THE MINISTRY OF EDUCATION AND TRAINING CAN THO UNIVERSITY
SUMMARY OF THE DISSERTATION Major: Land Management Major Code: 62 85 01 03
NGUYEN HONG THAO
ANALYZING THE RELATIONSHIP BETWEEN SOCIO-ECONOMIC AND ENVIRONMENTAL FACTORS FOR BUILDING AN INTEGRATED SYSTEM SUPPORTING AGRICULTURAL LAND USE PLANNING. A CASE STUDY IN SOC TRANG PROVINCE Can Tho, 2021
THE PROJECT WAS COMPLETED IN CAN THO UNIVERSITY
Supervisor: Assoc. Prof. Dr. Nguyen Hieu Trung
The dissertation was defended to the doctoral committee at college level Venue: Administration Building, Can Tho University Date and time: 14:00 PM, December 25, 2019.
Reviewer 1: Assoc. Prof. Dr. Le Van Trung Reviewer 2: Assoc. Prof. Dr. Chau Minh Khoi
Finding dissertation at: Learning resource center, Can Tho University Viet Nam National Library
PUBLICATIONS
1. Nguyen Hong Thao and Nguyen Hieu Trung, 2017. Establish open-source application for optimization agricultural land-use area. Can Tho University, Journal of Science 52a, 62-71. https://doi.org/10.22144/ctu.jvn.2017.111
change, 2017, and 2. Nguyen Hong Thao, Nguyen Hieu Trung, Le Quang Tri, 2017. Establishing the model for supporting agricultural land use allocation - A case study in My Xuyen district, Soc Trang province. Can Tho University, Journal of Science, Special issue: Environment 166-177. Climate https://doi.org/10.22144/ctu.jsi.2017.065
3. Thao, N.H. and Trung, N.H., 2018. Establishing an integrated model for supporting agricultural land use planning: A case study in Tran De district, Soc Trang province. Can Tho University Journal of Science. 54 (Special issue: Agriculture): 62- 71. https://doi.org/10.22144/ctu.jsi.2018.096
4. Nguyen Hong Thao, Nguyen Hieu Trung, Truong Chi Quang, Pham Thanh Vu, Phan Hoang Vu, Vuong Tuan Huy, Dang Kim Son, 2019. Application of optimizing algorithm and allocation of agricultural land use in the Mekong Delta. Journal of Soil Science, Special issue 57, 97–102.
5. Nguyen Hong Thao and Nguyen Hieu Trung, 2019. Using the Monte Carlo model to predict agricultural production areas for land use optimization. Can Tho University, Journal of Science 55, Special issue: Environment and Climate change (2): 164-174. https://doi.org/10.22144/ctu.jsi.2019.143
CHAPTER 1. INTRODUCTION
1.1 The necessity of the thesis
The land use planning is based on a 10-year cycle and on the criteria of the Ministry of Natural Resources and Environment (Ministry of Natural Resources and Environment, 2014). The FAO's guiding procedures for land use planning process (FAO, 1981) consists of 7 steps. In which there are two steps that need to be emphasized to ensure the sustainable development, which are the natural land suitability assessment and socio-economic and environmental appraisal. However, the step of alternative assessment of socio-economic factors often faces many difficulties because these conditions often changed from different regions, thus it difficult to apply by planners.
In order to do alternative assessment of socio-economic factors, the authors used different methods such as multi-objective land assessment (Pham Thanh Vu et al., 2009), Analytic Hierarchy Process (AHP) in land use classification (Akıncı et al., 2013; Elaalem et al., 2010). The advantage of these methods is to propose the optimized area for agricultural land use types (LUTs) based on constraints. However, there are limitations on result’s maps where many land use types are suitable for one land unit. Therefore, it is necessary to research and develop a supporting software program to simplify the application process based on the identified socio-economic factors.
The request on optimal spatial arrangement of LUTs in agricultural land use planning is the most a big question of the planner. Many studies have been carried out that can be listed are the methods applied for arranging LUTs based on MCA multi- criteria analysis with GIS and on Cellular Automata (CA) (Le Canh Dinh, 2011). These methods have helped planners in their decision-making. However, these current spatial arrangement methods do not take into account specific impact factors on land use such as infrastructure for agriculture as well as the risk on implementation, capability investment of land use types. Therefore, it is necessary to build a model of land use arrangement for agricultural production taking into account the socio- economic factors and the influence of transport and canals systems, electric supply systems, neighborhood land use, and investment capacity of farmers as well as the rate of poor households in the local area affecting the spatial arrangement of agricultural land use types.
Since the advantages and shortcomings of relevant studies, it is necessary to fulfill the lag of previous studies. Specifically, this thesis focuses on analyzing the relationship between socio-economic and environmental factors affecting the choice of agricultural land use type in establishment optimization model; develop an integrated system that allows planners to build planning solutions based on land suitability, socio-economic conditions, infrastructure, and farmer’s ability to achieve optimal spatial location according to characteristics of each type of agricultural land use.
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1.2 Objectives
Overall objective The research objective of the thesis is to analyze the main socio-economic - environmental factors affecting the types of agricultural land use. These factors can be used for building an integrated model to optimize the agricultural land use area and land use allocation. That can help planners to improve the efficiency of land use planning.
Specific objectives
Objective 1: Identify socio-economic and environmental factors affecting agricultural land use. Objective 2: Building an application to optimize the area of agricultural land use types using open source tools. Objective 3: Building an integrated model to optimize and allocate for agricultural land use map. Objective 4: Application of the integrated model in agricultural land use planning.
1.3 Contents of the study
(1) Analyze the relationship between socio-economic and environmental factors
for optimizing and allocating agricultural land use
(2) Develop open source computer software for optimizing agricultural land (3) Building an integrated model in agricultural land use arrangement. (4) Application of integrated models in formulating agricultural land use plans.
