EVALUATION OF COASTAL SANDY LAND IN THANH HOA PROVINCE FOR AGRICULTURAL DEVELOPMENT WITH CASE STUDY OF QUANG XUONG DISTRICT

I n a u g u r a l d i s s e r t a t i o n

zur

Erlangung des akademischen Grades eines

Doktors der Naturwissenschaften (Dr. rer. nat.)

der

Mathematisch-Naturwissenschaftlichen Fakultät

der

Ernst-Moritz-Arndt-Universität Greifswald

vorgelegt von

Hao Huu Nguyen

geboren am 02.03.1981

in Thanh Hoa, Vietnam

Greifswald, 2017

i

Dekan: Prof. Dr. rer. nat. Werner Weitschies

1. Gutachter: Prof. Dr. rer. nat. Reinhard Zölitz

2. Gutachter: Prof. Dr. rer. nat. Ing Ralf Bill

Tag der Promotion: 18th of October, 2017

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Summary

Quang Xuong is considered as one of the most developed districts in Thanh Hoa Province in terms of agricultural. The major purpose of this research is to find good places to suggest for annual crops production in the case study. Therefore, the assessment of land potential productivity, land suitability, and land cover/land use change in different periods is essential for making strategies of sustainable agricultural development as this will help land-users and land managers to discover the potential and limitations of the current existing land conditions to make appropriate policies and plans for future land use. Its results will provide basic information to make reasonable decisions for investments and rational reclamations of cultivated land before and after each crop season in order to meet the objectives of sustainable development in terms of economic efficiency, social acceptability, and environmental protection. The research site is located at latitudes 19034‘N - 19047‘N and at longitudes 105046‘E - 105053‘E. The total area is about 227km2; in which 128km2 is in use for agricultural activities. Based on soil classification methods by FAO-UNESCO (1988), the agricultural land is classified into six main soil groups, including Arenosols, Salic Fluvisols, Fluvisols, Gleysols, Acrisols, and Leptosols, 12 soil units and 18 sub-units. The largest area belongs to the Fluvisols group with 9358.29ha and the smallest area is identified as the Leptosol group with 219.33ha. Most of the soil in this district has low to moderate nutrition, but in general, they are still suitable for agricultural production. There are 42 land units defined in the land mapping, which can be different from each other by one or more land characteristics. The land mapping unit is created from the overlay of all thematic maps of soil chemicals, soil physical characteristics, and relative topography together by application of GIS techniques. It presents land characteristics and properties in this case study and will be used in comparasion with a particular crop requirement for growth in land suitability evaluation process. A certain land unit may be suitable for one or more types of different land use. It is also classified as highly suitable for a specific land utilization type, but less suitable or unsuitable for other crops. For example, in this study, land unit 26 is determined as highly suitable (S1) for growing paddy rice and maize, but it falls into moderately suitability (S2) for groundnut crop by using parametric (square root) method used in this thesis. Depending on the kind of crops need to be evaluated and its requirement for development compared with each land unit characteristics, land-users will determine the best suitable place for crop production.

i

Identification of land use change in different periods of time has become a central key to monitoring of land resources. It is relatively important for effective land management to protect the land resources, especially the land used for agricultural production from overuse and environmental changes. The sprawl of inhabitant areas, development of rural infrastructures, and industrialization are responsible for serious losses of agricultural land. In this study, remote sensing techniques were applied to studying the trends of land cover change in the abovementioned district in a period of about 24 years from 1989 to 2013. ArcGIS software was adopted to develop the land cover and the change of land use maps from 1989 to 2013. Two satellite images with moderate resolution were collected from USGS Earth Explorer website, Landsat5 TM for 1989 and Landsat8 OLI & TIRS for 2013. After image geo-processing, the images were classified into six land cover categories by applying supervised classification method (Maximum Likelihood). The six main obtained land cover types were built-up areas, agricultural land, forest land, water surface area, salty land, and unused land. The overall accuracies of land cover maps for 1989 and 2013 were 94.08% and 92.91%, respectively. The results of change detection analysis indicate that the cultivated, water surface and unused lands decreased by 22%, 17%, and 91%, respectively. In other side, the built-up and salty land increased by 78%, 58%, respectively and forest land increased from 52.69ha in 1989 to 395.76ha in 2013. The assessment of land potential productivity for agricultural production and land suitability for selected annual crops was based on FAO guidelines for land evaluation (FAO, 1976, 1985, and 1993) which were adopted and slightly modified for compatibility with Vietnamese conditions. All related data were stored, analyzed, mapped and presented in ArcGIS software. Weighted Linear Combination Method developed by Hopkins (1977) and GIS techniques were used to analyze and determine the land potential for agricultural use in the study area. The results show that 5.26%, 83.10%, 10.06%, and 1.57% of the investigated areas were assessed as high potential, moderate potential, low potential and very low potential for growing crops. Regarding land suitability evaluation, the simple limitation, parametric (square root), and AHP methods were used to evaluate the suitability levels for selected crops, including paddy rice, sweet potato, groundnut, maize, potato, sesame, soybean, and green pepper. The obtained results indicate that each applied method provides different results of land suitability level for a specific crop in certain land units compared to the other two methods, and OM, soil pH, soil texture, and relative topography were found out as the main limitation factors which affected land suitability level. The study also suggests that three different methods as abovementioned can be expanded and applied in other places with the appropriate factors used for land suitability evaluation according to particular area conditions.

ii

Zusammenfassung

Quang Xuong gilt als einer der wichtigsten Distrikte für die landwirtschaftliche Entwicklung in der Provinz Thanh Hoa. Hauptzweck der vorliegenden Arbeit ist es, für den Anbau einjähriger Kulturen am besten geeignete Flächen im Untersuchungsgebiet zu finden. Für eine nachhaltige landwirtschaftliche Bewirtschaftung ist es notwendig, die potentielle landwirtschaftliche Leistungsfähigkeit, die Landeignung, die Oberflächenbedeckung und die Landnutzungsänderung in verschiedenen Perioden zubetrachten. Die Arbeit soll Landwirte und Landmanager dabei unterstützen, Potenziale und Grenzen der gegenwärtigen bestehenden Bewirtschaftung zu bestimmen, um in der Zukunft gesetzeskonforme Richtlinien und sinnvolle Pläne der Bewirtschaftung zu erstellen. Die Ergebnisse sollen grundlegende Informationen liefern, um angemessene Entscheidungen für Investitionen und Rekultivierungen von Anbauflächen vor und nach jeder Pflanzsaison zu treffen, um das Ziel einer nachhaltigen Entwicklung im Hinblick auf wirtschaftliche Effizienz, soziale Akzeptanz und Umweltschutz zu erfüllen. Das Untersuchungsgebiet befindet sich zwischen den Breitengraden 19034'N- 19047'N und den Längengraden 105046'E - 105053'E. Die Gesamtfläche beträgt etwa 227km2; In welcher 128km2 für landwirtschaftliche Aktivitäten genutzt wird. Auf der Grundlage der Bodenklassifikation der FAO-UNESCO (1988) werden die landwirtschaftlichen Flächen in sechs Hauptbodengruppen, bestehend aus Arenosols, Salic Fluvisols, Fluvisols, Gleysols, Acrisols und Leptosole, 12 Bodeneinheiten und 18 Untereinheiten eingeteilt. Die größte Fläche nimmt die Gruppe der Fluvisole mit 9358,29ha ein, die kleine Fläche wird von der Gruppe der Leptosole Gruppe mit 219,33ha gebildet. Die meisten Böden im Distrikt haben einen niedrigen bis mäßigen Nährstoffgehalt, sind aber generell noch für die landwirtschaftliche Produktion geeignet. Es können 42 Landeinheiten unterschieden werden. Jede Landeinheit kann sich durch eine oder mehrere Eigenschaften voneinander unterscheiden. Eine solche Einheit entsteht aus der Überlagerung aller thematischen Karten von bodenchemischen und bodenphysikalischen Eigenschaften und relative Topographie (relative topography), durch die Anwendung von GIS-Techniken. Diese beschreibt Charakteristiken und Eigenschaften der untersuchten Gebiete und wird verglichen mit Wachstumsanforderungen und Landeignung. Eine bestimmte Landeinheit kann für unterschiedliche Landnutzungen geeignet sein. Sie kann für eine bestimmte Art der Landnutzung präferiert sein, jedoch weniger oder ungeeignet für andere Anbauarten.

Beispielsweise wird in dieser Studie die Landeinheit 26 als sehr geeignet (S1) zum Anbau

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von Paddy Reis und Mais eingestuft, aber nur moderat geeignet (S2) für den Erdnussanbau klassifiziert, nach dem in der Arbeit vorgestellten parametrischen (Quadratwurzel) Verfahren. In Abhängigkeit der untersuchten Anbauart und ihrer Ansprüche und der zur Verfügung stehenden Landeinheiten, kann der Landwirt den am besten geeigneten Platz für diese Feldfrucht bestimmen. Die Identifikation von Landnutzungsänderungen in verschiedenen Zeiträumen ist zu einem zentralen Schlüssel für das Monitoring der Landressourcen geworden. Das ist immens wichtig für ein effektives Landmanagement, um die Bodenqualität zu erhalten, insbesondere der landwirtschaftlich genutzten Bereiche, welche vor Übernutzung und schädlichen Umweltveränderungen bewahrt werden müssen. Die Zersiedelung, die Entwicklung der ländlichen Infrastrukturen und Industrialisierung sind für schwere Verluste an landwirtschaftlich nutzbaren Flächen verantwortlich. In dieser Arbeit wurde Fernerkundungstechnik angewendet, um die Trends der Landnutzungsänderung im Distrikt über einen Zeitraum von etwa 24 Jahren, von 1989 bis 2013, zu untersuchen. ArcGIS Software wurde verwendet für die Erstellung der Karten der Landnutzung und Landnutzungsänderungen in angegebenen Zeitraum. Zwei Satellitenbilder mit moderater Auflösung wurden über die USGS Earth Explorer-Website akquiriert: Landsat5 TM für das Jahr 1989 und Landsat8 OLI & TIRS für das Jahr 2013. Nach einer Vorprozessierung wurden die Bilder in sechs Landbedeckungskategorien unter Verwendung einer überwachten Klassifikation (Maximum Likelihood) eingeteilt. Die sechs Hauptkategorien der Landbedeckung waren bebaute Gebiete, landwirtschaftliche Flächen, Waldflächen, Wasserflächen, salzhaltiges und braches Land. Die Gesamtgenauigkeiten der Klassifikationen für die Jahre 1989 und 2013 betrugen 94,08% bzw. 92,91%. Die Ergebnisse der Veränderungsanalyse zeigen, dass die Anbaufläche, die Wasseroberfläche und die unbenutzten Flächen um 22%, 17% bzw. 91% sanken. Auf der anderen Seite stiegen bebaute und salzige Flächen um 78%, 58% bzw. Waldflächen von 52,69 ha im Jahr 1989 auf 395,76 ha im Jahr 2013. Die Bewertung der potentiellen Produktivität für die landwirtschaftliche Produktion und die Landeignung für ausgewählte einjährige Kulturen basierte auf FAO-Richtlinien für die Landbewertung (FAO, 1976, 1985 und 1993), die zur Anpassung an die vietnamesischen Verhältnisse leicht geändert wurden. Alle genutzten Daten wurden in ArcGIS gespeichert, analysiert, kartiert und präsentiert. Die von Hopkins (1977) entwickelte, gewichtete Linearkombinationsmethode und GIS-Techniken wurden verwendet, um das Landpotential für die landwirtschaftliche Nutzung im Untersuchungsgebiet zu analysieren und zu bestimmen. Die Ergebnisse zeigen, dass

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5,26%, 83,10%, 10,06% und 1,57% der untersuchten Gebiete als hohes Potenzial, mäßiges Potenzial, niedriges Potenzial und sehr niedriges Potenzial für das Wachstum von Nutzpflanzen bewertet werden. In Bezug auf die Land-Eignungs-Bewertung wurden die einfachen Beschränkungen, parametrische (Quadratwurzel) und AHP-Methoden verwendet, um die Eignungsgrade für ausgewählte Kulturen, einschließlich Rohreis, Süßkartoffel, Erdnuss, Mais, Kartoffel, Sesam, Sojabohnen und grünem Pfeffer zu bewerten. Die erhaltenen Ergebnisse zeigen, dass jede angewandte Methode, im Vergleich zu den beiden anderen Methoden, für eine bestimmte Kulturpflanze in bestimmten Landeinheiten unterschiedliche Niveaus in der Landeignung ergeben. OM, Boden pH, Bodentextur und relative Topographie erweisen sich als die Hauptfaktoren in der Beeinflussung des Landeignungsniveaus. Die Studie deutet auch darauf hin, dass die drei genutzten Methoden, erweitert und an anderen Orten, mit geeigneten Faktoren und unter den dortigen speziellen Bedingungen angepasst, für die Landeignungsbewertung verwendet werden können.

