THÔNG TIN TÀI LIỆU
MINISTRY OF EDUCATION AND TRAINING CAN THO UNIVERSITY SUMMARY OF PHD THESIS Land Management Code: 9850103 DANG TRUNG THANH RESEARCH AND DEVELOPMENT ORIENTATION OF AGRICULTURAL PRODUCTION MODELS FOR SMART URBAN DEVELOPMENT PLANNING (Case study of Thu Dau Mot city - Binh Duong province) Can Tho, 2023 THIS STUDY WAS ACCOMPLISHED AT CAN THO UNIVERSITY The scientific guidance: Assoc Prof Dr Pham Thanh Vu The scientific guidance: Prof Dr Vo Quang Minh The thesis will be defend against the council state thesis meeting at the Place: ………………… , Can Tho University At … … date … month … year 2023 Reviewer 1: Reviewer 2: The thesis could be found at the library: Learning Resource Center, Can Tho University National Library of Vietnam LIST OF PUBLISHED SCIENTIFIC ARTICLES Dang Trung Thanh*, Vo Quang Minh, Pham Thanh Vu (2021) Evaluating the efficiency of reuse of sludge from snakehead fish pond as fertilizer for growing spinach in the suburbs of Thu Dau Mot urban area Journal of VietNam Soil Science ISSN: 2525-2216 No 64/2021: 36-41 Dang Trung Thanh*, Nguyen Minh Ty, Vo Quang Minh, Pham Thanh Vu (2022) Survey of agricultural production models in the suburbs of Thu Dau Mot city - Binh Duong province Journal of VietNam Soil Science ISSN: 2525-2216 Số 69/2022: 122-127 Dang Trung Thanh, Nguyen Minh Ty, Håkan Berg, Nguyen Vinh Hien, Thi Kieu Oanh Nguyen, Pham Thanh Vu, Vo Quang Minh, Chau Thi Da (2023) Effects of organic fertilizers produced from fish pond sediment on growth performances and yield of Malabar and Amaranthus vegetables Journal Frontiers in Sustainable Food Systems Sec., Waste Management in Agroecosystems IF: 5,005 Vol 7–2023, pp.01-12 https://doi.org/10.3389/fsufs.2023.1045592 Dang Trung Thanh*, Vo Quang Minh, Pham Thanh Vu (2022) Weight of Factors Affecting Sustainable Urban Agriculture Development (Case study in Thu Dau Mot Smart city) Intelligent Computing & Optimization ISBN: 978-3-030-93247-3 Vol 371 Springer, pp 707–717 https://doi.org/10.1007/978-3-030-93247-3_68 Dang Trung Thanh*, Nguyen Huynh Anh Tuyet, Vo Quang Minh, Pham Thanh Vu (2021) Application of MicroStation and Tk-Tool in Assessing the Current Status and the Change of Agricultural Land in Ben Cat Town from 2014 to 2019 Research in Intelligent and Computing in Engineering ISBN: 978-981-15-7527-3, pp.241–253 https://doi.org/10.1007/978-981-15-7527-3_24 Dang Trung Thanh*, Nguyen Huynh Anh Tuyet, Vo Quang Minh, Pham Thanh Vu (2022) Applying the process of hierarchical analysis to assess barriers to agricultural production development in the suburbs of Binh Duong province, Vietnam Intelligent i Computing & Optimization ISBN: 978-3-031-19958-5 Vol 569 Springer, pp.599–610 https://doi.org/10.1007/978-3-031-19958-5_56 Dang Trung Thanh*, Nguyen Huynh Anh Tuyet, Vo Quang Minh, Pham Thanh Vu (2022) Evaluation of the Economic Efficacy of Models for Urban Agricultural Production in Binh Duong province, Vietnam Intelligent Computing & Optimization Vol 569 Springer, pp.732–744 https://doi.org/10.1007/978-3-031-19958-5_69 Dang Trung Thanh*, Nguyen Huynh Anh Tuyet, Vo Quang Minh, Pham Thanh Vu (2023) GIS and RS Application for Land use status quo Mapping in 2020 and Land use change assessing in Thu Dau Mot city Machine Learning and Mechanics Based Soft Computing Applications SCI, volume 1068, pp.117–132 https://doi.org/10.1007/978-981-19-6450-3_13 Note: * Lead author and corresponding ii Chapter Introduction 1.1 The urgency of study Thu Dau Mot City (TDMC) is the administrative, economic and cultural center of Binh Duong province To build and develop according to the criteria of a smart city, the task of smart economic development is very important In particular, the agricultural economic sector is determined to face the most difficulties due to the specific characteristics of the industry In order to develop urban agricultural production, improve the value of each unit of land area, contribute to the total value of the province's products, improve income and living standards, create jobs, maintain the urban greening and environmental protection, topic: "Research and development orientation of agricultural production models as a basis for smart urban development planning (specific research for TDMC – Binh Duong province)” was was implemented 1.2 Objectives of the study - Evaluation of the current situation, classify agricultural production models (APMs) for each area of the urban area; - Identify the factors affecting the development of agricultural models according to each urban area