1.4 Research objects and scope of the study Research objects
The study focused on identifying the main socio-economic, environment factors that mainly affect the arrangement of agricultural land use types in land use planning. Mathematical model in optimizing agricultural land area and spatial arrangement model of agricultural land use maps. Scope of the study
In terms of space, the thesis focusses on the research and experimental application of the integrated model in 3 districts representing for 3 ecological regions of Soc Trang province: Long Phu district where corresponds to the freshwater ecoregion, My Xuyen district where corresponds to the brackish water ecoregion outside the dike and Tran De that corresponds to the brackish water ecoregion inside the dike.
1.4.1 The scientific meaning and applicability
1.4.2 Scientific meaning
The thesis contributes new scientific points as follows: - Identifying main socio-economic and environment factors affecting the choice of agricultural land use types. Relationships of these factors in land use optimization and land allocation mapping.
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- Developing a computer software for solving optimizing agricultural land use with user interface. This is a specialized tool for the management and planning of agricultural land use. - Developing a model of spatial arrangement for land use mapping based on multi- criteria evaluation. Land use arrangement was based on economic priority level, land suitability and dominant characteristics of land use types, spatial correlation with social and environmental conditions as well as neighboring agricultural land use patterns, current land use status and infrastructure.
1.4.3 Applicability
The methods, tools and processes proposed from the results of the thesis is a useful reference for research at university, master and PhD level in Land Management. The socio-economic, environmental factors and the integrated model could be used as a decision supporting tool for land use planning process that can help planners to improve quality of agricultural land use plans.
CHAPTER 2. LITERATURE REVIEW
2.1 Factors affecting agricultural land use
According to many studies on land use in general, including agricultural land use, is influenced by socio-economic and environment, policy factors (Lambin and Geist, 2007). Considering foreign studies related to this impact, Baker and Capel (2011) in the United States showed that there are three main factors: socio-economic, environmental issues that determine the distribution of agricultural cultivation. In Europe, Cintina and Pukite (2018) show the economic, social and environmental, policy and management, technical and technological, subjective of farmers are the main affected factors. In Vietnam, studies show that natural, socio-economic and environmental factors have a strong impact on land use and the implementation of land use planning.
In Vietnam, the study of Bui Anh Tuan et al. (2013) in the case study in Son Tay Town (Hanoi) used regression statistics to evaluate which factors have a positive relationship affecting land use management. The results show that land policies, support policies (capital, technology); soil properties; size of farm land, the role of media and information are the selected factors.
Huynh Van Dung (2017) assessed 14 socio-economic factors affecting the implementation of land use plan in Giong Rieng district, Kien Giang province by using AHP method. The results showed that the economic factor was assessed as important (the weight of 0.61) compared to the social factors which were 0.21 and environment was 0.18. The results showed that in terms of economic factor group, production cost was the most important factor of farmer with a weight of more than 50% compared to the other profit and market. For social factors groups such as capital capacity, capital
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supporting, farming practices, technical assistance, job creation, land use planning. In which, the ability of capital of farmers, the support of capital and farming practices were the important factors.
However, the studies of factors only for assessing which factors are important, but did not show the applicability in the selection of land use and land arrangement in land use planning.
2.2 Optimization methods in agricultural land use planning
Multi objective optimization is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. In land use planning, optimizing is used to estimate land use area based on the constraint between nature, socio-economy and environment (Nguyen Hai Thanh, 2005; Nguyen Hieu Trung et al., 2015).
Optimization with single or multiple objectives is a method to find the best possible solution according to a certain requirement. Actually, it is a decision-making supporting. Decision-making is one of human activities and has been studied since the late 18th century. The method of decision making includes the choice of development options; therefore, it is important in many fields of science including social science as well as natural science (Nguyen Nhu Phong, 2010a). It can be said that optimization is the most important quantitative tool for decision making (Nguyen Hai Thanh, 2005; Pham Thanh Vu et al., 2016).
2.3 Studies related to agricultural prediction
Predictions are estimates and evaluations of future events, which are often uncertain. The goal of forecasting is to use existing information in a best way to guide future activities. Predictions are divided into two categories, qualitative and quantitative. In which, qualitative methods are based on qualitative data such as opinions, judgments, professional experience of experts. In contrast, the quantitative method is based on quantitative data collected over time series (Nguyen Nhu Phong, 2010a). Predictions based on the mathematical model need to analyze need large data source collected for building models.
Regarding simulation method in forecasting, according to Fishman (1997), Monte Carlo simulation method provides estimated solution based on statistical sampling method using computers. This is a method of solving non-random problems by random sampling. The samples are randomly selected and repeated several times to calculate the results according to the method of the given problem. Therefore, the Monte Carlo method often provides an estimated solution with acceptable errors in the case of difficult to find the exact solution for the problem.
Many studies have been done to predict factors such as price, demand for agricultural products, and output of agricultural products, but choosing a feasible method is important to determine the threshold for production. Forecasting production demand based on local production potential is more feasible than one for agricultural demand. Agricultural productions from historical production areas in many years meet supply and demand mechanism of the market
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2.4 Methods of agricultural land allocation in land use planning
Riveira and Maseda (2006) reviewed models and application methods in land use planning. There were two main stages: the land suitability assessment stage and the land allocation stage. Land use allocation was an important step in the development of planning options, which answered questions about arrangement of LUTs.
allocation Cellular based Land (iii) on
Solutions and software for land use allocation according to environmental, socio-economic conditions. The previous studies showed that land allocation solutions have been divided into the main groups: (i) Land allocation based on land suitability assessment. (ii) Land allocation based on multi-criteria evaluation, but mainly on AHP method; Automata. Most researchers used the first method that based on natural factors (soil, terrain, and hydrology) to determine level of natural adaptation, suitable results showed in form of adaptive maps. This kind of map provided information about multiple choices for a land mapping unit but it did not indicate where each land use type are located.
