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

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 BETW

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

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 and Climate change, 2017, 166-177 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

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

- 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 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

multi-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

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

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

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; (iii) Land allocation based on Cellular 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 economic-environment factors affecting agricultural land use:

socio-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, economy and environment affected land use The number of interviews was determined based on formula Yamane (1967)

Where N: total agricultural households; e: sample error

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

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

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

I DEN_LUT (i) = number_of_neighborhood _LUT(i)/8 (2)

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

I cap_LUT (i) =(W R I R + W C I C + W DEN I DEN_LUT (i) + W I I Invest )/( WR+WC+ (3)

WDEN+W I )

In which: WR, WC, WDEN, W I are weights for IR, IC, I DEN_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

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

(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

Long Phu Trường Khánh, Tân

Thạnh,

Long Phú, Song Phụng, Hậu Thạnh

Long Đức, Châu Khánh, Tân Hưng, Phú Hữu

Tran De Trung Bình, Lịch Hội

Thượng, Thạnh Thới Thuận, Viên Bình

Viên An Đại Ân 2, Liêu Tú,

Tài Văn, Thạnh Thới

An

My Xuyen Hòa Tú 1, Hòa Tú 2, Ngọc

Tố, Đại Tâm, TT Mỹ Xuyên

Ngọc Đông, Gia Hòa

1, Gia Hòa 2

Tham Đôn, Thạnh Phú, Thạnh Quới

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