivers, farmers without settling purifying ponds directly discharge water from the farming ponds into the rivers, contributing to water pollution of the rivers. Then, the water from these rivers is pumped directly into the ponds for the post-obit crops. Due to using the aforementioned h2o sources for farming, the farmers with settling purifying ponds are also affected by the belch of polluted water into rivers by households without purifying ponds. Therefore, purifying ponds do non make a difference in the technical efficiency in this surface area. However, in the long run, the uncontrolled wastewater from aquaculture ponds and the increased use of chemicals to control diseases in an unchecked fashion will contribute to ecology pollution, affecting future crops' efficiency [32]. Another study [33] indicated that farmers had not used the environmental inputs efficiently, which impacts the surround in the long run. The ecological quality of white leg shrimp ponds is often lower than that of black tiger shrimp [34]. Therefore, the control of environmental pollution in shrimp farming is essential.

In addition, at that place is no deviation in TE between the converted grouping and the traditional grouping, which shows that the converted farm-households and other farming weather condition are productively adaptable to the new model. Therefore, the conversion to white leg shrimp farming has been a sustainable trend for farmers in this area.

We can guess the output losses using Equation (10) based on real output and technical efficiency. The results are illustrated in Figure iv in which technical inefficiency caused significant output losses for white leg shrimp farming in the study sites. Effigy 4 shows that the average output loss of farms is most 1973 kg/hectare. Information technology indicates that these farms could have achieved higher productivity if the inputs were more efficiently used. This is the problem that well-nigh of the agricultural output sectors in Vietnam face. In another study [25] on resource use in rice farming in Vietnam too showed that resources apply efficiency of almost farmers is not high, significantly reducing the farmers' profits. Therefore, resource use efficiency or inputs wrinkle is very crucial to increase farms' productivity.

v. Conclusions

This study estimates the technical efficiency and output losses of white leg shrimp product in the Mekong River Delta. The stochastic borderland method and OLS regression model were used to analyze master information related to the household's socioeconomic characteristics, technical aspects, inputs, and outputs of the white leg shrimp farming collected from direct interviews with white leg shrimp farming households. The research found that the hateful output-oriented technical efficiency of farms was 72.9%, pointing out that the farms take the ability to increment shrimp yield by 27.ane% with present inputs application. In that location was a big difference in technical efficiency among farmers in the sample, especially many households still accept very low technical efficiency. Information technology indicates that many farms still have not made practiced use of their inputs in the almost efficient way or are still facing risks in production.

In addition, the results besides revealed that there is no difference in technical efficiency betwixt the converted and traditional groups. This is a positive point showing the loftier adaptability to the new model of converted households. Therefore, the white leg shrimp farming model has been a sustainable management for these farmers. Although some environmental variables had been included in this enquiry model, the ecological impacts from white leg shrimp production accept not been estimated. This limitation will be the topic for the following written report.

6. Recommendations

Given the results, it is essential to help these farms improve the technical efficiency in production by training them to utilize inputs more efficiently and avert production risks. Significantly, the output loss is withal very high, with an average loss of 1973 kg/ha; it has impacted farmers' profits essentially; minimizing these losses by efficient use of input resources is the well-nigh practical solution. The relevant management agencies should strictly control the prices and quality of inputs to assist farms minimize risks and increase production efficiency. They should be providing information on proper and optimal chemical use to aid farmers make the right choices, helping to minimize the impact on the environment affecting time to come crops. Forecasts most changes in the environmental atmospheric condition, weather, and climate should be provided to farms promptly, allowing farmers to adapt the ingather schedule and inputs use plan.

Author Contributions

Conceptualization, M.S.R.T. and H.T.V.; methodology, K.Due south.R.T.; formal assay, One thousand.S.R.T.; investigation, H.T.V.; data curation, H.T.V.; writing—original draft grooming, Thousand.S.R.T.; writing—review and editing, K.S.R.T. and J.-Y.50. All authors accept read and agreed to the published version of the manuscript.

Funding

This inquiry is funded by Vietnam National Foundation for Science and Technology Evolution (NAFOSTED) under grant number 502.01–2020.27.

Data Availability Argument

The data used during the electric current study are available from the corresponding authors on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

AE	Allocative Efficiency
EE	Economic Efficiency
OL	Output Loss
OLS	Ordinary Least Square
OTE	Output-oriented Technical Efficiency
RE	Revenue Efficiency
RO	Real Output
SFA	Stochastic Frontier Analysis
TE	Technical Efficiency

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Figure 1. Technical, allocative and economic efficiency.

Figure 1. Technical, allocative and economic efficiency.

Figure two. Technical and allocative efficiency from an output orientation.

Figure two. Technical and allocative efficiency from an output orientation.

Figure 3. The map of the Mekong Delta, Vietnam.

Figure 3. The map of the Mekong Delta, Vietnam.

Figure 4. Output losses due to technical inefficiency.

Figure 4. Output losses due to technical inefficiency.

Table ane. The farming area of black tiger shrimp and white leg shrimp in the period 2010–2018.

Table 1. The farming surface area of black tiger shrimp and white leg shrimp in the period 2010–2018.