The approaches used the Cellular Automata (CA) method to analyze the land (Ligtenberg, 2010). According to Le Canh Dinh (2011), SALUP software has been developed based on CA model. In which land use selection was based on determining on the current land use then using the spreading principle of Cellular Automata to arrange the land area until end of area need to be located. The limitation of the pure CA method is that the selection algorithm did not consider the characteristics of infrastructure, social practices, and investment capacity of each land use type to be arranged.
CHAPTER 3. RESEARCH METHODOLOGY
3.1 Method for analyzing the relationship of socio-economic-environment
factors affecting agricultural land use
These kinds of data were collected for analyzing the relationship of socio-
economic-environment factors affecting agricultural land use:
Land use statistics and land use maps of 3 districts Long Phu, Tran De and My
Xuyen in 2005, 2010 and 2015;
Socio-economic statistics from 2010 to 2018; The annual agricultural reports of Long Phu, Tran De and My Xuyen districts
from 2015 to 2018.
Household survey to collect information on agricultural production, socio- economy and environment affected land use. The number of interviews was determined based on formula Yamane (1967).
(1) 𝑛 = 𝑁 1 + 𝑁𝑒2
Where N: total agricultural households; e: sample error.
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Number of agricultural households of 3 districts in the study area is about 55,000 households, sample error is selected as 6%, and thus, the number of samples n calculated was 276 samples. Considering the number of samples of similar studies by according to the district and province levels of Le Quang Tri et al., (2013); Thai Phu Vinh et al., (2015); Santiphop et al. (2012). The total number of interviews in 3 districts was rounded up to 45 households / LUT. Therefore, the total number of famers for surveying is 315 households.
Descriptive statistical methods (Mann, 1995) was used to determine average values and standard deviations for quantitative economic indicator as profits of land use types; Qualitative social indicators include: education level, intensive farming level, production capital, technological and scientific transferring; farmer's risk assessment, infrastructure requirements for production; impact of agricultural land use types as environmental qualitative factor.
3.2 Methods of building the integrated model
The integrated model, named ST-IALUP (Soc Trang- Integrated model for supporting Agricultural Land Use Planning), was a combination of various tools where input and output data were well connected. The integrated model provided agricultural land area estimation, agricultural land area optimization software - LandOptimizer, and land use allocation model - ST-LUAM. The principle of integration was shown in Figure 3.2.
Figure 3.1 Integrated model ST-IALUP
Figure 3.2 showed the connection of components of the integrated model named. Using Monte Carlo method is to estimate the land use areas of limited land use types. Estimated land use areas were exported to a CSV file containing the area of the LUTs in the predicted over years. This data source was used as constraint value in LandOptimizer. This software gives optimized area for each LUT and was connected to the land allocation model, ST-LUAM, which performs land use solution maps.
3.2.1 Agricultural land use area estimating
In terms of estimating land use area, this thesis focused on analyzing three types of agricultural production: vegetables, fruit and aquaculture. Cultivating area was estimated based on Monte Carlo simulation method which was applied as the diagram
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in Figure 3.3. In which, data of area of annual crops, fruit and aquaculture from 2010- 2010 were loaded into the model.
Figure 3.2 Estimating the cultivated area using Monte Carlo method
Next, historical of cultivated area was analyzed to get frequency of occurrence. This frequency data was normalized. For each LUT, model generate cultivated values of next years by generating a random frequency in the range [0, 1]. This random number was used to get the cultivating area according with frequency where area values were classified. After that, the model checks condition to stop simulation, if it is false the calculated area value will be returned to the list of area values the next simulation cycle.
3.2.2 Method for developing agricultural land use optimization
LandOptimizer software was built using the programming language Visual Basic.Net on Windows operating system. The main steps of software development were shown in Figure 3.4.
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Figure 3.3 Main construction’s steps of LandOptimizer software
3.2.3 Method of building land use allocation model
After determined optimal area of each land use type (LUT) per each land unit, the thesis proposed a detailed land use allocation within each land unit. In particular, land use types are arranged into the cells inside a land unit map by the Cellular Automata method and multi-criteria assessment based on natural, socio-economic and environment factors as shown in Figure 3.5.
Figure 3.4 Diagram for building land allocation model
The economic investment capacity index of cells (IInvest): This index was assigned values from commune group. Communes are classified into three groups depending on the level of achieving New Rural construction Standards (NRS); Then standardize the commune group into 3 values [1; 0.5; 0]. These values were assigned to the commune boundary map.
The distance index from a cell to the nearest road (IR) and canal (IC) is calculated by the shortest distance from the position of each cell to the nearest road (canal). In the model, buffer method was created layer by layer from the road (canals) to determine the distance of cells contained by the buffers. Distance values were also normalized to the range [0, 1] according to the maximum distance of a cell to the roads (canals).
Ratio of density of land use type in neighborhood of a cell (IDEN_LUT). This index was determined by counting number of neighborhood cells of each land use types divided by 8.
(2) IDEN_LUT(i) = number_of_neighborhood _LUT(i)/8
The allocation capability index for a LUT of a cell (Icap_LUT) was determined by the formula (3). A LUT was assigned into a cell when it had highest value of Icap_LUT. In case there are many LUTs with the same value of Icap_LUT, the LUT was randomly selected from these LUTs. Icap_LUT(i) =(WR.IR + WC.IC + WDEN. IDEN_LUT(i) + WI.IInvest )/( WR+WC+ (3)
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WDEN+WI )
In which: WR, WC, WDEN, WI are weights for IR, IC, IDEN_LUT(i). These values
will be identified in calibration process of the model.
CHAPTER 4. RESULTS AND DISCUSSION
4.1 Introduction to the study area
Soc Trang is located at the southern mouth of Hau River in the Mekong Delta (Mekong Delta), about 60 km from Can Tho city, with geographical coordinates from 9014’28 '' to 9055’30 '' North latitude; 105034’16 '' to 106017’50 '' East longitude. Bordering provinces: Hau Giang, Tra Vinh, Bac Lieu and the East Sea. In 2018, Soc Trang province consists of 11 administrative units (1 city, 2 towns and 8 districts): Soc Trang city, Vinh Chau town, Nga Nam town, districts: Ke Sach, My Tu, Cu Lao Dung, Long Phu, My Xuyen, Thanh Tri, Chau Thanh and Tran De.