and the priority level in the development of urban APMs; - Evaluate the effectiveness and select potential UA models; - Build smart urban agriculture models and propose solutions to develop smart urban agricultural models for smart city construction 1.3 Subjects and scope of research - Existing APMs in urban and suburban areas - Research and develop smart urban APMs for the purpose of building and developing smart cities - Natural, socio-economic conditions have an influence on the development of APMs in TDMC 1.4 New finding of the thesis Identify factors and levels of influence in the development of UAPMs for each urban area of the city Determine the order of priority in the development of agricultural production models for each urban area Proposing solutions to develop smart UAPMs Chapter Research methodology 2.1 Content 1: Assessment of the current status of urban agricultural development in Thu Dau Mot city 2.1.1 Secondary data collection method Collect relevant documents available for research 2.1.2 Farm household survey method Using the Participatory Rural Appraisal technique (Nabasa, et al., 1995) Collect practical information on the production status of agricultural models In this study, the household survey was conducted directly using a questionnaire Survey scale: determine sample size by Slovin method (1984) for small sample size and know the population: n = N / [1 + N (e)2] (1) In there: • n: sample size; • N: total number of population; • e: allowable error (reference error levels are 1%, 5% and 10% for statistical confidence levels of 99%, 95% and 90%) From the general list of the Economic Department and the Farmers' Association of TDMC, there are 400 agricultural production households, with e=5% instead of the formula on the calculated sample of 200 samples (table 2.1) How to choose the survey sample intentionally, the order is odd number according to the existing list Table 2.1 Distribution of survey questionnaires by administrative units No 10 11 12 Administrative units (wards) Chanh My Chanh Nghia Dinh Hoa Hiep An Hiep Thanh Hoa Phu Phu Cuong Phu Hoa Phu Loi Phu My Phu Tan Phu Tho Type of production Cultivation Livestock Aquaculture 3 12 8 3 12 28 11 14 15 Total 15 23 11 15 12 31 11 19 22 13 14 Tan An Tuong Binh Hiep Tổng 12 132 27 41 20 200 2.1.3 Data processing methods - Current status of agricultural land use in the period 2010-2020 of TDMC is summarized using Microsoft Excel software Compare, analyze and evaluate the current status and changes of land use by types of use - Analysis of survey data on the production situation of farmers by type of production and by urban areas 2.1.4 Map method Using Microstation software to edit the current map of agricultural land use 2.2 Content 2: Evaluating the effectiveness and selecting suitable urban agriculture models for development 2.2.1 Methods of evaluating investment efficiency Using Excel to calculate: cost, income, profit, capital efficiency, labor efficiency of each investigated agricultural production model 2.2.2 Methods of evaluating economic, social and environmental effectiveness From the survey results on APMs, based on the results of economic, social and environmental performance indicators It is proposed to decentralize the indicators according to levels of efficiency assessment: high, medium and low with the corresponding scores of 3, and (Do Van Nha et al., 2016) 2.2.3 Method of building hierarchy table The hierarchy of factors affecting the development of smart urban agriculture models is made in the following order: - Research and synthesize documents to determine the criteria and initial requirements for smart urban agriculture development Combined with the results of the survey on agricultural production models available in the area (in content 1) and consulted The factors affecting the development of urban agricultural models are aggregated and hierarchical according to groups of factors at level 1, including factors: technology - technique; economy; society and environment - Develop specific influencing factors (level 2) on the basis of synthesizing requirements in production of different types of agriculture in the area and consulting Research has synthesized in the group of engineering technology with level factors; economic group has factors; the social condition group has factors and the environmental group has level factors - From the identified hierarchical factors, consult experts to determine the priority and importance of level and level factors for smart