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Table of Contents

Summary…………………………………………………………………...……………….i Zusammenfassung...... iii Table of Content...... vii List of Tables………………………………………………………………………………xi List of Figures……………………………………………………………………….…...xiii List of Abbreviations………………………………………………………………….….xv

Chapter 1. Introduction ...... 1 1.1 Background ...... 1 1.2 Problem analysis ...... 3 1.3 Research objectives...... 4 1.3.1 Main objective ...... 4 1.3.2 Specific objectives ...... 5 1.4 Hypotheses ...... 5 1.5 Research questions ...... 6 1.6 Structure of the thesis ...... 6

Chapter 2. State of The Art ...... 7 2.1 Definition and terminology of land evaluation for agriculture ...... 7 2.1.1 Land, land cover and land use ...... 7 2.1.2 Major types of land use, land utilization type and farming system ...... 8 2.1.3 Land evaluation in relation to ecological factors...... 10 2.1.4 Land characteristics and land qualities ...... 11 2.1.5 Cropping system and ecological requirements of crop ...... 12 2.1.6 Land Mapping Unit (LMU) ...... 13 2.1.7 Land Use Planning (LUP) ...... 14 2.1.8 Land sustainability and land suitability ...... 15 2.2 Theory and practices of land suitability evaluation ...... 17 2.2.1 Basic concepts of land suitability evaluation ...... 17 2.2.2 Land evaluation and FAO Framework for land evaluation ...... 18 2.3.3 The need for land evaluation ...... 21 2.3.4 The system of land suitability evaluation ...... 22 2.4 Land evaluation in Vietnam though different periods of time ...... 28 2.4.1 The period before 1975...... 28 2.4.2 The period of 1975 - 1990 ...... 29

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2.4.3 The period of 1990s up to the present...... 30 2.4.4 Discussion of land suitability evaluation in Vietnam ...... 34 2.5 Remote sensing techniques ...... 35 2.5.1 Remote sensing for agriculture and land cover mapping ...... 35 2.5.2 A brief history of Landsat ...... 37 2.5.3 Applications of Landsat data ...... 39 2.5.4 Landsat TM and Landsat OLI-TIRS ...... 40 2.6 Geographic Information System techniques...... 42 2.6.1 GIS and role of GIS for land evaluation ...... 42 2.6.2 Data structure ...... 44 2.6.4 Multi-criteria analysis within GIS context for land suitability ...... 44 2.6.5 Analytical Hierarchy Process theory for land suitability evaluation ...... 47

Chapter 3. Characteristics of the Study Area ...... 49 3.1 Natural resources and environment conditions...... 49 3.1.1 Geographical location and topographical characteristics ...... 49 3.1.2. Climatic condition ...... 49 3.1.3 The water resources ...... 53 3.1.4 Soil resources ...... 54 3.1.4.1 Soil classification in the study area ...... 54 3.1.4.2 Physical and chemical characteristics of major soil groups ...... 58 3.2 Social-economic conditions ...... 61 3.2.1 Population and labour force ...... 61 3.2.2 Economic growth ...... 62 3.2.3 Rural infrastructures situation ...... 64 3.2.3.1 Present rural transportation system ...... 64 3.2.3.2 Irrigation and drainage system ...... 65 3.2.3.3 Electricity system ...... 65 3.2.3.4 School systems ...... 65 3.2.3.5 Health services ...... 65 3.3 Current status of land utilization of 2012 ...... 66 3.4 Cropping system and economic efficiency of some main crops ...... 69 3.4.1 Cropping system ...... 69 3.4.2 Economic efficiency of the main annual crops ...... 72 3.5 Discussion ...... 73 3.5.1 Natural conditions ...... 73 3.5.2 Social-economic and infrastructure conditions ...... 74

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Chapter 4. Data Sources and Methodology ...... 75 4.1 Data resources used for this study ...... 75 4.1.1 Spatial data resource ...... 75 4.1.2 Attribute data resources ...... 76 4.1.3 Software used for management spatial and non-spatial data ...... 76 4.2 Study methodology ...... 76 4.2.1 Methodologies for gathering data ...... 76 4.2.2 Methodologies used for analysis and evaluation of land suitability ...... 78 4.2.2.1 Supervised classification and land cover change detection ...... 78 4.2.2.2 Linear Combination Method ...... 79 4.2.2.3 Simple Limitation method ...... 81 4.2.2.4 Square root method ...... 81 4.2.2.5 Integrated AHP method with GIS for land suitability evaluation ...... 83 4.2.3 Description of AHP method ...... 85

Chapter 5. Results of the Research ...... 91 5.1 Land cover change ...... 91 5.1.1 Image pre-processing ...... 91 5.1.2 Landsat images classification ...... 91 5.1.2.1 Selection of training samples for image classification ...... 92 5.1.2.2 Maximum-likelihood classification...... 94 5.1.3 Post classification processing ...... 94 5.1.4 Accuracy assessment ...... 95 5.1.5 Classification and change maps and statistics ...... 96 5.1.5.1 Classification ...... 96 5.1.5.2 Land use status in 1989 and 2013 ...... 97 5.1.2.3 Accuracy assessment ...... 98 5.1.2.4 Land Use/Land Cover change detection ...... 99 5.2 Land potential productivity assessment for agriculture use ...... 102 5.2.1 Determination of factors and variables development ...... 102 5.2.1.1 Chemical factor ...... 102 5.2.1.2 Physical factor ...... 104 5.2.1.3 Relative topography ...... 105 5.2.2 Determination of weights of main factors and variables ...... 106 5.2.3 Land potential productivity model ...... 108 5.2.4 Provisional land potential productivity assessment ...... 108 5.2.4.1 Assessment of chemical factor ...... 108

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5.2.4.2 Assessment of physical factor ...... 112 5.2.4.3 Assessment of relative topographic factor ...... 115 5.2.5 Final land potential productivity assessment ...... 117 5.3 Land suitability evaluation of the selected crops in the study area ...... 118 5.3.1 Selection of criteria for building land mapping units (LMU) ...... 119 5.3.2 Selection of the main agricultural crops ...... 123 5.3.3 Land requirement and determination classes for selected crops ...... 124 5.3.4 Calculation method ...... 128 5.3.4.1 Simple limitation method ...... 128 5.3.4.2 Square root method ...... 129 5.3.4.3 AHP method combining with multi-criteria approach ...... 130 5.3.5 Suitability of land area for paddy rice crop ...... 133 5.3.6 Suitability of land area for sweet potato crop ...... 134 5.3.7 Suitability of land area for groundnut crop ...... 135 5.3.8 Suitability of land area for maize crop ...... 135 5.3.9 Suitability of land area for potato crop ...... 141 5.3.10 Suitability of land area for sesame crop ...... 142 5.3.11 Suitability of land area for soybean crop ...... 147 5.3.12 Suitability of land area for green pepper ...... 148

Chapter 6. Discussion of the results ...... 159 6.1 Image classification and land cover change from 1989 to 2013 ...... 159 6.2 Land potential productivity assessment ...... 161 6.3 Land suitability evaluation for selected crops by using different methods ..... 164 6.4 Response to research questions ...... 166 6.5 Research limitations and consequences ...... 174

Chapter 7. Conclusion and Recommendation ...... 175 7.1 Conclusions ...... 177 7.2 Recommendations ...... 179 7.2.1 Recommendations for further researches ...... 179 7.2.2 Recommendations for soil improvement and crop development ...... 180

Acknowledgements ...... 183

References ...... 185

Appendixes ...... 205

Curriculum Vitae ...... 251

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List of Tables

Table 2.1: Major type of rural land use (after Young, 1976) ...... 8 Table 2.2: Example of some LUTs (MARD, 2009) ...... 9 Table 2.3: The structure of FAO framework for land evaluation (FAO, 1976) ...... 22 Table 2.4: Structure of land suitability classes (FAO, 1976) ...... 23 Table 2.5: Structure of land suitability classes and sub-classes (after Huizing, 1993) ...... 24 Table 2.6: Landsat mission dates ...... 38 Table 2.7: TM and ETM+ band designations ...... 38 Table 2.8: OLI and TIRS band designations ...... 39 Table 2.9: Fundamental scale used for the AHP (adaped from Saaty, 1977, 1990, 2008).. 48 Table 3.1: Soil classification in Quang Xuong District (Fertilizer, 2011 and field work) .. 55 Table 3.2: Fertility of soil group in Quang Xuong district ...... 60 Table 3.3: Population and labor force information in the study area ...... 61 Table 3.4: The average growth rate of period of 2008 - 2012 ...... 62 Table 3.5: The structure of economy in Quang Xuong in the period of 2008 - 2012 ...... 62 Table 3.6: Some agricultural development indicators in the period of 2008 – 2012 ...... 63 Table 3.7: Current status of land use of 2012 ...... 66 Table 3.8: Yield, area of main annual crops in Quang Xuong District (fieldwork and yearly data statistics office of Quang Xuong, 2012) ...... 70 Table 3.9: Hierarchical level of economic efficiency evaluation ...... 72 Table 3.10: Economic efficiency of some main annual crops ...... 73 Table 4.1: Characteristics of landsat5 TM, Landsat8 OLI and TIRS data ...... 75 Table 4.2: Land cover classification scheme ...... 78 Table 4.3: Main factors and their variables for land potential assessment ...... 80 Table 4.4: The level of land potential productivity ...... 80 Table 4.5: A schematic relation between the limitation and class level ...... 81 Table 4.6: Determine classes of land suitability for different land indices ...... 82 Table 4.7: Basic scale for pairwise comparisons ...... 86 Table 4.8: An example of pair-wise comparison matrix in AHP (M) ...... 87 Table 4.9: The average random index based on matrix size ...... 88 Table 4.10: The level class of land suitability evaluation ...... 89 Table 5.1: Result of land use/land cover classification for 1989 and 2013 ...... 98 Table 5.2: Accuracy assessment of the image classification of 1989 from Landsat TM data ...... 98

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Table 5.3: Accuracy assessment of the image classification of 2013 from Landsat OLI & TIRS data ...... 99 Table 5.4: The change of land use/land cover in the study area from 1989 to 2013 in ha 100 Table 5.5: Weight of each factor and variable ...... 107 Table 5.6: Score of each variable category for land potential productivity assessment ...... 107 Table 5.7: Distribution of OM in the study area ...... 109 Table 5.8: Distribution of CEC values in the study area ...... 109 Table 5.9: Distribution of soil pH values in the study area ...... 110 Table 5.10: Distribution of EC in the study area ...... 110 Table 5.11: Distribution of BS in the study area ...... 111 Table 5.12: Potential productivity level of chemical factor for annual cultivation ...... 111 Table 5.13: Distribution of cultivated land with different soil texture ...... 113 Table 5.14: Distribution of cultivated land with different soil texture ...... 113 Table 5.15: Distribution of cultivated land with different irrigated condition ...... 114 Table 5.16: The drainage capacity in the study area ...... 114 Table 5.17: Potential productivity level of physical factor for annual cultivation ...... 115 Table 5.18: Potential level of relative topographic factor for annual cultivation ...... 116 Table 5.19: Land potential productivity assessment for agricultural use ...... 117 Table 5.20: Decentralized level and its symbol of criteria for building mapping unit ...... 120 Table 5.21: Characteristics of land unit in the study area ...... 121 Table 5.22: Land characteristics for determination of suitable classes for growing selected annual crops ...... 125 Table 5.23: Name of evaluating main parameter, sub-criteria and sub-unit criteria ...... 130 Table 5.24: The value comparison of different experts for all criteria ...... 131 Table 5.25: Pairwise comparison matrix and calculating in level 1 ...... 132 Table 5.26: Suitability level area for growing paddy rice in Quang Xuong District ...... 133 Table 5.27: Suitability level area for growing sweat potato in Quang Xuong District ..... 134 Table 5.28: Suitability level area for groundnut in Quang Xuong District ...... 135 Table 5.29: Suitability level area for maize crop in Quang Xuong District ...... 135 Table 5.30: Suitability level area for potato crop in Quang Xuong District ...... 141 Table 5.31: Suitability level area for sesame in Quang Xuong District ...... 142 Table 5.32: Suitability level area for soybean in Quang Xuong District ...... 147 Table 5.33: Suitability level area for green pepper in Quang Xuong District ...... 148 Table 7.1: The advantage and disadvantage of three applied methods…………………..171

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List of Figures

Figure.2.1: Schematic representation of a cycling farming system ...... 10 Figure 2.2: Example of land utilization type and cropping pattern in Quang Xuong District ...... 13 Figure 2.3: Relationship between land, LMUs and land evaluation ...... 13 Figure 2.4: Land use sustainability ...... 16 Figure 2.5: Structure of land suitability classification ...... 25 Figure 2.6: Steps of land evaluation and land use planning ...... 25 Figure 2.7: Applications of land evaluation ...... 28 Figure 2.8: Top Landsat data uses from October 1, 2015 through March 31, 2016 ...... 40 Figure 2.9: Landsat 8 Spectral Bands and Wavelengths compared to Landsat 7 ETM+ .... 41 Figure 2.10: GIS application in agriculture ...... 42 Figure 2.11: An example of GIS layer ...... 44 Figure 3.1: Location and boundary of the study area ...... 50 Figure 3.2: Monthly mean temperatures of the study area ...... 51 Figure 3.3: Monthly mean precipitation distribution ...... 51 Figure 3.4: Relative humidity, sunny hour and evaporation ...... 52 Figure 3.5: Soil classification map of Quang Xuong district ...... 56 Figure 3.6: Sub-units of soil classification in Quang Xuong district ...... 57 Figure 3.7: The soil texture for sub-units of soil in the Quang Xuong District ...... 60 Figure 3.8: The transfer of economic structure in Quang Xuong 2008 – 2012 ...... 63 Figure 3.10: Structure of land use of 2012 in Quang Xuong District ...... 67 Figure 3.11: Structure of agro-forestry utilization in the study area ...... 68 Figure 3.12: Maize and paddy rice cultivation in the Quang Xuong ...... 71 Figure 4.1: Flowchart of the processing of satellite data ...... 78 Figure 4.2: The flowchart of land potential assessment for Agriculture use ...... 79 Figure 4.3: Simple limitation method ...... 82 Figure 4.4: Square root method ...... 82 Figure 4.5: General flowchart of Land suitability evaluation for specific selected crops ... 83 Figure 4.6: The main criteria, sub-criteria and sub-unit criteria in AHP structure...... 85 Figure 4.7: Aggregation of the rating and weight of each factor ...... 89 Figure 5.1: The geometric shape of the study area ...... 92 Figure 5.2: Three different False Colour Composite combinations ...... 93