To keep the objective of the thesis, the study area was selected according to the criteria of contiguous districts with fresh, brackish and saline water ecological characteristics. This helps to survey specific land use for these ecoregions. Based on the selected criteria, the study area consists of 3 districts of My Xuyen, Long Phu, and Tran De in Soc Trang province. In particular, Long Phu is in a fresh water area but risk of being affected by saline intrusion during extreme weather events (such as drought and saline intrusion in 2016); My Xuyen belongs to brackish water region; Tran De is divided into two areas: the saline area at the mouth of the river outside the dike and the area of fresh water inside the dike systems.
In order to analyze the changes in rice cultivation area over the years as a basis for predicting the development area for specialized cultivation, fruit and aquaculture, the statistics from year 2010 to 2018 for all 3 districts was shown in Figure 4.1. Specifically, the cultivated area of the districts concentrates only 2 crops in which the Summer-Autumn crop lasts for a large area of cultivation. The Summer-Autumn crop is included in the Summer-Autumn and Spring-Summer crops.
(Source: Generated from Statistic year book 2012-2018) Figure 4.1 Cultivated area of agricultural land use of 3 districts from 2010 to 2018
Figure 4.1 shown cultivated area of vegetables, fruit and aquaculture in Long Phu, Tran De and My Xuyen districts from 2010 to 2018. In general, the area of vegetables and crops has fluctuated up and down but in the recent period, there was a
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tendency increase continuously. In contrast, the area of fruit has the least variation and tends to range from 8,546 ha to 8,938 ha. Particularly, aquaculture area tends to increase continuously from 2010 to 2018.
4.2 Analysis of factors affecting agricultural land use
4.2.1 Selection of types of agricultural land use
In this study, the agricultural land use selected for research include the ones for the fresh, saline ecology areas which are rice, crops, aquatic products, and fruit. The selected land use types are representative of the ecological regions of the three districts in order to have a basis for data survey. The basis for selecting LUTs was based on relevant studies in the Mekong Delta region and was being of interest by the Department of Agriculture and Rural Development of Soc Trang (2018). Prospective LUTs in 3 districts include: 3 rice crops; 2 rice crops (Winter-Spring-Summer Fall), 2 rice crops + 1 vegetables, Rice-Shrimp, Vegetables (2-3 crops), Fruit and Shrimp (2-3 seasons).
4.2.2 Analysis of economic factors affecting agricultural land use
4.2.2.1 Profit of LUTs
Profit is one of the most concern criteria for the choice of agricultural land use types. Statistical results of the survey showed that most farmers wanted to choose the land use production with high profit. The statistical results described the total profit of the 7 uses are shown in Figure 4.2 in which the most profitable was shrimp farming, the two rice crops had lowest profit.
Figure 4.2 Profit of land use types
Figure 4.2 showed that there had huge difference in profits, especially between LUT7 (VND 277.23 million) and LUT2 (only about VND 42.42 million). However, in order to be able to implement LUT7, it is necessary to have not only capital but also intensive farming techniques as well as natural suitability conditions.
4.2.2.2 Investment capacity factors
The results of the implementation, being recognized by new rural construction
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(NRC) communes. In new rural construction criteria, there are many important indicators including income of households, poverty rate of communes. Therefore, this thesis uses NRC commune criteria as a qualitative factor affecting the disposition of agricultural land use. Based on these two criteria (income and poverty rate), the economic capacity of the communes is divided into 3 groups: Group 1 was the communes meeting NRC standards; Group 2 was the communes that have not met the NRC standard but have an average per capita income of VND 20-28 million and a poverty rate lest than 6%; Group 3 is the remaining communes (Table 4.1).
Table 4.1 Group of communes based on economic capacity
Dictrict
Group 1
Group 2
Group 3
Long Phu
Tân
Phú,
Song
Trường Khánh, Thạnh,
Long Phụng, Hậu Thạnh
Long Đức, Châu Khánh, Tân Hưng, Phú Hữu
Tran De
Viên An
Trung Bình, Lịch Hội Thượng, Thạnh Thới Thuận, Viên Bình
Đại Ân 2, Liêu Tú, Tài Văn, Thạnh Thới An
My Xuyen
Ngọc Đông, Gia Hòa 1, Gia Hòa 2
Tham Đôn, Thạnh Phú, Thạnh Quới
Hòa Tú 1, Hòa Tú 2, Ngọc Tố, Đại Tâm, TT Mỹ Xuyên
19/19
11-18/19
≤10/19
No of criteria archived
Average income per capita / year
NRC qualified (greater than 30 Million VND)
Not up to standard of NRC (20-28 Million VND)
Not up to standard of NR (< 20 Million VND)
Poverty rate
NRC qualified ≤ 4%
NRC qualified (≤ 6%)
Not up to standard of NRC (≤ 23%)
(Source: Generated from annual report of Long Phu, Tran De and My Xuyen)
4.2.3 Social factors
4.2.3.1 Number of labors
The results of the survey for the number of labors for each agricultural land use type per year were shown in Figure 4.3. Among them, the vegetables needed the highest number of working days, followed by shrimp that farmers have to take care all of the year. Two rice crops – vegetable and fruit have the similar number of working days, equivalent to 115 and 121 days per hectare per year.
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Figure 4.3 Number of workdays for LUTs during the year
4.2.3.2 Relationship between infrastructure and land use
The survey results showed that local agricultural production was facing problems such as: (i) water supplied and drainage due to distance from canals, (ii) difficulty in transporting materials and travel due to narrow or unpaved roads, (iii) affecting farming practices due to saline leakage from shrimp ponds to rice fields. Particularly for aquaculture, besides natural conditions such as land and water, people still faced difficulties in production due to lack of electricity or weak operation of equipment, thus affecting production efficiency.