urban agriculture development and calculation Global factors affecting the development of smart urban agricultural models of TDMC 2.2.4 The method of assessing the weight of influencing factors - Evaluate the weight of factors affecting production development (according to the level of interest of the subjects being investigated) For example, if a general requirement group has requirements, the rating score is assigned from to 4) of the subject groups for each requirement of each production model, normalizing to the same value from to (Sharifi, 1990), to solve the multi-objective problem (Van Pham Dang Tri, 2001) about the necessary requirements (2) In there: Xi: The ith request interest normalization score (range to 1) : is the total value of the scores of the evaluators for the ith requirement in the j requirement group for the development of UAMs : is the highest total score value of the requirement in the requirement group j for the development of UAMs according to expert opinion In the multi-criteria evaluation (MCE) method, based on the weighted values (W) to determine the importance of the factors 2.2.5 Statistical analysis method Statistical analysis of agricultural production according to survey results: using SPSS software (Nie, Bent & Hull, 1970) - Descriptive statistics: mean (Mean); standard deviation (SD) of productivity, economic efficiency of APMs - Comparative statistics on the difference between models in urban areas in terms of economic, social and environmental efficiency: Anova, Duncan 2.2.6 Method of consultation To evaluate the factors affecting the development of urban APMs The study investigated and interviewed experts who are experts, scientists specialized in agriculture, land management, natural resources and environment The number of consultation votes is according to the prepared form 2.3 Content 3: Building smart urban agricultural production models for Thu Dau Mot city 2.3.1 Model building method On the basis of selected models of agricultural production with economic, social and environmental efficiency (in content 2), select models capable of applying smart technology to production Typical models built include: cultivation, livestock and aquaculture Models must be suitable for production conditions in urban areas, have high economic efficiency and limit pollution Empirical models focus on applying smart technology to the production process 2.3.2 Methods of measuring the growth of plants and animals - Observe the growth, and measure the growth and development indicators of plants and animals in experimental models - Evaluating the development indicators of the experimental model with conventional production without applying smart technology in terms of: investment, productivity, time for harvesting, output, product price, income and profit 2.4 Content 4: Orientation and propose solutions to develop smart urban agricultural models 2.4.1 Development oriented construction method - Applying documents on smart city development, combined with recommendations of common standards on smart city, identifying guiding criteria for smart city construction planning - Synthesize, argue and propose smart city development orientation for TDM smart city construction planning 2.4.2 Methods of building development solutions The solution to develop smart UAMs for the purpose of planning and building TDM smart city is built on the following bases: - Results of the assessment of factors affecting the development of urban agricultural models (in content no 2) Based on the weights of level 1, level and global factors to determine the solution to focus on investing in each area: urban core, periphery and suburbs - Results of building models of UAP applying smart technology (in content no 3) To identify appropriate production models proposed development for each area: urban core, periphery and suburbs 2.4.3 Map method Using MicroStation software to present the results of proposing smart urban agricultural models according to the space of urban areas The content and methods of the study are presented in Fig 2.1 Fig 2.1 Flowchart of objectives, content and implementation methods Fig 3.4 Labor efficiency of 26 production models 3.2.2 Synthesize economic, social and environmental efficiency of urban agricultural models * Economic efficiency: The following economic indicators are used in the assessment: revenue, profit and efficiency of capital Table 3.5 Classification of indicators to evaluate economic efficiency Rating Level Score level High Medium