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Figure 5.3: Supervised Maximum likelihood classification of 1989 and 2013 ...... 97 Figure 5.4: land use/ Land cover types within the study are from 1989 to 2013 ...... 100 Figure 5.5: The change of land use/land cover categories types in ha ...... 101 Figure 5.6: The change of land use/land cover from 1989 to 2013 ...... 101 Figure 5.7: Potential productivity classification for soil chemical factor ...... 112 Figure 5.8: Potential productivity classification for soil physical factor ...... 115 Figure 5.9: Potential productivity classification for relative topographic factor ...... 116 Figure 5.10: Assessment of Land potential productivity for agricultural use ...... 118 Figure 5.11: Land mapping unit of Quang xuong District ...... 122 Figure 5.12: Suitability map for paddy rice using different methods ...... 137 Figure 5.13: Suitability map for sweet potato using different methods ...... 137 Figure 5.14: Suitability map for groundnut using different methods ...... 143 Figure 5.15: Suitability map for maize using different methods...... 145 Figure 5.16: Suitability map for potato using different methods ...... 149 Figure 5.17: Suitability map for sesame using different methods ...... 151 Figure 5.18: Suitability map for soybean using different methods ...... 153 Figure 5.18: Suitability map for green pepper using different methods ...... 155

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List of Abbreviations

AHP : Analytical Hierarchy Processes ALES : Automatic Land Evaluation system AVHRR : The Advanced Very High Resolution Radiometer BS : Base saturation CEC : Cation exchange capacity cm : Centimeter CR : Consistency Ratio e.g. : Exampli gratia (for example) EC : Sum of changeable basic cation et al. : Et alia (and others) etc. : Et cetera (and so on) ETM+ : Thematic Mapper Plus FAO : Food and Agriculture Organization GDP : Gross Domestic Product GIS : Geographic Information System Go : Gross Output ha : Hectare(s) IE : Intermediate Expenditure Km2 : Square kilometers LIS : Land Information System LMU : Land Mapping Unit LUP : Land Use Planning LUT : Land Utilization Type MARD : Minister of Agriculture and Rural Development of Vietnam MCE : Multi-Criteria Evaluation meq : Milligrams equivalent mg : Milligram MI : Mixed Income mm : Millimeter MODIS : The Moderate Resolution Imaging Spectro-Radiometer N : Non-suitability

N1 : Current unsuitability

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N2 : Permanent unsuitability NASA : The National Aeronautics and Space Administration NASS : The National Agricultural Statistics Service NIAPP : National Institute of Agricultural Planning and Projection of Vietnam OLI : Operation Land Imager OM : Organic matter PCQX : People Committee of Quang Xuong District PCTH : People Committee of Thanh Hoa Province RS : Remote sensing S : Suitability

S1 : High suitability

S2 : Moderate suitability

S3 : Marginal suitability TE : Total Expenses THSO : Thanh Hoa Statistics Office TIRS : Thermal Infrared Sensor TM : Thematic Mapper ton : Tons UNDP : The United Nations Development Programme UNEP : The United Nations Environment Programme UNESCO : The United Nations Educational, Scientific and Cultural Organization USA : The United States of America USAID : The United States Agency for International Development USD : The United States Dollar USGS : The United States Geological Survey UTM : The Universal Transverse Mercator VA : Value Added VND : Vietnam Dongs VNGSO : Vietnamese General Statistics Office WGS : The World Geodetic System = : Equals to > : Greater than < : Less than % : Percentage 0C : Degree Celsius

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Introduction

Chapter 1. Introduction

1.1 Background

Since the beginning of the human civilization, human beings have used land resources to satisfy their needs. Farming has been civilized men‘s primary occupation and agriculture has currently turned into more specific forms which have been given lables such as commercial agriculture and organic agriculture. Land comprises of the physical environment, including climate, topography, soil, hydrology and vegetation which all influence the potential of the land use (FAO, 1976). The Land and Water Development Division of Food and Agriculture Organization (FAO) has made considerable progress in the last three decades in putting together, applying and disseminating the tools for assessment and utilization of available land resources potential for developing crops and farming. To figure out proper plans and strategies for land development, it is pivotal to thoughtfully examine how suitable the land is for various uses (Marsh and Macaulay, 2002). Establishing appropriate suitability indices is the key factor in analysis of suitability. Evaluation of how suitable the land is for a particular use generally provides information for planning of land use (Fresco et al., 1989).

Land evaluation was known as soil survey, it was traditionally based primarily on soil resource inventories (Nabarath, 2008). Land evaluation is now concerned with ―the process of land performance when used for specified purposes‖. It includes the implementation and understanding of fundamental surveys of climate, soils, vegetation and different sides of land in term of the requirements of option types of land use (FAO, 1985). One of the primary goals of land evaluation is to reduce expenses by foreseeing the inherent capacity of a certain land area in a long term in order to minimize the socio- economic and environmental costs (De la Rosa, 2000). Finding a suitable land area for a particular agricultural crop is the need of the present day agricultural system.

The dramatic growth of the world population has been leading to the increasing demand for food, and in order to meet the demand the farming communities have to improve productivity. On the other hand, land is so scarce that it is necessary to find areas which best suit for crop growing (Selassie et al., 2014). Therefore, it is essential to assess the potential and requirements of a certain land parcel for agricultural activities based on a scientific procedure of land evaluation (Rossiter, 1996). The extent of potential of land can be estimated by using different methods. Any measurement should rely on an assortment

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Introduction

of suppositions since land suitability examination needs the utilization of various kinds of information and data (e.g., soil, climate, land use, topography, etc.). Geographic Information System (GIS) provides a more versatile and powerful tools compared to traditional data processing systems, as it offers methods for taking huge volumes of various types of datasets and controlling and joining the datasets into new datasets which can be shown in the form of thematic maps (Marble et al., 1984, Foote and Lynch, 1996).

As in the aforementioned discussion, the integrated information platform is extensively useful, especially when it is used to assist decision-making towards agricultural activities (Ghaffari et al., 2000). Applying GIS to developing models for land suitability evaluation is highly effective for land evaluation and land use planning (Charuppat, 2002). GIS supports decision-makers or planners to examine a circumstance given and suggests a chance to create scenario relating to asset preparations of different administration alternatives. Its common tools play a significant role in land evaluation, land use or land suitability assessment using a few information sources such as land cover, geology, roads, climate, and satellite images (Liu et al., 2007, Giap et al., 2005, Saroinsong et al., 2007).

Recently, many of GIS-based land suitability analysis approaches have been developed for land suitability assessment such as Boolean overlay and modeling. Nevertheless, these methodologies do not have enough characterized mechanism for incorporating decision makers‘ preferences into the GIS procedures. A solution may be integrating GIS and Multi-Ceriteria Evaluation (MCE) approaches (Mustafa et al., 2011). One of the MCE methods is an Analytical Hierarchy Processes (AHP) that has been integrated into GIS-based suitability process to achieve the required weightings for various criteria. GIS-based AHP has been popularized and widely used in assessing land suitability in the world because of its capacity of integrating a large quantity of inhomogeneous data, even for a huge number of criteria (Kamau et al., 2015).

Vietnamese economy has changed significantly from the planned economy to the market enterprise system, leading to a rapid economic growth of 7.3% per year during the period of 1995-2005 (WB, 2008). The changing mechanism has driven changes in different sectors. Since 1986, Vietnam has exercised ―open door‖ policy, which allows importation and exportation with other countries as well as foreign direct investments, and the context of the Land Use Planning (LUP) has also been gradually changed accordingly. The contents of LUP have been varied to meet the needs of different land users. When land classification is based on how suitable a land is for a particular use, it is possible to satisfy the growing need for intensive cultivation without any environmental

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Introduction

damage. Nowadays, GIS and remote sensing (RS) techniques are widely used as an effective tool for land evaluation in Vietnam. However, the use of GIS and RS techniques is not yet popular in provincial and rural settings because only a few administrators and managers have recognized its strategic importance in planning. The shortage of digital data, data standardization, and coordination are other reasons why GIS are used in a small scale in this area in this region. The coastal districts of Thanh Hoa Province have land potential for further development in agriculture and forestry, but lands have not been properly evaluated. Therefore, evaluation of land in those districts is an urgent and essential issue. It is necessary to apply the methodology and approaches of FAO and GIS techniques to this area with modifications to suit local conditions. This study aims to use GIS to classify the suitability of land with the use of integrated information for land use planning to increase the benefits for agricultural activities. The spatial information resulted from this study may be used to assist proper land use based directly on its potential which not only helps to minimize environmental problems but also increases individual farmers‘ incomes through proper land use.

1.2 Problem analysis Natural land area per capita in Vietnam is low (3808 m2/person); land area for agricultural production is about 1.100 m2/person. The demand for land for socio-economic development is increasing. In addition, Vietnam has a coastline (about 3260 km), so the exploitation and use of coastal farmland may be faced with many risks due to the impact of both natural factors and artificial activities (VNGSO, 2012). The coastal areas are constantly under extraordinary weight of disasters. This region has been facing such threats as poverty, overexploitation, flooding, and salinity. It lacks of methods to protect and restore the quality of the soil after use. Like many developing countries, Vietnam is under the pressure of the increasing population and the demand for food. Thanh Hoa Province is a typical example of such pressure. The current land use systems are not sustainable as they contribute to the problems of land degradation with respect to the regularly growing population in developing nations; therefore, there is an expanding urgent need to match land types and land use in the most objective way in order to boost sustainable production and to satisfy the differing needs of society while moderating delicate biological system (FAO, 1993). At present, the agricultural land in Thanh Hoa Province is dealing with encroachment for expanding residential area, basic construction and other non-agricultural

3

Introduction

land purposes. The boundaries between diverse types of land resources and agricultural land are not regulated. The development of agricultural production implies that it has a high potential for poverty alleviation, rural development and environmental improvement. On the contrary, industrialization, abuse and over-exploitation and improper farming area practice have heightened debasement of land resources. The degradation of agricultural land resource can be a major risk for the existence and sustenance of the communities. The land itself and its soils are an essential prerequisite for agriculture and down streamed crops production. However, there has been only minor evaluation of land potential for a wide range of utilization and assessment concerning land potential has not incorporated current agricultural land use. In order to resolve these problems, it is necessary to update information and set up a database to find the best method for assessment of land potential productivity and its suitability for crop production. This work is useful for providing guidelines for effective planning of land use. Land suitability evaluation is a procedure of assessing the capability of land for different kinds of land use (Dent and Young, 1981). Using GIS may help gain the accuracy and efficiency of the assessment results on account of integrating multiple variables. Planning and decision-making tasks have widely applied GIS and realized that it is the most powerful tool capable of providing reliable information (Michalak, 1993). Consequently, GIS has wide application in evaluation of land potential. In this research, GIS was used to join various kinds of data from different sources in order to provide an evaluation of land potential productivity for agricultural use and the land suitability of the study area for selected crops. The land capability sites and their suitability derive from the stand requirements of single crops with regard to particular conditions of chemical and physical soil properties, topography, climate and socio- economy. These results are not only be used as a fundamental soil-database but they also play an important role in the use of suitable soil resources and sustainable land management. In addition, assessing land suitability in integrating GIS model can be a new approach in Thanh Hoa Province of Vietnam.

1.3 Research objectives 1.3.1 Main objective The purpose of this study is to enumerate existing types of main land use, land use systems, land capability and to prepare land inventory in order to classify the land suitability using GIS and to select the types of land that can best support selected crops in

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Introduction

Quang Xuong District in Thanh Hoa Province. The gathered information will be the scientific base for suggestions and recommendations for land users, planners, and decision- makers.

1.3.2 Specific objectives  To analyze the main types of agricultural land use status of coastal sandy land of Thanh Hoa Province, Vietnam.  To establish the database of land information system of the study area.  To identify and map the major land cover/land use types from different period of time by using remote sensing.  To develop land suitability evaluation model in the study area by applying GIS techniques.  To analyze of the environmental, socio-economical characteristics of land and infrastructure systems in the study area.  To assess land suitability by applying limitation method; parametric method and Multi-criteria analysis with GIS.  To map the land suitability for specific crops as a useful stage for Land Use Planning.  To propose some suggestions and recommendations for the land use planning of sustainable agriculture in the future, especially for land users, planners and decision-makers. 1.4 Hypotheses  Using remote sensing to classify digital images can show how land cover changed from different periods of time.  GIS tools can give a suitable set of techniques for the assessment of land capability for agricultural use and the assessment of land suitability for a particular crop within the study area.  Soils in the study area differ significantly in their properties and suitability. There are specific land characteristics which differentiate land quality into classes of different productivity for agricultural land use types.  Qualities of land that directly affect plant growth in the study area, are leading to highly suitable, moderately suitable, marginally suitable and not suitable land for given land utilization types.  Different methods which are used to assess land suitability may provide dissimilar results of land suitability class.

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Introduction

1.5 Research questions (1) What are the characteristics of land and land use condition in the coastal sandy area of Thanh Hoa Province? (2) What are the promising land use types in the existing land, climates, social - economic and infrastructural conditions in the study area? (3) Which roles do land information systems play in land evaluation? (4) How is physical land suitability assessment used for agricultural development? (5) What are the criteria for land evaluation and how does it classify in different suitability ratings? (6) Who are the main beneficiaries of present research?

1.6 Structure of the thesis This current dissertation consists of seven chapters. Chapter One presents the research background and an analysis of the research problem. It further schemes the objectives and the hypotheses of the research. At last, it gives an outline of the thesis organization. Chapter Two mentions the relevant concepts or a review of basic knowledge associated with land suitability assessment. It also gives a short introduction of the techniques which are used in this thesis. Chapter Three deals with a case study which describes a study of an area location, topography, geography, and natural resources conditions, land use situation socio-economic structure. Chapter Four lists and specifies the methods and approaches, and tools used in relation to collecting, analyzing data for this study. Chapter Five displays the results of land cover change. It also shows the map of land capability assessment for agricultural production by using Weighted Linear Combination Method and further deals with the chemical, physical soil, the infrastructure analysis to evaluate land suitability for eight selected agricultural crops and provides the maps of land suitability classification for each plant. Chapter Six gives a discussion on the results of the this study. The last chapter, chapter Seven, gives the conclusions of the research and makes recommendations for future research.