Figure 4.4 Infrastructure requirements of LUTS
The three LUTs such as two rice crops, three rice crops and rice-shrimp, are the types affected by neighbor land uses. Especially for rice-shrimp, if surrounding households raise shrimp or hold salt water in the pond, then neighboring rice-growing households will not be able to cultivate or achieve low yields. For shrimp and fruit farming, the most priority was strong electric power source to operate machinery, followed by the need to be located near the road as well as the influence neighbor LUTs. In fact, if they want to cultivate aquaculture products, the neighboring households must also cultivate the same style, which will bring about high efficiency. About 20% of people agreed that vegetables and fruits should be located near rivers and canals. However, vegetables and fruit needed to be located near the road due to the
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farming behaviors of farmers.
4.2.4 Environmental factors affecting agricultural land use
4.2.4.1 Risk of LUTs
Besides natural characteristics of land, risk factors in production of land use types that were assessed by people through four levels such as high risk, medium risk, and low risk and non-risk as shown in Figure 4.5.
(a) (b)
Figure 4.5 Risks of use types (a) and environmental benefits of LUTs (b) Risk factors include more or less uncertainty about productivity, prices and weather risks. Figure 4.5 showed that over 60% of the people assessed the shrimp farming style with highest risks in production. On the contrary, about 50% of people think of using two rice crops or two rice crops – vegetable provided low-risk or no-risk crops. In the case of fruit and vegetables, the average risk was assessed because the risk of farming depends only on the market, and the yield and weather do not usually affect these LUTs.
4.2.4.2 Environmental benefits of LUTs
The analysis results show that the use patterns such as two rice crops, two rice crops-vegetable, rice - shrimp were assessed to be good for the environment. In contrast, three rice crops, shrimp and vegetables were evaluated as not good for the environment. At a level that was not good for the environment, vegetables has the highest rate of bad environmental among the LUTs. The shrimp was only assessed to be medium to the environment. The results of these assessments will be used to set goals in the application of agricultural land use optimization.
4.2.5 Summary of main factors affecting agricultural land use
The specific socio-economic factors surveyed had different impacts on LUT. For applying these factors in building an integrated model to support land use planning, these factors are ranked based on the statistical results for 2 main purposes. The order of applicability of these factors was presented in Table 4.2. Each factor was considered to serve for one purpose: optimize land use area and allocate agricultural land use.
Table 4.2 Summary of the influence of factors on agricultural land use
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Factors Detailed Affecting LUTs and it orders Applied
Optimization Profit
Economic Allocation Capacity of investment
Optimization No of labors
Allocation Road systems
Social Allocation Road systems
Allocation
LUT 7, LUT 6, LUT 5, LUT 4, LUT 3, LUT 1, LUT 2. LUT 7, LUT 6, LUT 5, LUT 4, LUT 3, LUT 1, LUT 2 LUT 5, LUT 7, LUT 3, LUT 6, LUT 1, LUT 4, LUT 2 LUT 5, LUT6, LUT 7, LUT 1, LUT 4, LUT3, LUT 2 LUT 5, LUT 6, LUT 7, LUT 4, LUT3, LUT 1, LUT 2 LUT 7, LUT 4, LUT 1, LUT 3, LUT 2 Based on Land suitability order Optimization Neighboring LUT Land suitability
Optimization Environment Risk of LUT
Optimization Benefit of environmnent LUT6, LUT 7, LUT 1, LUT 5, LUT 4, LUT 3, LUT 2 LUT 2, LUT 4, LUT 3, LUT 1, LUT 6, LUT 5, LUT 7
Thus, in the optimizing agricultural land use area software, factors such as natural adaptation, profitability, number of labors, risk level, and benefit level of environment were used. These factors were considered as the objectives of the optimization model according to single goal or multiple goals. The priority order of LUTs for each element was different, there are opposite situations. Therefore, the application of optimization model will help balance the impact of these factors on the overall results.
For the allocation of agricultural land use, factors including investment capacity, transport infrastructure, canals and requirements of neighboring LUTs. The order of the LUTs was ranked based on the results of the survey analyzed and considered in the land arrangement. The combined results show that LUT 7 (Shrimp) and LUT 6 (Fruit) are prioritized to be located near roads, canals and rivers, where investment was possible, compared to other kind of land use. This feature was considering for two regions: For brackish areas, shrimp was prioritized to be arranged in priority areas, near roads, rivers and canals, in areas with investment potential, then it gradually spreads out, followed by rice - shrimp arranged. For fresh water region, vegetables and fruit are prioritized to be located near roads, canals and rivers and investment able areas, followed by rice - vegetable and rice (LUT 1, LUT 2).
4.3 Building an integrated ST-IALUP model
The results in previous session showed the relationship of natural, socio- economic, environment factors in optimizing the area of land use and agricultural land use allocation. A newly integrated model called ST-IALUP (Soc Trang- Integrated model for supporting Agricultural Land Use Planning) was built. This integrated model was built with the following tools: (i) Agricultural production area estimating model used to determine boundary conditions of production areas that need for land use optimization; (ii) A new software to optimize the area of agricultural land use to natural, socio-economic and environment according to constraints related
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conditions; (iii) Agricultural land use mapping model for LUTs that have been optimized.
4.3.1 Modeling area for agricultural production
The input data for model collected that were production area of vegetables, fruit and aquaculture in 3 districts was collected from period 2010-2018. The simulation was repeated 10.000 times to determine the mean and the standard deviation from the replicate simulation data. Figure 4.6 shown the area of the 3 LUTs analyzed.
Figure 4.6. Estimated area of vegetables, fruit trees and aquaculture products to 2030
The results of Monte Carlo analysis in Figure 4.6 showed that the area of vegetable land in 2030 is 14.868 ± 894 ha, the area of fruit is 8,799 ± 136 ha and the area of aquaculture land is 16.697 ± 2.540 ha. The mean area of vegetable and fruit did not change much. However, the standard deviation value of vegetables is about 900 ha and aquaculture area was more than 2.500 ha. These values and its range various ranges will be used as constraint values in optimization.