Low Revenue/1,000m2 /year (million VND) > 30 20 – 30 < 20 Profit/1,000m2/ year (million VND) > 15 10 - 15 < 10 efficiency of capital / 1.000m2 (time) >2 1–2 50 30 - 50 < 30 Value of working day of labor /1.000m2 (million VND) > 0,3 0,2 – 0,3 < 0,2 * Environmental efficiency: In this study, only evaluating the environmental efficiency of each land use type based on scoring criteria, namely: Solid waste, wastewater and ecological landscape Table 3.7 Classification of environmental performance evaluation criteria Rating Level Score level Solid waste Wastewater High Reused Treated and reused 15 Ecological landscape Lush, diverse Medium Low Collected Not yet collected Treated Not processed yet Medium Low * Evaluation of the overall effectiveness of UAPMs: the highly effective model has a score of 19-24 points, the model with average efficiency scores from 13-18 points, the model with high efficiency scores from 11 to 16 points low-performing models have a score of 12 or less Details are presented in Table 3.8 Table 3.8 Economic, social and environmental effects of production models Efficiency (points) No 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Model Total Economy Society Environment score Rice cultivation (control) 12 Hydroponic 19 Vegetables in pots, crates 19 Vegetables grown on the ground 21 Mushroom cultivation 19 Planting crops 6 21 Planting orchids 22 Planting flowers of all kinds 22 Plant flowers for Tet 5 19 Planting bonsai trees 21 Planting melons 6 21 Citrus fruit trees 18 Other fruit trees 18 Rubber tree 12 Raising rabbits 19 Concentrated pig raising 19 Dairy cows 19 Raising cows, buffalo for meat 18 Poultry farming 19 Breeding ornamental fish 20 Breeding fish for meat 17 Aquaculture combined with 6 21 entertainment services Fish breeds farming 16 Eel farming 19 Frogs farming 19 Crocodile farming 19 Summary of evaluation results Low High High High High High High High High High High Medium Medium Low High High High Medium High High Medium High Medium High High High The data in table 3.8 shows that there are two models with low efficiency: rice cultivation and rubber plantation 3.2.3 Factors affecting the development of agricultural models in each urban area 3.2.3.1 Urban core area 16 - Level factors affecting the development of agricultural models There are 04 criteria at level identified: technology engineering, economy, society and environment According to the analysis results, the factor technology - engineering has the highest weight (W=0,477) (table 3.9 and fig 3.5) The reason is that production conditions are limited by the size of the area and labor Fig 3.5 Weighting of level factors affecting agricultural models in urban core areas - Level factors affecting the development of agricultural models + Technology - engineering in farming: production management is considered the most important (Fig 3.6a) The reason is said to be highly urbanized production conditions, the need for automatic production, and the reduction of manual labor + Economic conditions: product consumption is the most interested job with W=0.367 (Fig 3.6b) The reason is that many households have difficulty accessing stable agricultural product consumption channels + Social conditions: factors about people's health and spirit have the highest importance with W=0.343 (Fig 3.6c) The results of this assessment have reflected the focus of people in the urban core area about the health and mental health of the community + Environment: landscape, ecology is the most concerned factor with W=0,534 (Fig 3.6d) The reason is considered to be due to the relatively high income and economic conditions of the population in the urban core area, so people have paid more attention to the ecological landscape and living environment 17 a) b) c) d) Fig 3.6 Level factor weighting in terms of industry and economy (a), economy (b), society (c) and environment (d) for the development of agricultural models in urban core areas 3.2.3.2 Urban periphery - Level factors affecting agricultural production models Similar to the urban core area, the importance level according to the analysis results is the technology-engineering factor with W=0.387 (table 3.9 and fig 3.7) Because production conditions are also limited by land and labor, the application of technology - engineering in production is of the utmost concern However, the weights between the factors have significant variation compared to the urban core area Fig 3.7 Weighting of level factors affecting agricultural patterns in urban