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State of the Art

Chapter 2. State of The Art

2.1 Definition and terminology of land evaluation for agriculture 2.1.1 Land, land cover and land use In land evaluation, land is defined as an area that is characterized by natural features, including its explotiability and width. Soil properties involve climate, soil, geological components below the surface, hydrology, animals, plants and the impacts of human activities in the past as well as in the present (FAO, 1976). Land can also be considered an area of the earth‘s surface with relatively stable properties or changes of natural cycle which can predict the biosphere above, within or below it such as the air, geological conditions, plants, animals, current and previous human activities that affect human use of land in the present and in the future (Beek, 1978b). General characteristics of soil in the land assessment research is its attributes that can be measured or estimated and soil scientists often choose some most important properties to consider in each specific condition in the study area (FAO, 1985). There is often confusion between the term ―land cover‖ and ―land use‖. Land cover is the observation of physical cover and biological cover of the earth‘s land. However, land use is the aggregate of arrangement, activities, and inputs that people attempt in a certain land cover types (FAO, 1997a, FAO and UNEP, 1999). Therefore, land use and land cover are firmly related. All the natural and man-made features that cover the earth‘s surface is land cover, whereas land use refers to the human activities related to a specific land unit in terms of use and management practices and impacts (FAO, 1997a).

What is situated at the surface of the earth is known as land cover of a certain area which contains physical material at the earth surface. Land use is a description of how people utilize land for socio-economic activities as for agriculture, commerce, residential use or recreation. . In general, a cetain land is used for multiple purposes (Cihlar and Jansen, 2001).

Land use in agriculture is typically the land used for farming activities. For example, a land is covered by woods, the land is for timber production. Similarly, where paddy rice is grown or for grazing, the land can be known as aricultural land. Therefore, according to land evaluation for irrigated agriculture (FAO, 1985), land use is a description of the land forming the most distinctive feature of land suitability evaluation as compared to other approaches. It is the fundamental for the primary that land suitability which can only be assessed with

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State of the Art respect to specified kinds of use.

The definition of land use demonstrates an immediate connection between land cover and the activities of individuals in their environment. As a rule, land cover does not coincide with land use. A land use class is to consist of deffirent land covers (Hrebicek and Pillmann, 2009).

2.1.2 Major types of land use, land utilization type and farming system  Major type of land use Land assessment includes relating land mapping units to specified types of land use which are relevant for the area under consideration. These types of land use may be comprehensively characterized as ―major types of land use‖ or described in more detail, as ―land use types‖ (Huizing, 1993). In general, a major type of land use is the main subdivision of rural land use, mostly on the basic of produce (such as annual crops, perennial crops, rice, pasture, forest, recreation, and wildlife) and partly on the technology that is employed as for irrigation and improved pastures. Major kinds of land use are normally considered in land evaluation investigations of a qualitative or observation nature. Table 2.1 shows 14 major types of rural land use. Table 2.1: Major type of rural land use (after Young, 1976)

1. Annual cropping 2. Perennial cropping Rained arable farming 3. Wetland rice cropping 4. Irrigated cropping Irrigated farming 5. Extensive grazing Grazing 6. (Semi-)intensive grazing 7. Commercial forestry 8. Community forestry Forestry 9. Environmental forestry 10. Recreation forestry 11. Tourism (recreation) 12. Wildlife conservation Other types of rural land use 13. Water conservation 14. Road construction

 Land utilization type A land utilization type (LUT) is a particular type of land regarding its diagnostic or key attributes. As key attributes, only those factors that are selected have marked influence on the productive capacity of the land (Huizing, 1993). A LUT is a sort of land use characterized in more detail, as indicated by an arrangement of technical specifications in a given physical, chemical and socio-economical setting (FAO, 1976, 1985, 1993). According to Vietnamese soil scientists, a LUT is described or defined according to

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State of the Art the detail level of the major type of land use. This is associated with crop, combined crop or crop system with management and specified methods in the technical environment and certain socio-economy. In other words, a LUT is delimited and described by the technical and socio-economic attributes such as crops, cultivation techniques, the volume of production, labor and production costs (MARD, 2009). Table 2.2 gives an example of some LUTs according to different map scales.

Table 2.2: Example of some LUTs (MARD, 2009)

Land Utilization Type 1/100.000 – 1/50.000 1/25.000 – 1/10.000 > 1/10.000 1. 3 rice 1. 3 rice 1. 3 rice 2. 2 rice 2. 2 rice 2. Winter rice + summer rice

3. 2 rice + one crop 3. One rice + one crop 3. Rice + fish 4. Annual crops 4. 2 crops + one rice 4. Annual crops 5. Perennial crops 5. Annual crops 5. Vegetables

 Farming system The concept of a farming system is widely used in English-speaking countries to refer to a farming system or agribusiness system. A farming system is a unique arrangement and reasonable stability of agribusiness that has been managed by famers according to the identified operations related to the physical, biological and socio- economic environment (Shaner et al., 1981). Sharma et al., (1991) claims that a farming system is an arrangement of agro-financial activities that are interrelated and connected with themselves in a specific agrarian setting. It is a mix of farm enterprises to which cultivating families allocate their resources in order to efficiently utilize the existing enterprises to increase the productivity and profitability of the farm. A farming system is usually composed individual farming systems with similar resources, enterprise patterns, household livelihoods and imperatives, for which improvements and intercessions are considered compatible (Dixon et al., 2001). According to Huizing (1993), a farming system is a complicated network of land, labor, capital goods and influences of the socio-economic environment, guided by a farm manager. During the economic and social analysis of land evaluation results, the farming system is the key in this system. An appropriate combination of farm enterprises is expressed through farming systems (cropping systems horticulture, livestock, fishery, forestry, and poultry) and the methods available to the farmer to increase them for benefit. It not only communicates sufficiently with environment without dislocating the ecological and socioeconomic

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State of the Art balance, but it also endeavors to meet the national objectives on the other (Jayanthi et al., 2002). The farming system is an essential circulatory system (organic resources – livestock – land – crops). Therefore, management decisions are identified with one component may influence the other. Organic resources

Green forage Feed nutrients Livestock Green- manure ―Weeds Mulch compost CompostCrops ‖ Off-take

Nutrients Water Livestock Land Manure products Compost

Figure.2.1: Schematic representation of a cycling farming system (Thornton and Herrero, 2001)

2.1.3 Land evaluation in relation to ecological factors The theory of landscape ecology has considered land as a carrier of ecosystems. Land evaluation, according to an ecological perspective, is derived from a sustainable development perspective (Zonneveld, 1995). Sustainable agriculture is a selective system, diversity and natural ecological balance. In which the natural and the socio-economic factors impact mutually with each other and co-existence, bring high economic efficiency, fresh environment, safe products and market acceptance (Phien and Siem, 1993).

Land cannot be studied separately. Separation of soil characteristics out of the ecosystem may lead to superficial prospect and mistaken decisions of land use. According to the World Health Organization (2005), a decision or a specific activity of man in land use needs to take into account its effect on the whole ecosystem such as soil erosion, land degradation, floods, lower the groundwater level, and salinity. Land evaluation generally involves the entire ecosystem. Thus, the ecological conditions should be included in the qualitative or quantitative standard hierarchies. In the mapping systems for land evaluation, besides the maps reflecting the characteristics of the land, it is significant to build different kinds of maps as map of climate, topography, irrigation, and vegetation. Furthermore, land assessment also takes into account economic factors that relate to the LUT and the social impacts on the human. Understanding of land combination and interaction effects of internal exchange relationship when the balance is

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State of the Art broken by the development or disturbance due to the change of surrounding environment are strongly essential for successful land assessment (FAO, 1976). Land evaluation has to be considered on an extremely wide range, including space, time and the elements of nature and society. It is not only a requirement of the natural sciences but it is also related to the fields of economics and technology. Therefore, it is significant to involve experts of different majors in land evaluation (Bong et al., 1995). Land evaluation is the assessment of suitable land for man use in agriculture, forestry, irrigation design and land use planning (Beek, 1978a). In other words, land evaluation aims to provide information about the advantages and disadvantages for the use of land, as a basis for making decisions about the use and management (Khang and Tan, 1995).

2.1.4 Land characteristics and land qualities A land characteristic is a land attribute that can be measured or evaluated in routine surveys in any operational sense, including remote sensing and census as well as by natural resource inventory. It can be used to identify between land units of varying suitability for use and utilized as a method of describing land qualities. Examples are mean annual rainfall, pH and soil nitrogen percentage, soil texture, available water capacity, and mean annual rainfall (FAO, 1976, 1983). When land attributes are utilized specifically in assessment, problems arise from the interaction between characteristics. Besides land characteristics, soil attributes are strongly important in procedure of land suitability evaluation for agriculture. A soil characteristic can be comprehended as a simple attribute of soil and it will directly affect the soil quality. Larson and Pierce (1991), Gregorich et al., (1994) and Papendick et al., (1994) states that a minimum data set of soil characteristics that represent soil quality, defined by physical, chemical and biological characteristics of soil, has to be chosen and measured. The physical, chemical and biological characteristics of soil imply a minimum data set (Kavetskiy et al., 2003). A land quality refers to a comflex attribute of land which distinctively has positive or negative impacts on the suitability of land use. Instances of land qualities widely applied in agriculture are climate conditions, moisture availability, soil drainage, soil depth, potential for mechanization, and erosion hazard (FAO, 1984, 1985). The land ability must perform specific requirements for a LUT. There are a huge number of land qualities, only those applicable to land use alternatives under consideration need be determined. The value of a land quality is not fixed. It may change over time to create a resulting in a time- series of severity levels of land quality (Rossiter, 1996). Generally, land quality is not usually estimated or measured directly, and so must be described by a set of diagnostic

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State of the Art land characteristics. Moreover, the soil quality is also a substantial factor to be concerned for crop growing (Nabarath, 2008). One part of land is soil and soil quality indeed is a subset of land quality. Soil quality plays an important part in how well soil functions in maintaining the diverse ecosystem and productivity, partitioning water and solute flow, filtering and buffering, nutrient cycling, and providing support for plants and other structures (Potts et al., 2010). Van Bruggen and Semenov (2000) defined soil quality as, ―The capacity of a specific kind of soil to function within natural or managed ecosystem boundaries, to sustain biological productivity, maintain environmental quality, and promote plant and animal health‖. Estimates of soil quality possibly reflect the status of soil as a vital resource (Doran and Zeiss, 2000).

2.1.5 Cropping system and ecological requirements of crop Cropping system is not a new term, but it still has been used more often in recent years in discussions about sustainability of agricultural production systems. According to FAO (1976) the cropping system is a subsystem of a farming system. The cropping system refers to cultivated crops or crop sequences in interaction with farm resources, farm enterprises and management techniques applied in a particular year or over many years (Hall et al., 1992). In other words, a cropping system usually indicates a combination of crops in time and space. A combination in time occurs when crops occupy different growing periods and a combination in space take place when crops are inter-planted. Land is always regarded as the most important factor to support a specific crop, where the selection of a crop to be planted is made as the minimum input is applied. The better suitability of land is, the less input is needed. Otherwise, where less suitable land requires a high input for production so that output costs are affected heavily (Nabarath, 2008). The agronomical and physiological needs of specific crops affect the input elements of the cropping. Different crops have different ecological requirements for an appropriate development and yield. Reasonable moisture, nutrient supply and sufficient rooting depth are essential for photosynthesis and biomass production to maximize plant growth (Verheye and H., 1996). According to Finke et al., (1998), the requirements of the LUT or crop indicate the set of land qualities that determine the production and management conditions of a kind of land use. Figure 2.2 is an example to illustrate the cropping system in the study area.

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State of the Art

Land utilization type (LUT) Cropping pattern

2rice + one crop Sumer rice + winter rice + corn Cropping

system 3 crops Soybean + corn + groundnut

Figure 2.2: Example of land utilization type and cropping pattern in Quang Xuong District

2.1.6 Land Mapping Unit (LMU) In the process of land evaluation, land unit is a specific parcel that is shown on the map. It has enough characteristics of the land quality to make up the difference from other land units in order to ensure their suitability for all kinds of different land uses (FAO, 1976, 1985). Land resource mapping is done by combining data on climate, soil relief, hydrology and vegetation. Thus, LMU is defined and mapped by natural resource surveys and it is never entirely homogeneous because of the natural variability of land characteristics. The complexity of the terrain and the scale of the map may directly affect how homogeneity of LMU is. In fact, GIS is widely applied in order to overlay relevant data set to make LMU (George, 2005). One of the simple methods to examnine land units‘ features is direct observation from the field or remote sensing technique (Nabarath, 2008). Each land unit has its own properties, but they are connected very closely to the requirements of a land and the natural conditions of the LUTs to determine the suitability level of the land for each LUT. A mapping unit may be single or compound. Single LMUs consist of only one type of land, while compound LMUs are associations or complexes of more than one type of land. The components of compound units may have different suitability levels. On the contrary, different LMUs may offer comparative possibility for a specific type of land use (Huizing, 1993).