4.3.2 Developing software for agricultural land use optimization
Based on the socio-economic factors affecting the land use area, LandOptimizer software was built to optimize land use area for each land mapping unit based on these factors. Source and packaged program have been uploaded in to website: https://github.com/nhthao/LandOptimizer
4.3.2.1 Designing the input for the software
For the input data, depending on equation programming options, the input data includes: land unit maps, land use suitability of LUTs, profit, number of labor, environment benefit rate, and limited area for LUTs. The profit was based on land suitability level of LUTs in land units. Profit was standardized directly on LandOptimizer as shown in Figure 4.7.
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Figure 4.7 Standardize profit on LandOptimizer
The output data of LandOptimizer were in Excel format that showed the area of LUTs per land unit. For the most part, each land unit could have different LUTs. The results data sheet was also exported to CSV format as input data for the land use allocation tool.
4.3.2.2 Developing optimization objectives
𝑛
𝑚
Optimizing with single goal The objective optimization function is set in the case of optimizing an objective such as adaptation or profit optimization. The objective functions in equations (4) and (5) are used in the case of adaptive optimization and profit optimization of land use types for each land unit.
𝑖=1
𝑗=1
𝑛
𝑚
(4) → 𝑀𝑎𝑥 Maximizing Land suitability level ∑ ∑ 𝑇𝑁𝑖𝑗𝑋𝑖𝑗
𝑖=1
𝑗=1
→ 𝑀𝑎𝑥 (5) Maximizing profit ∶ ∑ ∑ 𝐿𝑁𝑖𝑗𝑇𝑁𝑖𝑗𝑋𝑖𝑗
𝑛
𝑛
𝑛
𝑖=1
𝑗=1
𝑚 + 𝑤2 ∑ ∑ 𝑀𝑇𝑗𝑋𝑖𝑗 𝑗=1
𝑖=1
𝑚 + 𝑤3 ∑ ∑ 𝐿𝐷𝑗𝑋𝑖𝑗 𝑗=1
𝑖=1
𝑛
Multi-objective optimization function: The objectives were maximizing the profit, land suitability, number of local labor used, environmental benefit rate, minimizing risk of LUTs. 𝑚 (6) 𝑤1 ∑ ∑ 𝐿𝑁𝑖𝑗𝑇𝑁𝑖𝑗𝑋𝑖𝑗
𝑚 − 𝑤4 ∑ ∑ 𝑅𝑅𝑗𝑋𝑖𝑗 𝑗=1
𝑖=1
→ 𝑀𝑎𝑥
Where : i = 1..n, n: index of land units; j = 1..m, with m the index of LUTs Xij: Area of LUTj in land unit i. LNij: Profit of LUTj. in land unit i (unit: million VND / ha).
LDj: the number of working days of LUTj. / ha.
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MTj: Environmental benefit coefficient of LUTj.. Farmer’s assessments of
environmental benefits of LUTs.
RRj: Risk coefficient of LUTj. This is the LUTj. productivity risk indicator. The
smaller the risk value gave greater contribution to the goal function.
Wi: The weight of the objectives. In this study, the assumption of equal- weighted goals is set by default to 1 with the meaning that the goals in the multi- objective function have the same priority. These weights can be adjusted for increasing (moving toward 1) or decreasing (moving towards 0) depending on the priority of the local goals for the local development orientation.
Constraint equations Constraint of the total area of LUTs per land use units must be less than or equal
to the area of land unit (Inequalities 7).
(7)
𝑛 𝑚 ∑ ∑ 𝑋𝑖𝑗 𝑖=1 𝑗=1
≤ area of 𝑙𝑎𝑛𝑑 𝑢𝑛𝑖𝑡i
The total labor demand of the LUTs cannot exceed the local agricultural labor resources.
𝑛 ∑ ∑ 𝑖=1
𝑚 𝑗=1
≤ Total working days 𝐿𝐷𝑗𝑋𝑖𝑗 The minimum of each agricultural product to supply (system of inequation 9) (8) (9) ≥ Minimum production of LUT k
∑ 𝑋𝑖𝑗𝑁S𝑗 NSj: Yield of LUTs that provode the product k
j = 1. . l ( LUTs that provide product k) k = 1. . p (type of products)
The maximum of each agricultural product to supply ∑ 𝑋𝑖𝑗𝑁S𝑗 ≤ Maximum production of LUT k
𝑛 ∑ 𝑋𝑖𝑗 𝑖=1
Total area of LUTj <= Total limited area of LUTj (10) (11)
≤ Limited area of LUTj, j = 1. . m The limited area of specific LUTs was estimated in Section 4.3.1. Users can choose one of the pre-set plans to implement the corresponding integrated objective function. Figure 4.8 showed the options for choosing different goals and constraint options for building optimal model.
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Figure 4.8 The interface of optimization on LandOpimizer
The results were displayed in detail on the results box of the software including
the area of LUTs per land unit, total profit, the productions of each product.
Compare the results of LandOpimizer and the optimization model had been built on GAMS (Nguyen Hong Thao, 2007) for the case of study in Co Do District, Can Tho City. The results showed that the optimization results of LandOptimizer and the GAMS mathematical model are similar.
4.3.3 Developing agricultural land use allocation model ST-LUAM
In order to map the area of agricultural land use after optimized where each land unit can be proposed for many different land use types. To solve this problem, the spatial allocation model named ST-LUAM was built based on the method presented in Section 3.3.3.
4.3.3.1 Determining the socio-economic factors used in the arrangement of
agricultural land
In order to ensure the arrangement of the LUTs in accordance with the local reality as required for the agricultural land allocation problem, the LUTs when arranged in cells on the map must meet the following bound conditions: (i) The first condition to consider is the arrangement of agricultural land use types on each location with the highest level of adaptation; (ii) Priority was given to neighboring cells having the same land use type. Agricultural farming is often organized in association of households with similar natural and social conditions. Farmers can learn from each other about farming techniques and agricultural disease treatment; (iii) For some LUTs, priority is required to be given to locations near roads to facilitate the electric
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supply and near rivers, canals to be proactive in water sources for production; (iv) prioritizing the LUTs that need high investment costs for groups of communes meeting new rural construction standards.