periphery - Level factors affect agricultural production models Similar to the contents analyzed in the urban core area Level factors are assessed for each area as follows (Fig 3.8) Technology 18 engineering in farming: greenhouse has the most important level Economic: investment capital for production has the most important level Social: consultant has the most important degree Environment: solid waste problem of the most importance a) b) c) d) Fig 3.8 Level factor weights on technology - engineering (a), economy (b), society (c) and environment (d) for the development of agricultural models in periurban areas 3.2.3.3 Suburban area - Level factors affecting agricultural production models For the suburban area, there is a difference compared to the above two areas, the importance here according to the analysis results is the economic factor with W=0,354 (table 3.9 and fig 3.9) The reason is that people mainly rely on direct income from agricultural activities Fig 3.9 Weighting of level factors affecting agricultural patterns in suburban areas 19 Table 3.9 Decentralized synthesis of factors affecting urban agricultural production in Thu Dau Mot city 23 24 25 26 Environment 18 19 20 21 22 Society 15 16 17 Basic construction cost 0.179 0.043 0.149 0.143 0.367 0.017 0.029 0.026 0.036 0.034 0.087 0.002 0.004 0.003 Consultants Trademark Land use rights Health Supporting policies 0.039 0.094 0.175 0.343 0.277 0.005 0.012 0.022 0.044 0.035 Solid waste Wastewater 0.101 0.098 0.016 0.015 Air pollution 0.267 0.042 Ecological landscape 0.534 0.084 0.238 Production cost Profit Product consumption Educational level Labour Infrastructure 0.127 0.158 0,160 0.082 0.042 0.035 0.031 0.037 0.036 0.030 0.040 0.140 0.054 Investment 0.287 0.091 Basic construction cost 0.194 0.062 0.148 0.215 0.156 0.047 0.068 0.050 Educational level Labour Infrastructure 0.091 0.068 0.068 0.012 0.009 0.009 Consultants Trademark Land use rights Health Supporting policies 0.291 0.156 0.105 0.108 0.114 0.040 0.021 0.014 0.015 0.015 Solid waste Wastewater 0.342 0.218 0.055 0.035 Air pollution 0.209 0.033 Ecological landscape 0.231 0.037 0,318 Production cost Profit Product consumption 0,136 0.213 0.108 0.090 0.080 0.096 0.093 0.077 0.103 20 Technology and engineering 0.039 Membrane house, net house Farming on the land By means of scaffolding Hydroponics, aeroponics Automatic watering 0,387 Automatic fertilizer Environmental sensor Production manager Product storage and processing Economic 0.071 0.163 Technology and engineering 0.148 Investment Level factors Urban suburbs Weight Weight Global Level of level of weights level (W=W1 factors factors factors *W2) (W2) (W1) Society 12 13 14 0.026 0.023 0.030 0.041 0.076 0.049 0.073 0.088 Economic 11 Economic 10 0.054 0.048 0.064 0.086 0.158 0.103 0.153 0.185 Society Membrane house, net house Farming on the land By means of scaffolding Hydroponics, aeroponics Automatic watering 0.477 Automatic fertilizer Environmental sensor Production manager Product storage and processing Environment Technology and engineering Level factors Urban periphery Weight Weight Global Level of level of weights level (W=W1* factors factors factors W2) (W2) (W1) Environment Urban core Weight of No Level level factors factors (W1) Level factors Membrane house, net house Farming on the land By means of scaffolding Hydroponics, aeroponics Automatic watering 0,286 Automatic fertilizer Environmental sensor Production manager Product storage and processing 0,162 0.162 0.174 0.099 0.098 0.100 0.087 0.090 0.073 0.046 0.050 0.028 0.028 0.028 0.025 0.026 0.021 0.117 0.033 Investment 0.203 0.072 Basic construction cost 0.171 0.060 0.164 0.240 0.223 0.058 0.085 0.079 Educational level Labour Infrastructure 0.134 0.153 0.095 0.027 0.030 0.019 Consultants Trademark Land use rights Health Supporting policies 0.128 0.131 0.117 0.105 0.136 0.025 0.026 0.023 0.021 0.027 Solid waste Wastewater 0.244 0.316 0.040 0.051 Air pollution 0.208 0.034 Ecological landscape 0.232 0.038 0,354 Production cost Profit Product consumption 0,198 Weight Global of level weights (W=W1 factors *W2) (W2) - Level factors affecting agricultural production models Level factors are highly appreciated for each area as Fig 3.10 Technology - engineering in farming: farming on land is the most concerned Economy: profit is most concerned Social: labor has the most important level Environment: water has the most importance Fig 3.10 Level factor weights on technology - engineering (a), economy (b), society (c) and environment (d) for the development of agricultural models in suburban areas 3.2.4 Priority in the development of urban agricultural production models of Thu Dau Mot city The summary results of priority levels based on the weighted values of factors affecting agricultural production in TDMC are presented in Table 3.10 Table 3.10 Priority in the development of urban agricultural models Factors Level Level Area Urban core Urban periphery Urban suburbs Number priority Number priority Technology - engineering Economy Number priority Environment Technology - engineering Economy Environment Economy Technology - engineering Society Urban core Environment (Ecological landscape) Economy (Product consumption) Society (Health) Urban periphery Environment (Solid waste) Society (Consultants) Economy (Investment) Urban suburbs Environment (Wastewater) Economy (Profit) Technology engineering (Farming on the land) 21 Urban core Level (full) Urban periphery Urban suburbs Technology - engineering Economy (Product (Production manager) consumption) Technology - engineering Economy (Investment) (Membrane house, net house) Economy (Profit) Environment (Wastewater) Environment (Ecological landscape) Environment (Solid waste) Technology engineering (Farming on the land) Table 3.10 shows that, in order to develop urban agricultural production models, when considering the global weights in the order of priority for the areas as follows:: - Urban core: priority is Technology - engineering (production management), priority is economics (consumption of products) and priority is environment (landscape, ecology) - Urban periphery: priority is economic (investment capital), priority is technology - engineering (membrane house, net house) and priority is environment (solid waste) - Urban suburbs: priority is economic (profit), priority is environment (wastewater) and priority is Technology - engineering (cultivation on land) 3.3 Building smart urban agricultural production models for TDMs On the basis of 21 selected urban agricultural production models (section 3.2.2), there are models selected for testing the application of smart technology to production These are models in the top 10 models with the highest capital or labor efficiency The results show that when applying smart technology to production, it helps to reduce labor, increase productivity, optimize the farming environment, shorten the time for harvesting, and increase efficiency from 1.80 - 2.07 times compared to production in the condition that smart technology is not applied, in addition, it also contributes to environmental protection 3.4 Orientation and propose solutions to develop smart urban agricultural models 3.4.1 Orientation to develop smart urban agricultural models 3.4.1.1 Criteria for building Thu Dau Mot smart city Applying documents on smart cities, the proposed criteria for building TDMC include 14 criteria as shown in Fig 3.11 22 Fig 3.11 Criteria for building Thu Dau Mot smart city (Source: Compiled and drawn by the author) 3.4.1.2 Identifying requirements for smart urban agriculture development for TDMc By synthesizing domestic and foreign documents and from testing results of UAMs applying smart technology and techniques in production The requirements for the development of smart urban agriculture models include contents as shown in fig 3.12 Fig 3.12 Requirements for smart urban agriculture development (Source: Compiled and drawn by the author) 3.4.1.3 Orientation to develop smart urban APMs Synthesize and evaluate the economic, social and environmental effectiveness of 26 urban production models, combined with the above smart city construction and development goals There are 21 proposed urban APMs for future development (table 3.11) models are not proposed: (i) growing citrus fruit trees, (ii) planting other fruit 23 trees, (iii) breeding fish, (iv) growing rice and (v) growing rubber trees These models have a general average to low efficiency Forecast agricultural land fund to 2030 and propose the development of APMs The smart technology to be applied, the capital investment capital requirements, production capital and profit of each model are presented in Table 3.11 and Fig 3.14-3.16 Fig 3.14 Map of the development of agricultural models in urban core areas Fig 3.15 Map of developing agricultural Fig 3.16 Map of the development of models in urban periphery agricultural models in urban suburban areas 24 Table 3.11 Forecast agricultural land fund to 2030 and propose the development of agricultural models for each