Land

Unit 7 Unit 6 Unit 5 Unit 4 Unit 3 Unit 2 Unit 1 (Compound)

Suitable S1 + S2 Ne S3n Suitable S1

Figure 2.3: Relationship between land, LMUs and land evaluation (Huizing, 1993)

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State of the Art

2.1.7 Land Use Planning (LUP) Planning requires appropriate decision-making methods that help to transform a present condition to a more acceptable one by distributing available resources to reduce costs and increase benefits under a dynamic social quilibrium (Rodríguez Parisca, 1995). Land Use Planning has been understood as a professional term to mean used by both state and private sectors. According to FAO (1993) "Land use planning is the systematic assessment of land and water potential, alternatives for land use and economic and social conditions in order to select and adopt the best land use option. Its purpose is to select and put into practice those land uses that will best meet people‘s needs while safeguarding resources for the future. The driving force in planning is the need for change, the need for development of management or the need for a quite different pattern of land use dictated by changing circumstances". Dent and Young (1981) define LUP as a method to help decision-makers in utilizing land by systematically assessing land and alternative patterns of land use, choosing the use which meets determined objectives and drawing up of strategies and projects for the use of land. LUP tries to choose the best utilization of resources through diagnosing land use problems, creating suitable choices and observing the effectuation of demonstrated options (Dent, 1988). It is known as the best use of land in perspective of acknowledged objectives, ecological and social opportunities and constraints (FAO, 1989). The primary target of LUP is to sort out the uses that best reach specific objectives for different tracts of land and the conceptualization of projects, programs or management plans to apply these uses. This is also crucial when authorities and land users realize a need for land use change (Ojeda-Trejo, 1997). This demands the political will and the ability (instruments, budgets, and manpower) to assist and implement the plan. In addition, any change to land use mut be approved by all the stakeholders involved (FAO, 1993). LUP is one of the most important solutions to sustainable development at both national and regional levels. Based on different development scenarios, land users can use land resources to improve their income and achieve their goals (Crowley et al., 1975). It contributes greatly on economic development in the future; that is, it supplies land and space for such development. Industrialization and urbanization are not two major driving forces contributing to land-use change but they are also related directly to the LUP. Associated with industrialization and urbanization, the transition of land resources from agricultural land to non-agricultural land is continuously increasing (Long et al., 2007). The widespread land degradation and disorderly development causing the loss of the best quality farmland to non-agricultural use are caused by shortage of a proper land

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State of the Art use policy. In developing countries, the transformation of agricultural land use to non- agricultural use such as buildings, roads, public constructions is obviously increasing over years. However, it is usually difficult to collect data of converting agricultural land to urban land (Dushaj et al., 2009). Making significant decisions on land use and the environmental protection is one of the main goals of LUP. Various data from multiple, interrelated sources and types are input and a great deal of information must be considered simultaneously for holistic planning. An indispensable component in the planning process is physical and chemical soil information to reflect directly upon land suitability (Coleman and Galbraith, 2000). Socio-economic needs and spatial analyses of elements, including topographic shapes, vegetation characteristics, soil and water resources are calculated in the process of land use planning (Miryaghoubzadeh and Shahedi, 2012). The selection of the preferred land use for improving sustainable use of land and management of resources is an important activity in LUP (Huizing and Bronsveld, 1994). A suitable land management not only decreases soil limitations but it also increases agricultural yield. Land resources are limited, so that land evaluation is an important stage in the land use planning (Mostafa, 2004). FAO (1990) postulates that LUP plays an essential role in deciding the best model of sustainable land use.

2.1.8 Land sustainability and land suitability  Land sustainability The definition of the term sustainable developemnt is a controversial topic (Saunders and Becker, 2015, Kaphengst, 2014). The most widely accepted definition is ―sustainable development is development that meets the present needs without compromising the ability of future generations to meet their own needs.‖ (Brundtland Commission, 1987). The Hyogo Framework for Action futher explains that ―The sustainability of development depends on its ability to prevent new risk creation and the reduction of existing risk‖ (Committee, 2014) One of the most important issues in land use system is sustainability. Sachs (1992) believes that there are five main factors, namely economic, social, spatial, cultural and ecological, which impact sustainable development. It is crucial to consider the extent to which a land is used to meet the requirements of productivity, security, protection, viability and acceptability into the future (Nabarath, 2008). Sustainability is a complex concept for applying to land use because of posing many significant challenges, which already appear in a particular sector such as agriculture (Kaphengst, 2014). Sustainability in an agricultural system is evolving human needs

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State of the Art without devastating and, if possible, by improving the natural resource base on which it depend (USAID, 1988).  Land suitability To optimize land use, the evaluation of land suitability should be based on the rational cropping system (FAO, 1976, Sys et al., 1991). The suitability is land characteristics and a function of crop requirement. It requires examination of how land quality will meet the requirement of a specified kind of land use (FAO, 1976). Land suitability is the capacity of a portion of land to endure the production of crops in a feasible manner. Land Suitability is the degree of appropriateness of land for a certain use and could be assessed for present condition or after improvement (Ritung et al., 2007). An essential part of land suitability potential evaluation is finding out the environmental limit which impacts sustainable land use planning and confronts the assessment of land performances for a specific use (Bandyopadhyay et al., 2009). Identifying the main restrictions of a certain crop production and allowing decision-makers to develop a crop management system for increasing the land productivity are an important aim of the land analysis (Halder, 2013). A relationship between sustainability and suitability, stability, land degradation, and land use are indicated by the definition of sustainability (Huizing et al., 1995). It implies that land suitability assessment should take accounts of the risks of soil and other types of soil degradation (FAO, 1983). Land suitability is an essential part of sustainability evaluation of land use. The sustainability is defined by suitability together with vulnerability. The sustainable land use should be at the highest level of suitability and lowest level of vulnerability (De la Rosa, 2000). Figure 2.4 shows the relationship between land suitability, vulnerability and sustainability.

Suitability Vulnerability

Sustainability

Figure 1 Figure 2.4: Land use sustainability (Adapted from De la Rosa, 2000)

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State of the Art

2.2 Theory and practices of land suitability evaluation 2.2.1 Basic concepts of land suitability evaluation Land evaluation is to examine land performance and potential for a specific production purpose, which includes the execution and interpretation of data collected from staudies of landforms, soild, climate, vegetation and other aspects of land in order to figure out the promise for future land use (FAO, 1976). The principle intention of land evaluation is the anticipation of the capability and limitations of land for changing use. The introduction of the fully new land or a new management practice may be involved in the process of land suitability evaluation (Dent and Young, 1981). Rossiter (1995) supposes that the modern era of land evaluation appeared when ―Framework for Land Evaluation‖ was published in 1976 and the subsequent generation guidelines for land evaluation of general kinds of land use (FAO, 1983, 1984, 1985, 1991). Suitability of land evaluation is an integral part of land use planning (FAO, 1976, 1983). The study of land resources does not only end at the statistical quality and quantity of land, but also assesses the ability of land suitability for proposing suitable use and agricultural development in sustainable manner and economic, social, environmental factors are careful considered in the assessment of land suitability (FAO, 1992, 1996). However, land suitability is assessed and classified with respect to specified kinds of use as contradicted to a single scale of ‗goodness‘ of land (Rossiter, 1994). In a broad sense, the aim of land suitability assessment is to identify the most appropriate spatial pattern for future land uses associated with specified requirements, preferences, or predictors of some activity (Hopkins, 1977, Collins et al., 2001) In terms of physical potential, land suitability evaluation tries to find the best places of the land that suit a given range of utilization types, which may be included agricultural uses or nature preservation alternatives as well. The procedure hereby in use is based on the crop requirements for growth and the environmental conditions (Verheye, 1987, Beek, 1978b). Evaluation of land suitability is analying the criteria from deffirent land resources and socio-economic conditions (Prakash, 2003).. According to Austin and Basinski, land suitability evaluation is the assessment or prediction of land quality for a specific use, within the conditions of its productivity, degradation hazards and management requirements (Austin and Basinski, 1978). Land suitability evaluation is not only the process of estimation and combination of specific patterns of land in terms of their suitability for defined uses, but also carried out separately for each category of land use (Gong et al., 2012). In general, land suitability evaluation can

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State of the Art help decision-makers and land use-planners figure out the questions ―the best site of land use‖ to apply under certain conditions (Rabia and Terribile, 2013). According to Vargahan and Hajrasouli (2011), land suitability evaluation is the investigation of a particular area of land in order to satisfy an appropriate type of land use. Many factors are involved in this process and they may directly or indirectly control the ability of a certain part of land to host the land use. The result of land suitability assessment and generating suitable maps for different kinds of land use will facilitate to reach sustainable agriculture.

2.2.2 Land evaluation and FAO Framework for land evaluation Many views and different frameworks of land evaluation have launched to guide assessment of land suitability all around the world such as the guidelines of land evaluation of Russia, USA, England, Canada, India and Africa. Depending on the purposes and the specific conditions, each country sets out the content, methods of assessment, and classification of land resources for their own. Generally it is important to follow two tendencies known as assessment in terms of nature in order to determine the potential and the appropriate level of land with the specific purpose and assessment of land in terms of economic efficiency on a certain type of land use (Giang, 2012). In 1976 the framework for land evaluation (FAO, 1976) was established to unify the worldwide land evaluation standards and provided a new conceptual approach and methodological orientation to evaluation of land suitability (Mostafa, 2004). Subsequently, the guidelines for rain fed agriculture (FAO, 1983), forestry (FAO, 1984), irrigated agriculture (FAO, 1985), extensive grazing (FAO, 1991) and farming system analysis for land use planning (FAO, 1992) were publicized to evaluate general kinds of land use. The framework for land evaluation of 1976 became one of the most acceptable and widely applied in evaluation of land resources, especially in developing countries such as Nigeria (Hill, 1978), Malaysia (Biot et al., 1984), Sri Lanka (Dent and Ridgway, 1986), Bangladesh (Brammer et al., 1988), Jamaica (FAO and UNEP, 1994b), Indonesia (FAO and UNEP, 1994a), Kenya (Fischer, 1994) and Thailand (Shrestha and Eiumnoh, 1995). The FAO Framework for land evaluation (1976) was designed to offer tools for the formulation of each concrete evaluation. The system is based on the following concepts: (1) quality of the land, not only the soil; (2) suitability of land use for a specific purpose; (3) the physical conditions and economical ones is needed to take into account in land evaluation; (4) the concepts of land evaluation is essentially economic, social and political; (5) comparison of two or more alternative land uses; (6) proposal of suitable land use; and (7) a multidisciplinary approach (Van Diepen et al., 1991).

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The evaluation criteria for sustainable land use by FAO (1976) as follows:  Economic efficiency - Production value (production * price) - Total variable costs (annual basic investment) - Mixed income - The effectiveness of capital - The value of workdays  Social impact - Employment (employees/ha/year) - The accepted ability of workers (attracted labor) - The accepted ability of market - Social Stratification (rich-poor division, the ability of investment and debt capital) - The social conflicts and environmental problems (bring high economic efficiency first, but damage to the environment in long-term, etc.).  Environmental ecology - From the point of the ecosystem (artificiality or nature, high biological productivity or low, simplicity or difficulty to change, etc.) - Environmental impact (concentrations of hazardous waste in wastewater, nutritional content and hazardous waste in different depth of soil layers, with or without the possibility of epidemics in production). - Other natural conditions (natural change of land surface, etc.). - Impacts on human health (ability to generate toxic substances to human health).

The level of material inputs in the evaluation are land improvements such as soil conservation or drainage and their total impact. It is recommended to avoid causing erosion from land use to preserve land for long-term production and improve productivity by introducing alternative crops and new land management schemes, which can help predict the successive crop yields or outputs (FAO, 1986). Primarily, land evaluation is the analysis of all data about the land such as soil fertilizer, climate, and vegetation in terms of realistic alternatives for improving the use of that land. This should concentrate on the land itself, its attributes, functions, and potential (FAO, 2007). Nevertheless, many land use decisions are made based on the basis of socio-economic aspects; hence, the main objective of land evaluation is to collect the positive land use for each type of land, taking into account both physical and socio- economic conditions and the conservation of ecological resources for future (Rossiter, 2001). Therefore, land evaluation may be supported by many subjects. It may be used for

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State of the Art various purposes, ranging from land use planning to the potential exploitation for specific land uses, especially agricultural land use or the motivation for improved agricultural land management. The framework for land evaluation defines concepts such as land, land mapping unit, major kind of land use, land utilization type, multiple and compound land use, land characteristics, land qualities, diagnostic criteria, land use requirements, limitations, land suitability, land suitability order, class, subclass, unit and potential suitability classification. The majority of standards and concepts of the 1976 framework still stay legitimate. The existing framework is reached out with financial strategies developed in the diagnosis and outline system (FAO, 2007). The framework permits comparing or matching the requirements of each potential crop with the characteristics and qualities of a certain kind of land (FAO, 1983). It also sets out fundamental concepts, standards and methodology for an efficient biophysical and financial assessment of the potentials for specific land uses likely to be relevant to the area. The art of land evaluation is to classify the proposed strategies of land use as a wise or unwise utilization of land which may depend on the predication of the most essential changes, to decide whether these are desirable or acceptable (FAO, 1980). Generally, final evaluation of the suitability of using an area for a particular purpose is based on independent land qualities, which may each limit the land-use potential (Rossiter and Van Wambeke, 1997). Physical assessment and economical assessment have been analyzed in the process of land evaluation. In physical evaluation, the physical factors are needed to consider whether a land use can be implemented on a certain area, the nature and severity of physical limitations or hazards. In economic evaluation, the economic measurement of benefits of specific land utilization is calculated. Thus, land evaluation may be conducted in either physical or economic terms. In the original framework, physical evaluations are referred to as qualitative evaluations. Quantitative research methods demand more-or-less detailed models of land performance, and these models usually require sufficient data (Punithavathi, 2012). Many recent studies attempting to compare quantitative and qualitative assessments of land qualities have been conducted by using the qualitative models to identify if the areas are worth further investigating, and which areas can be rejected as unsuitable without detailed study (Van Lanen et al., 1992b, Van Lanen et al., 1992a) Land evaluation can be improved in several waysla (Ojeda-Trejo, 1997). First of all, the preferences and constraints of local users need to be taken into account by involving

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State of the Art them in the planning project. That may include both the assessment of the impact of interventions from market as well as the economic, social and environmental outputs of the land use plan implementation. In addition, the process is worth maintaining and integrating existing data like remote sened data and field data by using more flexible data processing methods (Neameh, 2003). Finally, presentation of land evaluation and land use plans should be done in non-technical terms (Bronsveld et al., 1994).