4.3.3.2 Calibration and verification of ST-LUAM model
The calibration experiment was set up on GAMA platform to balance weights in the case of land area allocation to land units in 2010 compared to the current status map in 2010. The automatic calibration process was implemented according to the Genetic Algorithm on GAMA software. The Fitness function of the calibration process was set based on the Kappa value when comparing land use map in 2010 and the allocated land use map.
The result of automatic calibration of the parameters determined the weight combination set as follow: WInvest (investment commune) = 0.9; WR (distance to the road) = 0.1; WC (distance to channel) = 0.9; WDEN (Density of LUTs) = 0.7. The set of weighted values found in this section were set by default in the model in the case of study.
With the set of weights found, the model was verified with the land use map in 2015. The results of the land map allocation in case of 2015 were compared to the land use map in 2015 that gave a high similarity index (Kappa = 0.934)
The verification results shown that the ST-LUAM model meets the rational arrangement in reality of the study area when it has taken in account socio-economic constraints.
4.4 Application of integrated models in Soc Trang province
The process of proposed to apply the ST-IALUP integrated model to support land use planning affecting by the socio-economic and environmental conditions including the following steps: Estimating the area of production that help to determine the limited area of LUTs. Using the FAO (1976) land evaluation method to assess the land suitability for selected LUTs. Perform optimization using LandOptimizer software. Then export the data into ST-LUAM format for land use allocation. If there are multiple scenarios, users can repeat the optimization step with different scenarios, then rerun ST-LUAM for arranging the land use map according to the scenarios.
4.4.1 Input data
4.4.1.1 Establishing land unit map and performing land use evaluation to 2030
The monochromatic map layers were overplayed to build land unit map that included soil type map, acid sulfate hazard, the depth of acid sulfate soil layer, salinity map, salinity time (number of months), number of months irrigated capability by fresh water.
The land unit map was created using the overlay method as described in the method section. The result of a map of 28 land units with specific land characteristics is detailed in Figure 4.9
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Figure 4.9 Map of land units of Tran De, Long Phu and My Xuyen districts In which, unit 14 occupies the highest area (18,586.90 ha) in My Xuyen district with soil characteristics belonging to Fluvisols soil type with the depth acid sulfate soil layer appearing less than 50cm, salinity 8 (‰) and salinity duration of 6 months in a year. In Tran De district, there are 2 large-scale land units including unit 3 and unit 6 with the areas of 16,996.50ha and 10,047.49ha, respectively. These are land units with low salinity because they are located in the dyke and are supplied with fresh water through the canal system, but irrigation capacity can only meet 2 crops.
According to the Department of Agriculture and Rural Development of Soc Trang (2018), promising LUTs in 3 districts included: LUT1 - 3 rice crops; LUT2 - 2 rice crops (Winter-Spring-Summer Fall), LUT3 - 2 rice crops + 1 vegetables, LUT4 - Rice-Shrimp, LUT5 - Vegetables (2-3 crops), LUT6 - Fruit and LUT7 - Shrimp (2-3 crops).
4.4.2 Setting up optimization scenarios
The optimization scenarios were set to optimize the agricultural land area of the
three districts until 2030 with economic, social and environmental changes.
Scenario 1: Optimizing agricultural land until 2030 under normal conditions. This scenario was designed to determine the optimal land area and land allocation for agricultural production under the actual natural conditions and socio-economic development until 2030.
Scenario 2: Optimizing agricultural land to 2030 under conditions of environmental and natural changes similar to drought and salinity intrusion phenomenon in 2016. Which was recommended by authorities that farmers should limit three rice crops area. Thus, this scenario is to explain when the three-rice-crop area is recommended not to be used as before, which LUT need to be used to replace it.
For each scenario, there were 3 alternatives: Optimizing land suitability level, optimizing profits, optimizing multi objective (profits, labor, risks and environmental benefit). Regarding limited areas of the LUTs of the scenarios, the minimum and maximum thresholds of LUT’s area were defined in Table 4.3. Table 4.3 Limitation boundaries of the KSDs of the two scenarios
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LUT Scenario 1 Scenario 2
Upper bound Upper bound
Lower bound 0 0 0 0 0 0 0 LUT1 LUT2 LUT3 LUT4 LUT5 LUT6 LUT7 Lower bound 0 0 0 0 0 0 0 Non-limit Non-limit 12.768,00 Non-limit 2.100,01 8.799,00 16.697,01 Non-limit Non-limit 15.436,00 Non-limit 2.500,01 8.936,00 19.236,01
4.4.3 Exploring weights of the multi-objective optimization model
In the two scenarios presented, the weight of the objectives is set by default to 1, which means that the weights are equal. To help managers determine weights for local goals, LandOptimizer provides a new feature that allows users to automatically cycle through goal weights, with each set of LandOptimizer goal weights providing the total area for each land use type.
The objective parameters of the optimization were explored by combining 3 target parameters (W1, W2, W3). In which profit parameter (W1) that can receive from 0.1 to 0.8; W2 from 0.1 to 0.9- W1 and W3 is the compensation for the two first parameters. The explored result of 36 set of parameters showed that the weights W1 = 0.4; W2 = 0.2; W3 = 0.4 were selected to develop multi-objective agricultural land use optimization for 2 scenarios.
4.4.4 Optimizing agricultural land use area
4.4.4.1 Optimization results
The results of optimizing area of LUTs according to 2 scenarios were shown in
Figure 4.10.