Provinces	Latitudes and Longtitudes	Unit	2010		2012		2013		2014		2018
			BTS	WLS	BTS	WLS	BTS	WLS	BTS	WLS	BTS	WLS
Long An	10.69° N, 106.24° E	one thousand ha	2.19	0.95	i.48	2.40	i.10	2.71	1.00	5.seventy	0.76	6.28
Tien Giang	10.44° N, 106.34° E	1000 ha	iii.71	0.66	3.38	0.83	2.74	1.34	ii.65	1.38	ane.93	2.45
Ben Tre	x.24° Due north, 106.37° E	chiliad ha	30.03	0.77	28.79	ii.44	26.05	5.39	29.51	5.11	25.75	10.35
Tra Vinh	ix.95° N, 106.33° E	one thousand ha	25.38	0.03	23.97	0.52	25.89	ii.32	xx.65	5.15	25.00	seven.sixty
Soc Trăng	9.lx° N, 105.96° E	1000 ha	48.34	0.29	39.26	4.41	30.48	xv.54	57.05	27.01	23.48	32.67
Bac Lieu	ix.25° N, 105.51° E	1000 ha	124.98	3.42	116.02	3.24	119.30	4.89	122.21	8.07	140.26	8.48
Ca Mau	ix.fifteen° Due north, 105.19° E	thousand ha	266.54	0.08	264.20	2.36	263.52	3.53	262.80	6.60	261.85	8.03
Kien Giang	9.82° Northward, 105.12° E	1000 ha	80.98	0.74	85.99	i.06	86.84	1.15	94.42	ane.91	121.77	ii.44
Full		m ha	582.xviii	six.98	563.11	17.29	555.95	36.91	590.31	lx.95	600.82	78.32

Tabular array 2. Descriptive statistics of inputs and output in white leg shrimp cultivation.

Table two. Descriptive statistics of inputs and output in white leg shrimp tillage.

Item	Annotation	Unit of measurement	Hateful	Std. Dev.	Min.	Max.
Yield	Y	Kg/ha	8541.39	8956.64	174.00	70,000.00
Feed	X 1	Kg/ha	6736.23	7303.lxxx	60.00	55,000.00
Medicine	X 2	chiliad VND/ha	102,199.40	164,827.40	300.00	1,259,737.00
Fuel	10 3	1000 VND/ha	4180.52	5.12128	58.82	52,500.00
Seed	X iv	larvae/ha	795,386.ten	613,786.60	xl,000.00	4,411,765.00
Labor	X 5	man twenty-four hours/ha	334.25	221.51	9.56	1125.00

Table 3. Stochastic frontier product estimates.

Tabular array 3. Stochastic frontier production estimates.

Variables	Stochastic Frontier
	Coefficients	Standard Error
Feed quantity	0.3280 ***	0.0490
Medicine costs	0.1119 ***	0.0324
Fuel and raw materials costs	0.1204 **	0.0473
Seed quantity	0.1624 **	0.0743
Labors for farming activities	0.2017 ***	0.0550
Constant	0.8876	0.7209
$\ln {\sigma }_v^2$	−1.227	0.3249
$\ln {\sigma }_u^2$	1.6589	1.3465
${\sigma }_{\mathrm{five}}$	0.5414	0.0879
${\sigma }_u$	0.4362	0.2937
${\sigma }^{\mathrm{two}}$	0.4834	0.1720
$\lambda ={\sigma }_u/{\sigma }_5$	0.8058 ***	0.3766
Log Likelihood	−182.1696
Wald χ2 value	285.47

Tabular array iv. Frequency distribution of technical efficiency for white leg shrimp farming.

Table 4. Frequency distribution of technical efficiency for white leg shrimp farming.

Output-Oriented Technical Efficiency (%)	Number of Subcontract	Percentage	Cumulative
>90 ≤ 100	0	0	0
>80 ≤ 90	xxx	15	15
>70 ≤ 80	112	56	71
>60 ≤ 70	47	23.5	94.5
>50 ≤ 60	9	4.five	99
≤50	2	1	100
Mean TE (%)		72.nine
Minimum TE (%)		44.1
Maximum TE (%)		87.three

Table v. The difference in TE betwixt the two farmer groups.

Table v. The departure in TE betwixt the two farmer groups.

Farmer Groups	Number of Farm	Mean	Std. Deviation	Std. Fault Mean	Pr (|T|>|t|)
Traditional grouping	99	0.7359	0.0842	0.0084	0.1979 ns
Converted group	101	0.7222	0.0644	0.0064

Table half-dozen. Factors associated with technical efficiency.

Table half-dozen. Factors associated with technical efficiency.

Variables	Coefficients	Robust Standard Error
Constant	0.6707 ***	0.0217
Experience	−0.0004 ns	0.0011
Farming density	0.0001 **	0.0000
Swimming size	0.0086 *	0.0045
Swimming size square	−0.0003 **	0.0001
Labor	0.0078 **	0.0038
Swimming	0.0028 ns	0.0033
Purifying swimming	0.0025 ns	0.0115
Conversion	−0.0080 ns	0.0126

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