2.3.3 The need for land evaluation Nowadays, agricultural land has been a result of the pressure from population development of the world, the increasing demand for food and the expanding construction land for urbanization. As a land is a limited resource, reliable and accurate land evaluation is indispensable for assisting decision-makers and land users to use the scarce land resources efficiently and develop models to predict the land suitability for different types of agriculture (Maddahi et al., 2014). Each country‘s decision on how to use its land depends on the synthetic factors which are closely related to each other, including soil characteristics, socio-economics, administration, political restrictions and the human‘s needs (FAO, 1985). Verheye (2008) states that competition for land is created on account of the improving demand for food and space from growing population while the best land suitability is short and marginal and less suitable areas are gradually taken into cultivation. In addition, the pollution of soil and ground water is results of the industrialization from the intensification of agriculture and a high use of agro-chemichy. Agriculture is often viewed as an environmental problem; however, if carefully managed, it can also become an integral part of the environment. According to Dent and Young (1981), there is nothing new about the basic features of land evaluation, since far back in the past that land users decided to plant what crops were the best for those kinds of their land. Furthermore, in the process of settlement, the land users knew where land was suitable for certain crops that they wanted to grow or the seasons were suited for their crops. This knowledge was formed though the experience that handed down from many generations as well as from the failures. Land evaluation is an important part of land use planning, a basic platform for proposing the right decision of land use (Quy, 2001). The purpose of land assessment is providing information about the opportunities and constraints for the use of land as a basic for making on its use and management (Sys et al., 1993). In the process of land evaluation, a scientifically standardized technique is used to estimate the characteristics of land

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State of the Art resources for certain uses and its results can be used as a guidance for land users and planners to find out a better use (Ritung et al., 2007). The need for optimum use of land implies that a new equilibrium has to be achieved between human factors, socio-economic conditions and environmental elements. The change of any equilibrium is always difficult so that the realization of development programs is strongly complex and requires more capital inputs (Sys et al., 1991). In the aforementioned discussion, it becomes clear that decision making of land use is a political activity with major social, economic and environmental impacts. Therefore, land evaluation techniques need to be developed to recognize potential alternative land uses.

2.3.4 The system of land suitability evaluation The suitability of a given piece of land is its potential to support a defined purpose. Land suitability analysis process includes evaluation and grouping of selected areas of land in term of their suitability for a specific use (Duc, 2006). According to Neameh (2003), factor analysis and limitations of land should be used to determine if its use is suitable. The structure of FAO framework for land evaluation is presented in Table 2.3.

Table 2.3: The structure of FAO framework for land evaluation (FAO, 1976)

SN Categories Description 1 Land Suitability Orders Reflecting kinds of suitability 2 Land Suitability Classes Reflecting degrees of suitability within Orders Reflecting kinds of limitation or main kinds of 3 Land Suitability Subclasses improvement measures required, within Classes Reflecting minor differences in required management 4 Land Suitability Units within Subclasses

According to the guideline framework by FAO (1976) for land evaluation, the land suitability comprises of assessing and grouping the land types in orders and classes depending on their capacity. Land suitability orders imply whether land is evaluated as suitable or not suitable for use under consideration. The letter ―S‖ stands for suitability and the letter ―N‖ stands for unsuitability. Land suitability classes reflect the degree of suitability and unsuitability, presented in Table 2.4. Generally, three classes are recognized within the order suitability (S1, S2, S3) and within the order of unsuitability, there are normally two classes (N1, N2). The area may be classified as unsuitable for a certain use for a number of reasons (Nabarath, 2008). It may be an impracticable technique, such as the soil texture of sand land for growing paddy rice, or that it would be damage for

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State of the Art environment liked degradation and soil erosion. The areas which were not assessed are grouped as an extra class "NR" meaning not relevant.

Table 2.4: Structure of land suitability classes (FAO, 1976)

Order Class Description Land having no, or only minor limitations that

S1 (highly suitable) will not significantly reduce productivity and will not raise inputs to a given use Suitable Land having minor limitations which in

(S) S2 (Moderately suitable) aggregate are moderate severe for sustained application of a given use Land having limitations which in aggregate are S3 (Marginally suitable) severe for sustained application of a given use Land having severe limitations that preclude the

N1 (Currently not suitable) given type of use, but can be improved by None- specific management suitable Land with so severe limitations which are (N) N2 (Permanently not suitable) impossible for successful use of the land in the given manner or very difficult to be overcome

Land suitability can be classified as highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and not suitable (N). These levels are being used by many researchers for different crops such as Pyrethrum flower production, in Kenya (Wandahwa and Van Ranst, 1996); crop-land suitability analysis (Ahamed et al., 2000, Kamau et al., 2015); land suitability evaluation for sugar care in Thailand (Paiboonsak et al., 2004); Robusta coffee in the Dak Gan, Vietnam (D‘haeze et al., 2005); qualitative land suitability evaluation for the growth of onion, potato, maize, and alfalfa, wheat, barley, safflower (Jafarzadeh and Abbasi, 2006, Jafarzadeh et al., 2008); for principal crops in the West Shoush Plain, Iran (Albaji et al., 2009); cropping system in a watershed, India (Martin and Saha, 2009); for Rabi and Kharif crops (Mustafa et al., 2011); for cereal crops in Yigossa Watershed, and Northwestern Ethiopia (Selassie et al., 2014). Land suitability classes reflect the level of suitability. The classes are marked with continuous Arabic numbers in sequence of decreasing degrees of suitability within an order (Nabarath, 2008). However, it is highly recommended that they should be kept to the minimum necessity to meet interpretative aims and a one-to-five scale should be preferably used (Sekar, 2016).

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Land suitability subclasses reflect kinds of limitations like moisture deficiency and erosion hazard. The lower-case letters with mnemonic significance such as S2m, S2e, and S3me imply subclasses. There are no subclasses in class S1. The subclasses recognized number and the limitations chosen will depend on the purpose of classification (Sekar, 2016). Limitation factors as indicated in FAO (1976) have been used by several authors like Chuong (2008a) , Behzadad et al., (2009), Malekian and Jafar zadeh (2011) etc. Such and limitation factors are indicated by small alphabet letters as follows. c: Limitations by climate, d: Limitations by root able soil depth, f: Limitations by flooding in rainy season, n, s; Limitation by salinity and alkalinity, t: Limitation by physical properties of soil and topography, w: Limitation by drainage. A subclass can be divided into subdivisions commonly called land suitability units which have the same degree of suitability and similar types of limitations at the subclass level. These units are usually labeled with a hyphen prior to Arabic numbers, such as S2e-1 and S2e-2. The difference between units is in their production features and management requirements. Their identification allows for detailed explanations at the farm planning level (Sekar, 2016). By identifying land suitability units, detail management or farm planning suggestions can be made for individual fields or farms. Structure of land suitability classes and sub-classes and structure of land suitability classification are shown in Table 2.5 and figure 2.5.

Table 2.5: Structure of land suitability classes and sub-classes (after Huizing, 1993)

Land suitability Land suitability classes Land suitability subclasses orders

S1 (highly suitable) e.g. S2n, S3me or S3sl; Possible codes of

S S2 (Moderately suitable) limitation:

S3 (marginally suitable) m: moisture availability n: nutrient availability N1 (currently unsuitable) N e: resistance to erosion N (Permanently unsuitable 2 sl: soil slope

Based on FAO framework (1976), two types of the proposed classification are current suitability and potential suitability. 1. Current suitability refers to the suitability for a defined use in the present condition, without major improvements.

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2. Potential suitability refers to a defined future use of land units in their conditions after specified major improvements have been completed where necessary.

ORDER CLASS SUBCLASS UNIT

S1 (highly suitable) S2m S2m-1 S (suitable) S2 (moderately suitable) S2e S2m-2 (suitable) S3 (marginally suitable) S3me etc N1m N1 (currently not suitable) N1e N (unsuitable) N2c N2 (Permanently not suitable) etc Figure 2.5: Structure of land suitability classification (FAO, 1976, 1983)

As mentioned above, the major purpose of land evaluation is predicting land performance over time for specific types of uses (Rossiter, 1996). Land evaluation is a concept that describes the interpretation process of the principal inventories belonging to soil characteristics, vegetation cover, environmental conditions, climate status and other aspects associated with the land to find out the best land use among its alternatives (Sys, 1979). From an agricultural view, the biggest difficulty for land use planning is to sustain biophysical land properties and diverse agricultural land use at the same time. In order to avoid deterioration of the environment, the sustainable land use system needs to be defined. To increase agricultural production, all the possible options including (1) expansion the surface of agricultural land; (2) introduction of irrigation techniques; (3) use of fertilizers and pesticides; (4) improvement of management practices; finally (5) use of improved crop varieties must be based on the evaluation system consequences (Mostafa, 2004).

Identifying land use

Identifying Identifying Defining Collecting Assessing environment the most Planning Application objectives the data suitability and social- suitable land use of land economic land sue evaluation issues Identifying land units

Figure 2.6: Steps of land evaluation and land use planning (FAO, 1986)

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Land use planning can help decision-makers and land users to use land in a way that (1) reduces the current land use problems and (2) satisfies the specific socio-economic or environmental issues such as sustainability, increases income, and enhances food self- sufficiency or environmental conservation (Baniya, 2008). Land evaluation is an important part of the process of land use planning and its results should be applied in rational land use planning (FAO, 1993). Many scientists and research organizations in the world have carried out land evaluation and applied its results for land use planning (Minh et al., 2003). The steps of land suitability evaluation are presented in the Figure 2.6. Land evaluation has been recommended on the land utilization types that best fit the land unit in a particular area and its results must be applied in land use planning. The planners and land users have to determine where and when the plan of land use can be best implemented, and match the requirements of socio-economic and environmental sustainability of the communities. A successful land use planning should be evaluated within the huge region and the country including the reasonably economic and technical solutions according to the patterned proposal (Quy, 2001). Land evaluation reveals the ability of a portion of land to tolerate to the production of crops in a sustainable way. The consequences not only allow describing the major limitation factors of production of a particular crop, but also permit decision-makers to create a crop management system for increasing the land productivity. In addition, Land evaluation is important as it helps achieve optimum utilization of available land resources for sustainable agricultural production ( FAO, 1976). Traditional land assessment has been mainly based on soil resource inventories, normally called soil surveys (Bacic, 2003). This has been implemented in Russia, the USA, and Hungary for more than a century and in most other parts of the world for at least fifty years (Boulaine, 1989, Zinck, 1992, Yaalon and Berkowicz, 1997). Initially, they were supported for rural land use decision making together with the matching of production systems to soil types and soon became systematized in land capability approach (Douglas et al., 1982), the authors stated that soil types were classified by their capacity to manage general classes of land use. In the early decade of 1970s, the existing land classification system was not satisfied to support rational land use planning in three main aspects as their ability (Rossiter, 1994) as: (1) Existing land classification systems were only focused on physical factors and omitted socio-economic aspects of land use.

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State of the Art

(2) The systems did not mention enough information of land use for realistic evaluation, in other words, a single classification was applied to land uses with different requirements. (3) They were not able to be applied outside of their land of calibration. The original land evaluation approaches were developed to estimate the potential of a land given for general kinds of use. In addition, a specific kind of use must be evaluated for essential development of a current land that has led to the publishing conceptual framework on land suitability by FAO (1976), which has been internationally accepted in land evaluation analyses (De la Rosa et al., 2002). The evaluation of land is commonly interdisciplinary which takes advantage of large amounts of information from different sources such as soil, climate, crops and management. In other words, land assessment provides essential information about land resources (Nabarath, 2008). Its results must be presented in reports and maps (Van Diepen et al., 1991, Rossiter, 1996). The results are specified to be used for decision making, both strategic land use planning and specific local land allocation by the direct land users (Bacic, 2003). A widely adapted land evaluation method by FAO in many countries continuously modified to suit their land conditions demonstrates that the FAO framework for land assessment has been significantly improved and is feasible. The duties of land management system have to resolve all of the issues associated with land statistic registration, soil map investigation, land suitability evaluation and land use planning. Based on the results of land evaluation, many land use planning projects have been funded and implemented in many places in Tanzania, Ethiopia, Nigeria and Philippines by World Bank and FAO organization (Cox and Atkins, 1979, Dent and Young, 1981). Land evaluation has become a popular scientific field and obtained many huge achievements in the past three decades (Baniya, 2008). Nowadays, the land suitability evaluation according to FAO framework has been used in FAO and UNDP projects and many national parts of the world, with necessary locally acceptable alternations and simplifications. FAO has learned and borrowed the best from different existing land classification methods, inappropriate elements are eliminated and attempt to implement. Van Diepen et.,al (1991) states that ―It is becoming outdated from an operational point of view, but with a function as background philosophy‖. Nevertheless, Rossiter (1996) claimes that the FAO framework is not only capable of modification and interpretation, but also it can be extended with the help of new analytical techniques (Rossiter, 1996). The Russian scientist Dokuchaev suggests that land evaluation

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State of the Art should indicate the types of soil and natural land quality. The author also emphasizes that all factors for land evaluation must be distinguished subjectively and scientifically. They must be studied and researched carefully to improve land productivity together with agro- economic statistics to propose the best land use measures in localities and in the entire country (Dokuchaev, 1983). Land suitability evaluation for different land utilization types or crops should be considered as the most important content of land use management process. In order to figure out the problems, analyzing and forecasting information regarding land uses, and the results of land suitability assessment need to be applied as a vital basic source (Chuong, 2008a). George (2001) believes that land suitability evaluation consequences are applying much various field and purposes. The applications of land evaluation are shown in Figure 2.7.