(a) (b) Figure 4.10 Area of LUTs of scenarios 1 (a) and scenarios 2 (b) The optimal area of 3 options in scenario 1 were spatially arranged by ST- LUAM model shown in Figure 4.11
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(a)Optimizing land suitability (b) Optimizing profit (c) Multi-objective
Figure 4.11 Land use maps of 3 alternatives in scenarios 1
The optimal options of scenario 2 are arranged on the map by ST-LUAM model
The results of land use allocation in 2 options multi-objective option and profit were relatively similar because the optimal area of LUT3,4,6,7 were the same in the two alternatives. Area of LUT1 and LUT5 in the multi-objective case was less than the optimizing profit case. Inversely, area of 2 rice crops more than the profit case. shown in Figure 4.12.
(a) Optimizing land suitability (b) Optimizing profit (c) Muili-objective
Figure 4.12 Land use maps of 3 alternatives in scenarios 2
In the allocated map of profit optimization option, the area arranged for 2 rice crops - vegetable mainly distributed in Tan Thanh, Huu Thanh, Phu Huu and Tan Hung communes of Long Phu district and Tai Van, Vien An and Thanh communes Thoi An, Thanh Thoi Thuan, of Tran De district.
For the multi-objective optimization plan of Scenario 2, area of two rice crop – vegetables were located in the communes of Long Phu and Tran De district but area in Tran De were increased. Because in the multi-objective optimization model, the area is prioritized to choose according to many criteria of high profits, low risks and high environmental benefits.
4.4.4.2 Selection for the optimal plans
When comparing the profits of the two scenarios, the total profit of the profit optimization and multi-goal optimization plan of scenario 2 is lower than the scenario 1. The profit difference between the two options in each scenario in the range of 10-13 billion VND.
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Figure 4.13 Compare the total profit of the two scenarios in 2030
Comparing the total profit and the goals to be achieved of the options for each scenario, the multi-objective option provided a suitable solution than optimizing of profit option. Thus, depending on the conditions, the multi-goal optimization plan of each scenario was selected.
Based on the allocated map, ST-LUAM provided also an overlapping function that allowed users to identify areas need to be converted for the proposed alternative. Figure 4.14a shown the allocated map the multi-objective option of Scenario 1 compared to land use map in 2015 (Figure 4.14b) and the map of areas that needed to be converted. Noted that in the land use map, there was not distinguish in detail the number season for rice crops nor rice - vegetable rotation crops. It did not distinguish between rice - shrimp and shrimp farming areas. On the map Figure 4.14c, the colored areas were the recommended areas to be changed if the plan will be implemented.
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(a) Multi-objective map (b) land use map in 2015 (c) Areas to be converted
Figure 4.14: Different maps between the multi-objective map and the land use map in 2015 of scenario 1
From the above results, it shows that the integrated ST-IALUP model can integrate optimization software (LandOptimizer) and land use allocation model (ST- LUAM) for building land use plans. At the same time, the results of the model can be easily compared with current land use map that can help planners test different alternative by changing input data.
4.4.5 Applied ST-LUAM in analyzing factors affecting land use allocation
For economic factors, the profitability of LUTs has a direct impact on the total area of LUTs, the capability of investment played an important role in land use allocation. The empirical results of land use arrangement but eliminating the impact factors demonstrated that these factors played an important role in allocation of agricultural land use.
a) No Iinvest b) No IDEN_LUT c) Not IC , nor IR
Figure 4.15 Arranging agricultural land without considering the factors
Figure 4.15a showed that without considering the investment capacity index of the communes, the LUTs land were arranged along canals and roads. The results of the allocated map that did not take into account the road and canals systems, The LUTs of
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two rice - vegetables and shrimp were concentrated only in the communes that provided good investment capability, not along the road and channels as in the two cases above without regard to infrastructure.
The results showed that land use allocation processes need to consider the factors affecting land use allocation. It will contribute to improving the efficiency of land use planning.
CHAPTER 5. CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions
Socio-economic and environmental factors affecting land use and agricultural land development in the study area of Soc Trang province identified for 3 ecological regions: fresh, saline and brackish water regions. Socio-economic and environmental factors are divided into 2 groups: The group of factors influencing land use optimization includes land suitability, profitability, labor force, risk in cultivation and a good impact on the environment; the group of factors includes investment capacity, transport infrastructure, canals, the need to be located next to each other, those affect the arrangement of land use.
An integrated land use planning model under the impact of socio-economic factors called ST-IALUP was built. The model comprises 3 components: (1) Model for estimating the area of the LUTs; (2) LandOptimizer software for land optimization software program; (3) ST-LUAM model for agricultural land arrangement.
The first output of the integrated model was the model for estimating the area of LUTs on the basis of historical data on cultivated areas and the Monte Carlo calculation method. The application of this model identified the linked cultivated area for the optimization of the area of fruit trees, annual crops and aquaculture land.
A new software called LandOptimizer integrated in ST-IALUP has been built. This software enables land use optimization and calculation optimum area for land use systems with single objective optimization functions including profitability, workforce optimization or the multi-objective optimization function. profitability, work, risk reduction and the environment. Based on the functionality of the software, the manager can examine all possible target weights as a basis for selecting options.
Solve the problem of the arrangement of the different LUTs in a land unit thanks to the new ST-LUAM model proposed. In which, the competitive LUTs are prioritized according to the criteria of neighborhoods, proximity to roads, proximity to canals and the municipality's investment capacity.
The thesis proposes the approach using integrated ST-IALUP model which includes the following steps: Determination of the delimited area, optimization of the use area and distributing optimized land use types on the map. The results of the integrated model in the case study in 3 districts of Long Phu, My Xuyen and Tran De, Soc Trang province showed that the optimal plan for the target in the two scenarios is the one chosen because the harmonization between profitability and risk reduction as well as a negative impact on the environment.
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5.2 Recommendations
The ST-IALUP integrated model should continue to be applied in different irrigation conditions as well as with a variety of land use systems in order to continue to be adjusted according to actual conditions.
In addition, the forecasting work of agricultural production is essential for land use planning. Thus, there is a need to extend agricultural production forecasting studies for the optimization of land use area.