urban area Agricultural land area (ha) Area I II Model Hydroponics Vegetables of all kinds grown in pots and boxes Mushrooms (medicine) Planting orchids Planting bonsai Aquarium fish farming Hydroponics Vegetables of all kinds grown in pots and boxes Vegetables of all kinds grown on the ground Growing mushrooms of all kinds Planting orchids Planting flowers background Planting Tet flowers Planting bonsai Growing melons 10 Raising rabbits 11 Poultry farming 12 Aquarium fish farming 13 Raising fish for meat 14 Aquaculture combined with entertainment services 15 Eel farming 16 Raising frogs 17 Crocodile farming Forecast Reduced to 2030 compared to 2020 0.0 250,7 Requirements on agricultural land Yes No (1) (2) (3) (4) (5) (6) (7) (8) x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x -2.3 -358,5 Smart technology x x 25 Can Average Average produce capital Average production without construction profit/year capital applyin investment (million (million g smart unit (million VND/1000m VND/1000 technol VND/1000m ) m2) ogy ) 480 55 105 x 350 65 52 x x x x x x 300 320 250 1,000 480 50 60 100 300 55 50 80 50 380 105 x x x 350 65 52 x x x x 350 50 70 x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 300 320 350 250 250 400 350 1,000 1,000 200 50 60 100 50 100 90 100 3,050 300 100 50 80 60 50 50 60 150 341 380 80 x x x x x x 500 200 160 x x x x x x x x x x x x x x x x x x 200 200 200 250 200 500 90 70 60 x x x x x x Agricultural land area (ha) Area III Model Hydroponics Vegetables of all kinds grown in pots and boxes Vegetables of all kinds grown on the ground Growing mushrooms of all kinds Planting crops Planting orchids Planting flowers background Planting Tet flowers Planting bonsai 10 Growing melons 11 Raising rabbits 12 Raising pigs 13 Raising dairy cows 14 Raising buffaloes and beef cattle 15 Poultry farming 16 Aquarium fish farming 17 Raising fish for meat 18 Aquaculture combined with entertainment services 19 Eel farming 20 Raising frogs 21 Crocodile farming Forecast Reduced to 2030 compared to 2020 Requirements on agricultural land No (1) (2) (3) (4) (5) (6) (7) (8) x x x x x x x x x x x x x x x x x x x x x 350 65 52 x x x x x x x x 350 50 70 x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 300 250 320 350 250 250 400 350 900 500 300 1,000 1,000 200 50 50 60 100 50 100 90 100 1,500 1,500 2,000 3,050 300 100 50 50 80 60 50 50 60 150 300 300 400 341 380 80 x x x x x x x x 500 200 160 x x x x x x x x x x x x x x x x x x x x x x x x 200 200 200 250 200 500 90 70 60 x -909.0 Can Average Average produce capital Average production without construction profit/year capital applyin investment (million (million g smart unit (million VND/1000m VND/1000 technol VND/1000m ) m2) ogy ) 480 55 105 Yes x 1,386.6 Smart technology x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x Note: - Urban core area (I); urban periphery (II); and urban suburbs (III) - Smart technology: (1) – (8) corresponds to the requirements of smart urban agriculture development (Fig 3.12) - Capital construction investment rate, production capital, and profit are calculated for models with smart technology investment 26 3.4.2 Proposing solutions to develop smart urban agriculture models for Thu Dau Mot smart urban planning Based on the weight of factors affecting agricultural production, the proposed solutions include: technology - engineering; planning and planning; policy 3.4.2.1 General solution (1) Technology - engineering solutions: In the immediate future, automation technologies in agricultural production, associated with a traceability platform are the areas that need to be prioritized for research so that they can be applied in a short time (2) Planning solutions, plans - Provincial People's Committee to integrate urban agriculture planning in provincial planning to 2030 In which zoning of urban areas is associated with population density and urban functions; - Develop a plan to develop smart agricultural products with quality and value, prioritizing local typical products (3) Policy solutions - Provincial People's Committee promulgates policies on investment capital for agricultural production Expand cooperation with institutes, schools, and technology enterprises to acquire appropriate smart agriculture and digital governance technologies; - Strengthen human resource training, especially high-quality human resources to proactively approach smart agriculture 4.4.2.2 Specific solution (1) Urban core area: - Technology - engineering solutions should be given top attention (priority 1, item 3.2.4) Solutions to improve the rate of technology application in production are: encouraging, mobilizing and supporting people (details in section 3.4.2.1, item (1)) - Solution on land fund: agricultural land is expected to be gone by 2030 (all 2.3 ha) The solution is to develop hydroponic plants, grow on substrates, pots, containers and raise ornamental animals, using terrace space, hanging gardens, high-rise farming - Production scale, investment capital: The average proposed production scale is 300-400 m2 of production space/model Capital construction investment capital 400 million/1,000 m2 27 - Production model: proposed models (detailed table 3.11) The main products are: fresh vegetables with high economic value, creating green space, entertainment, health promotion activities (2) Urban periphery: - Economic solutions (investment capital) are given top priority (section 3.2.4) Solutions to address investment capital for production development include: policies on loans, incentives, support from development funds, banks, and credit (section 3.4.2.1, item (3)) - Production scale and investment capital: The average proposed production scale is 2,000 m2/model Capital construction investment capital 380 million/1,000 m2 - Production model: 17 models are proposed (details in table 3.11) - Land fund: agricultural land by 2030 is expected to be about 251 (reduced by 364 ha), the solution is to develop models using less land, multi-functional agriculture combined with services In the future, when the urban periphery grows up, the solution is to develop crops and livestock that use less agricultural land or not use land as the condition of the current urban core area (3) Urban suburban areas: - Economic solutions (profit) should be given top attention (priority 1, section 3.2.4) To improve profits, it is necessary to combine many solutions such as: Technology - engineering; policies and implementation organization (section 3.4.2.1, sections (1), (3)) in order to reduce input costs and improve production efficiency - Production scale, investment capital: The proposed average production scale is 5,000 m2/model Construction investment capital 400-420 million/1,000 m2 - Production model: proposed 21 models (detailed table 3.11) - Land fund: agricultural land by 2030 is expected to be about 1,387 (reduced by 934 ha) Developing local specialty crops, raising dairy cows, raising pigs, poultry in cold cages and aquaculture combined with services In the future, when urban suburbs grow up, crops and livestock will be developed that use less land and save natural resources, similar to the current conditions of urban peri-urban areas 28 Chapter Conclusion and suggestions 4.1 Conclusions (1) The study assessed the current situation of agricultural production and classified 26 agricultural production models for each area: core, periphery and suburbs of Thu Dau Mot city (2) The study has identified 26 factors affecting the development of agricultural models by urban areas and the priority in the development of urban agricultural production models for each region, specifically: (i) Urban core: Priority is Technology - engineering (ii) Urban periphery: priority is economic (investment capital) and (iii) Urban suburbs: priority is economic (profit) At the same time, evaluate the economic, social and environmental effectiveness of each model and select 21/26 urban agriculture models that have potential for development and facilitate the application of smart technology in production (3) The study has built typical urban agricultural production models that apply smart technologies to production, including: (i) hydroponic vegetable growing, (ii) leafy vegetable growing on land, (iii) growing tomatoes on substrates, (iv) growing medicinal mushrooms, (v) raising poultry and (vi) raising ornamental fish The results show that the models have a higher efficiency of 1.80 - 2.07 times than the same model produced in the condition that no technology is applied (4) The study proposed groups of general solutions to develop agricultural production models and specific solutions for each urban area for the goal of planning and building Thu Dau Mot smart city by 2030, orientation to 2050 4.2 Suggestions Continue to research and build effective smart urban agricultural models, save resources and reuse by-products for the goal of Thu Dau Mot smart city development And the applicability to cities with similar conditions 29
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