Agricultural development planning

Population supporting capacity Irrigation suitability assessment

Agricultural technology transfer Livestock forage balance assessment

Land degradation risk assessment Environmental impact assessment

Agricultural inputs recommendations

Figure 2.7: Applications of land evaluation (inherited from George 2001) The results of land evaluation will be used by land use planners to select the best suitable areas for agricultural purposes, use techniques that range in degree of detail from farmers‘ experience and experts‘ judgment to integrated computer model simulating soil- water flow, nutrient consumption related to crops growth and environmental effects (Bouma, 1989, Van Diepen et al., 1991, Van Lanen and Wopereis, 1992).

2.4 Land evaluation in Vietnam though different periods of time 2.4.1 The period before 1975 The concept of land classification has been long existed in Vietnam though division of ―four ranks of land, six ranks of edaphic compositions‖ in order to collect taxes on land (Thu and Khang, 1998, Vu, 1995) . In the process of production, land was evaluated and categorized in a simple manner as good land or bad land (Nhan, 1995).

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During the feudal and colonial periods, research of land evaluation was implemented in fertile areas, which had capabilities of exploitation with the aim of identifying potential use to establish plantations and farms. In the 1930s, French experts of soil researched of land and land use in Southeast region to develop rubber plantations (Sy, 2012). After 1945, in the North, Department of Land Management, Institute of Soils and Fertilizers, and National Institute of Agricultural Planning and Projection (NIAPP) classified of land in agricultural production areas by applying the Docuchaev‘s method in order to enhance soil fertility management and agricultural land tax ranking (Tuan, 2014). Land was divided into 5 - 7 ranks according to ecological conditions and land characteristics (Chuong, 2008a). Many provinces established soil classifying maps at the community level, remarkably contributed to land management during the production planning time (Thu and Khang, 1998). In the early 1970s, Bui Quang Toan and many scientists of Soils and Fertilizers Institute have studied and evaluated classification in 23 districts, 286 cooperatives and 9 specializing cultivation areas. The researchers have also suggested the technical process of land classification as four main steps including: (1) collecting the documents and data, (2) identifying the local needs to be evaluated, (3) assessment and classification of soil qualities, (4) building land classification maps. The factors in land assessment, classification were divided into four suitable levels and four classes (MARD, 2009). Over the whole, the primarily goal of soil classification and land investigation in this were period mostly focused on making ranking of agricultural land taxes, not focusing on planning suitable crops.

2.4.2 The period of 1975 - 1990 In order to serve the general development planning and the expand land for agricultural production, land evaluation and building soil map were significantly formulated. In this period, Vietnamese soil scientists achieved certain results from fundamental investigation and soil research in the whole country (Chuong, 2008a) as follow: (1) Acknowledging basically soil types and agricultural land resources, this is an important platform for the evaluation of land resources, creating strategies for development for different territories from district to national level. The soil types in the whole country were also inventoried for exploiting and using plan. (2) Specialized crop and new economic areas were established to expand agricultural land such as: rice, rubber, coffee, annual and perennial crops, etc.

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Therefore, the agricultural land area increased from 5 million ha in 1975 to more than 8 million ha in 1999 (VNGOS, 2001). (3) From research of soil characteristics, 1.5 million ha of alkaline soil was improved and used effectively. In addition, there were also several improvements for soil fertility like 1 million ha of salty soil, 1 million ha of exhausted soil, and waterlogged soil. In short, during the centrally planned economy time, most provinces developed maps of land use planning (short, medium and long term) or maps planning of specific crops for supporting the economic development strategies. These results contributed to changes in plant structure, animals, diversification of agricultural products. However, these studies of soil in this period had a number of limitations, and mostly focus on soil fertility with little or not care about land, land use and land evaluation so that the land use planning did not achieve accurate consequences. Besides, the methods of land assessment were not unified and standardized (Tuan, 2014).

2.4.3 The period of 1990s up to the present. In the past few years, especially since 1990, Vietnamese soil scientists have studied and applied FAO land evaluation method based on natural, socio-economic conditions. Land management in Vietnam has been developed regarding the orientation of changing economic patterns and sustainable agro-forestry development (Thu and Khang, 1998). Many studies have been applying the method of land evaluation according to FAO in different ranges and in the whole country. Land evaluation methods of FAO (1976, 1983, 1984, 1985 and 1993) together with experience of international land evaluation experts were quickly comprehensive and initially experimented by soil scientists and land managers (Thu and Khang, 1998). Most initial studies were experimental and only introduced the theoretical framework for land evaluation of FAO to Vietnam. The achieved results relatively conducted to the improvement of land assessment in Vietnam (Vu, 1995) such as a result of investigation on agricultural land evaluation and classification (Toan, 1991), alkaline soil classification in Mekong Delta (Xuan et al., 1994), determining poor soil characteristics (Tan and Mua, 1995), etc. The research mainly concentrated on evaluation natural conditions, land quality and fertility, and did not significantly consider socio-economic conditions. In the past 15 years, many of projects, series of research and experimental programs applying the land evaluations procedures of FAO have been implemented in various ecologic areas and have obtained positive results with flexible application to Vietnam conditions (Chuong, 2008a).

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State of the Art

From 1990 to 1995, NIAPP did a number of studies on land evaluation nationwide in nine ecologic regions and various specialized areas according to investment projects (MARD, 2009). The Land of Northwestern hills and Northern midlands were evaluated by Le Duy Thuoc (1992) and Le Van Khoa (1993). In the Red River Delta, the findings of land evaluation by Cao Liem (1991), Dao Chau Thu (1993), Vu Thi Binh (1995), and Nguyen Cong Pho (1995) were published in applied research of land evaluation under the guideline framework of FAO undertaken on the map of 1:250,000 scales, allowing assessment at the aggregation level for the overall planning of the Red River Delta. In the Southeast region, the research by Pham Quang Khanh (1995) on land mapping units and the current status of land use at 1:250,000 scales showed that there was 54 units of land, 7 main types of land use with 94 land uses systems in agriculture. In the Western highlands, there were 54 agro-ecological units, 195 units of land and 5 land use systems. The results were presented in the studies by Pham Quang Khanh (1990), Nguyen Khang, Do Dinh Dai (1994). In Mekong Delta, studied conducted by Ton That Chieu and Nguyen Cong Pho (1991), Nguyen Cong Pho (1995) found that there were 123 units of land in total region, including 63 units of alkaline land, 20 units of salty land, 22 and 18 units of alluvial land without any restrictions. A nationwide project of land evaluation (1993 – 1994) was conducted on 9 ecological regions with appropriate map scales from 1:250,000 to 1:500,000. The results showed that there were 340 units of land and 90 major kinds of land use on the national map (NIAPP, 1994). In 1995, Vietnam Department of General Cadastral implemented "Land evaluation projects at the district level". Several districts were chosen to present for natural economic regions (Northwestern hills and Northern midlands, Red River Delta, coastal central plain and Mekong Delta) with the participation of scientists, research institutions and the cooperation of local cadastral agencies. The objective of this programme was to promote diversification of agricultural products (Tuan, 2014). Phong (1995) researched and created proposal of using land resources for sustainable agricultural development from 1996 – 2000 and 2010 period. From 1996 to present many prominent studies and research relating to land evaluation and agricultural planning have been carried out. Especially from the national conference (1995) on land evaluation and land use planning from the point of view of ecological and sustainable development was organized by NIAPP with the participant of international and domestic scientists. The conference discussed and examined the application of FAO method to land evaluation in Vietnamese conditions. It also talked about the issue that needed to be continuously studied to complete the content of the land

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State of the Art evaluation procedure of Vietnam and improve the efficiency of application evaluation results into agricultural land use planning (MARD, 2009). Nhan (1996) applied GIS to generating thematic map, establishment of land mapping units in the examining characteristics of land and evaluating land suitability use in agricultural production of the Mekong delta. This study also introduced some difficulties of data information for new land assessment procedure. This land classification method according to the limited conditions had become popular and had been widely implemented until 1997. Nevertheless, the application gave rise to two the following tendencies (MARD, 2009): (1) Completely stereotyped, not concerned about adjusting the engaged factors in the classification process, especially the less important elements leading to inaccurate consequences, improper to reality. (2) Manually arbitrary adjustment, did not set out the provisions and having no detailed explanation leading to coercive results while heterogeneous parameter systems and data. In 1999, a new land evaluation procedure was established by the Ministry of Agriculture and Rural Development (MARD) for serving agricultural production conformable to branches‘ criteria. Afterwards, the processes were comprehensively adopted and many studies proved more logical by choosing the assessment criteria (Chuong, 2008a). In the years of 2002 to 2005, the Ministry of Natural Resources and Environment carried out s project entitled ―Building a restructuring model of land use serving the required industrialization and modernization of rural agriculture‖. This project applied the land evaluation procedure of MARD in order to evaluate land at district level. It was a scientific ground for proposing the appropriate restructuring of land utilization (MARD, 2009). Typically, Khanh (2002) used the results from the project of soil investigation, land evaluation and land use planning of Ba Ria Vung Tau Province to establish soil map and classify soil types in accordance with FAO-UNESCO. The research also evaluated land adaptability for some kinds of land use of the province. Tri (2002) assessed and suggested land suitability areas for agro-forestry-fishery planning orientation in Ca Mau Province in the period of 2001 – 2010, etc. On the other hand, most studies at that time only used land evaluation procedure of FAO, and measured natural characteristics of land affecting land use types, not concentrated much on socio-economic and rural infrastructure conditions during land valuation process (Chuong, 2008a). The Automatic Land Evaluation system (ALES) was developed by Rossiter (1997). It not only allowed building expert systems to evaluate land according to the method

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State of the Art presented in the framework for Land Evaluation of 1976, but also assisted land evaluators in comparing and describing the interactions between land requirements and soil characteristics by using the software (Rossiter and Van Wambeke, 1997). The software interested Vietnamese scientists and was applied to some certain conditions in Vietnam. Khanh and Quang (2003) used ALES to assess land of central highlands of Vietnam. D‘haeze (2005) examined land suitability for Robusta coffee in the Dak Gan district. The finding displayed that ALES brought many advantages. Nevertheless, it completely depended on either constructing evaluation model or specialists‘ opinions. The evolution of new methods and approaches for land assessment together with improved procedure around the world has positively impacted the land evaluation process in Vietnam. Since 2000, multi-criteria method and GIS technique have been developed considerably as a new concept in land evaluation. Those changes opened new directions to accommodate with crop allocation and gained higher outcomes when being applied to agricultural land planning (Chuong, 2008a). Tri and his colleagues practiced multi-criteria method in land assessment at Trung Hieu Commune under the FAO procedure guideline (1976). This research mentioned four multi-criteria methods including: weight summation, electre-II, expected value and mixed method. The results showed that among four mentioned methods, the weight summation and expected value were the two most suitable ones (Tri and Tri, 2004). FAO framework for International Sustainable Land Management Evaluation (FAO, 1993b) and multi-criteria approach with three main given parameters of economy, society and environment were also applied to land evaluation in Lam Ha District, Lam Dong Province. The study concluded that multi-criteria analysis in land assessment provided more positive results in selecting sustainable agricultural developing zones (Khanh and Dinh, 2004). Giap et al., (2005) used GIS and multi-criteria for land evaluation for shrimp farming in Hai Phong Province and the findings demonstrated that this approach was the potential usefulness of GIS in combination with multi-criteria for aquaculture development in land evaluation framework to evaluate land suitability for shrimp farming (Giap et al., 2005). The multi-criteria approach and GIS technique, which were practiced in central Vietnam for land evaluation by Chuong (2008), proved that multi-criteria method, together with the GIS for agricultural crops, has high feasibility and is appropriate at a community level (Chuong, 2008b). In addition, Khoi and Murayama (2010) applied GIS based multi-criteria evaluation method for delineation of suitable crops areas in the Tam Dao national park region and concluded that the approach provides an effective framework for land evaluation. The choice of judgment parameters and the identification of a suitable level for each parameter had a direct influence on the results

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(Khoi and Murayama, 2010). With all of advanced mentions above, the application of land evaluation procedure of FAO combining with multi-criteria analysis in land evaluation have achieved better results than traditional methods and widely been applying and attracting the interest of Vietnamese scientists. In Thanh Hoa Province, studies on land evaluation for different types of land use and for agricultural crops in the past years have not been a big concern. According to the report of the current status of land use in 2000, the state had 8 major soil groups with 42 subunits of soil (Hoa, 2000). In 2010, Department of Natural Resources and Environment of Thanh Hoa combined with Hanoi University of Agriculture implemented additional surveys of and investigations into land resources in the state to build a soil map at scales of 1:100,000 under the classification of FAO-UNESCO. The results indicated that there were 10 major soil groups with 60 subunits of soil in the whole province (Fertilizer, 2011). Thai et al., (2013) investigated the characteristics of soil to build soil maps for major paddy rice growing areas. Consequently, one soil map at scale of 1:100,000 in the whole province, 12 soil maps at scale of 1:25,000 for 12 researched districts and 251 soil maps at scale of 1:5,000 for 251 communes belonging to the researched districts were built. Those significant results play an important role in both planning agricultural strategies of the province at macroscopic scale and constructing detail scale of land use planning in the commune level.

2.4.4 Discussion of land suitability evaluation in Vietnam In the past 20 years, studies of land suitability evaluation of particular types of land use, especially for agricultural crops, have gained substantial achievements. The application of the FAO framework (1976) to Vietnamese conditions was well implemented and assisted to finalize the land assessment procedure for Vietnam. The combination between multi-criteria evaluation method and the guideline FAO was employed in certain areas. In addition, land evaluation activities were additionally experimented with GIS and remote sensing techniques to create thematic maps and map overlaying to establish land mapping units and soil map for serving land use planning. Successive studies of land evaluation in the past few years have provided great contributions of both scientific and realistic significance to the national land use planning, especially to the orientation of agro- forestry developing strategies. Conversely, many deficiencies can be found in these applications as follows: (1) There are very few projects of land evaluation based on the perspective of ecological and sustainable development to care for the expectation of detail land use planning, allocating suitable crops and developing cultivation in the

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adaptation of crops diversity at the commune level, conspicuously in the coastal sandy land areas. The research was entirely performed from district to provincial and national scale. (2) The framework of FAO (1976) for land evaluation and cultivation system analysis have widely been concerned and practiced in Vietnam. On the other hand, not much projects combined both methods for land use planning. In the coastal sandy land of Thanh Hoa, up to now, there has been no official study on this issue. Most of the research concentrated on analyzing natural characteristics of land to build soil maps and applying FAO procedure to evaluation activities. (3) Land evaluation research in Vietnam in general and in Thanh Hoa state in particular mostly has implemented in the certain parts or in the specific areas. There is not any homogeneous document of scientific land assessment procedure to apply and employ in further other areas such as the hilly or the sandy and other parts with different condition in Vietnam. (4) The combination between FAO procedure with GIS and multi-criteria evaluation technique has been conducted in Vietnam in the past few years. However, it did not bring a highly effective result because of the limited data resources and the knowledge of land evaluators.

2.5 Remote sensing techniques 2.5.1 Remote sensing for agriculture and land cover mapping More and more satellite missions like food production, resource management and environmental characterization associated with remote sensing (RS) have developed over the past decade and they are going to extend over the coming decade and beyond. Results from these missions will offer the potential for contributing to the security of human existence on Earth in different ways. RS has powerful tools that can be applied to handle the problem of thematic maps which have to be updated. It has capabilities to map and extract information of the earth resources for different purposes. One of the most important applications of RS is land cover mapping (Campbell and Wynne, 2011). According to Casady and Palm (2002) remote sensing for agriculture can be defined as ―observing or a field crop without touching it‖. It integrates new technologies that can offer increasingly efficient, complete, precise and timely information (Casady and Palm, 2002). One of the earliest and the primary utilizations of RS in agriculture is crop identification and area calculation to ensure food security at regional and global scale. RS

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State of the Art data is directly used to predict yields of crop in the regression models or input data to agro- meteorological models for their calibration (Piekarczyk, 2014). RS has been used in the first half of the twentieth century with local-scale forest mapping and land cover mapping initially was a product of the development of RS from aerial photography (Colwell, 1960). Viewing large areas repeatedly was necessary for acquiring information about land cover. Since 1972, the first satellite sensor for monitoring the earth resources has been launched by National Aeronautics and Space Administration (NASA). This has had significant advances in RS that have allowed observing agricultural resources mapping over much larger areas by using space-borne sensors. RS has been used in studies on vegetation for many years with various perspectives such as building the map of forest fires, vegetation cover or detecting changes in vegetation through different periods (Janssen et al., 1990, Dorren et al., 2003). RS has been widely used in natural research for mapping vegetation since it can quickly determine the data, distribution, and change of vegetation for large areas. In addition, it provides the possibility of inferring results of mapping to regional extent, even in large inaccessible areas (Hoersch et al., 2002). Having a diagnostic tool, RS allows measuring reflectance of light energy from the crop canopy, which could be useful in detecting plant stress and correcting the problem at the same time. Using RS to create a picture or map is quick approach for calculating the extent of an essential crop characteristics or a field that has the same characteristics (Casady and Palm, 2002). Many countries have been using RS to identify crops and area estimation. In the United States of America, the National Agricultural Statisics Service (NASS) has extensively applies RS methods to create land cover maps and acreage. However, NASS mainly uses traditional methods based on information collected from famers for estimating crop area since satellite images do not meet the time requirements of reports on the examination of crop area (Piekarczyk, 2014). Information about land use and land cover is needed for water-resources inventories, flood control, water supply planning, and waste water treatment (Anderson et al., 1976). Townshend et al., (1991) used RS to calculate the changes in the vegetative cover of the land surface on a global scale. More recently, numerous studies have used RS data as a basic to create a global land cover mapping from the advanced very high- resolution radiometer (AVHRR). DeFries and Townshend (1994) using maximum likelihood classification of monthly composited AVHRR normalized difference vegetation index data at 10 spatial resolution to build the global land cover map compiled from RS. Afterward, DeFries et al., (1998) applied a decision tree classification technique and using

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AVHRR data to produce a map of global land cover at 8-km spatial resolution. Friedl et al., (2002) using data from the moderate resolution imaging spectro-radiometer (MODIS) described global land cover mapping activities. One of the most influential factors causing ecoogical systems and climate change is land cover change (Vitousek, 1994). It reflects human activities and physical environments on Earth. However, knowledge of land cover and its dynamics is particularly limited by the paucity of accurate land cover data (Foody, 2002). Primary causes of changes to land use are commonly urbanization and new residential settlements, which has impacts on local communities‘ environmental, social and economic sutainability (Yuan et al., 2005). Lambin and Ehrlich (1997) stated that the change in land cover may be caused by one or all of the following circumstances: (1) Long-term change to climatic conditions (2) Geomorphologic and ecological factors (3) Human beings‘ land exploitation, such as deforestation and overuse (4) Natural disasters, such as droughts and foods and (5) Climatic change as a result of human activities In this study, maximum likelihood supervised classification and post-classification change detection techniques were used to find out land cover changes over the period of 1989 - 2013 in the research site. The images source for the 1989 year was the Landsat Thematic Mapper (TM) data acquired by Landsat 5. Landsat Operation Land Imager and Thermal Infrared Sensor (OLI and TIRS) data acquired by Landsat 8 was applied to classify land cover in 2013.

2.5.2 A brief history of Landsat The Landsat program is a combination between the US Geological Survey (USGS) and NASA to collect the Earth data by using a series of satellites. The mission of Landsat is aimed to collect data from monitoring the Earth, including land areas, coastal lines, and coral reefs for scientists and authorities to know about the possible changes in the Earth's surface for scientific purposes (Simberloff et al., 2005).

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Table 2.6: Landsat mission dates

Satellite Launched Decommissioned Sensors Landsat 1 July 23, 1972 January 6, 1978 MSS and RBV Landsat 2 January 22, 1975 July 27, 1983 MSS and RBV Landsat 3 March 5, 1978 September 7, 1983 MSS and RBV Landsat 4 July 16, 1982 June 15, 2001 MSS and TM Landsat 5 March 1, 1984 January 2013 MSS and TM Landsat 6 October 5, 1993 Did not achieve orbit ETM Landsat 7 April 15, 1999 Operational ETM+ Landsat 8 February 11, 2013 Operational OLI and TIRS Source: (USGS, 2012) TM sensors onboard Landsat 4 and 5 were designed with several additional bands in the shortwave infrared (SWIR) part of the spectrum and improved the spatial resolution of 30 meters for the visible, near-IR, and SWIR bands and the addition of a 120-meter thermal-IR band. Landsat 7 (ETM+) was designed to collect data of the world‘s land mass into 57,784 sences with 30-meter visible, near-IR, and SWIR bands, and 120-meter thermal band. The spatial resolution of panchromatic band is 15-meter with a fourfold development in spatial resolution over TM. The OLI have eight spectral bands at 30-meter resolution and one panchromatic band at 15 meters. The TIRS captures data in two long wave thermal bands with 100-meter resolution, and is registered to and delivered with the OLI data as a single product. TIRS data products have a 30-meter resolution and a 16-bit range (USGS, 2012). Table 2.7: TM and ETM+ band designations

Wavelength Resolution Spectral Bands Use (micrometers) (meters) Bathymetric mapping and distinguishing soil Band 1: blue-green 0.45–0.52 30 from vegetation and deciduous from coniferous vegetation. Emphasizes peak vegetation, which is useful Band 2: green 0.52 – 0.61 30 for assessing plant vigor. Band 3: red 0.63 – 0.69 30 Emphasizes vegetation slopes.

Band 4: Reflected IR 0.76 – 0.90 30 Emphasizes biomass content and shorelines. Emphasizes moisture content of soil and Band 5: Reflected IR 1.55 – 1.75 30 vegetation; penetrates thin clouds. Useful for thermal mapping and estimated soil Band 6: Thermal IR 10.40 – 12.50 120 moisture. Useful for mapping hydrothermally altered Band 7: Reflected IR 2.08 – 2.35 30 rocks associated with mineral deposits. Band 8: panchromatic 0.52 – 0.90 15 Useful in ‗sharpening‘ multispectral images. Source: (USGS, 2012)

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Table 2.8: OLI and TIRS band designations

Wavelength Resolution Spectral Bands Use (micrometers) (meters) Band 1: coastal/aerosol 0.43 – 0.45 30 Increased coastal zone observations. Bathymetric mapping and distinguishing soil Band 2: blue 0.45 – 0.51 30 from vegetation and deciduous from coniferous vegetation. Emphasizes peak vegetation, which is useful for Band3: green 0.53 – 0.59 30 assessing plant vigor. Band 4: red 0.64 – 0.67 30 Emphasizes vegetation slopes. Emphasizes vegetation boundary between land Band 5: Reflected IR 0.85 – 0.88 30 and water, and landforms. Used in detecting plant drought stress and delineating burnt areas and fire-affected vegetation, and is also sensitive to the thermal Band 6: Reflected IR 1.57 – 1.65 30 radiation emitted by intense fires; can be used to detect active fires, especially during nighttime when the background interference from SWIR in reflected sunlight is absent Used in detecting drought stress, burnt and fire- Band 7: Thermal IR 2.11 – 2.29 30 affected areas, and can be used to detect active fires, especially at nighttime. Band 8: Reflected IR 0.50 – 0.68 15 Useful in ‗sharpening‘ multispectral images. Band 9: panchromatic 1.36 – 1.38 30 Useful in detecting cirrus clouds. Useful for mapping thermal differences in water Band 10: TIRS 1 10.60 – 11.19 100 currents, monitoring fires and other night studies, and estimating soil moisture. Band 11: TIRS 2 11.50 – 12.51 100 Same as band 10 Source: (USGS, 2012)

2.5.3 Applications of Landsat data Nowadays, Landsat data is becoming a fundamental data source for addressing basic science questions. It is also valuable in assisting decision-makers in such diverse field, including monitoring and observations of the Earth‘s surface. Landsat data has been worldwide used by both state and private institutions in deffirent fields such as commercial, industrial, civilian, military, and educational communities. As a global change in various fields such as agriculture, forestry, geology, resource management, geography, mapping, water quality, and coastal region so that many studies are applying the data of Landsat for their research. While Landsat 7 gives a great deal of crucial data from observations of the Earth, Landsat 8 keeps going with its

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State of the Art missions. The consistency of Landsat data over the years of acquisition can reveal land cover changes over period of time (USGS, 2012)

Figure 2.8: Top Landsat data uses from October 1, 2015 through March 31, 2016 Source: (USGS, 2016b)

2.5.4 Landsat TM and Landsat OLI-TIRS  Landsat 5 Landsat 5 was launched on March 01, 1984 at Vandenberg Air Force Base, California by NASA and continued to acquire imagery in 7 spectral bands until January 2013. When it was launched, the weight of this satellite was amost 2,200 kilograms. The spacecraft has 3-axis stabilized, zero momentum with control of 0.01 degrees using reaction wheels and 7 spectral bands with 3 bands in the visible range and 4 bands in the infrared range. Most of them have a 30m pixel size. Band 6, with a spatial resolution of 120m, is aimed to measure the temperature with unique sensitivity to thermal infrared radiation. It is to distinguish trees and plants, water, moisture, some rock properties (Wang et al., 2013). The satellite orbits the Earth at the altitude of approximately 705 kilometers with a sun-synchronous 98.2-degree inclination and a descending equatorial crossing time of 9.45 a.m. The Landsat data is delivered into 57,784 scenes that measure 185 kilometers wide by 170 kilometers long. Landsat 5 is on the Worldwide Reference System-2 orbit path and revisits the same spot on the earth every 16 days.

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 Landsat8 Landsat 8 is the next-generation of Landsat satellite to ensure the continued acquisition and availability of Landsat-like data beyond the duration of the current Landsat missions (USGS 2016a). The satellite was launched in 2013 February 11 at Vandenberg Air Force Base, California by NASA. Its data became available on May 30, 2013. In comparison with previous versions, this satellite collects high-quality data to satisfy scientific and operational requirements to observe land use and land change by NASA and USGS (USGS, 2014). The satellite is maintained with periodic adjustments for life of the mission by Worldwide Reference System-2. Like the Landsat5, Landsat8 orbits the Earth at 705 kilometers altitude and covers the entire globe every 16 days. It weighs approximately 2,071 kilograms. The spacecraft is about 3.0 meters long and 2.4 meters in diameter. Landsat 8 carries two instruments. The first is Operation Land Imager (OLI) sensor includes refined heritage bands, along with three new bands: a deep blue band for coastal/aerosol studies (band 1), a shortwave infrared band for cirrus detection (band 9) (Gao and Kaufman, 1995), and a quality assessment band. The second one is Thermal Infrared sensor (TIRS) offers two thermal bands. Both of these sensors can give improved signal-to-noise (SNR) radiometric performance within a 12-bit dynamic range (Boori et al., 2015). Landsat 8 is comprised of totally 11 bands, most of which have a spatial resolution of 30 meters. Band 1 is aimed to collect coastal and aerosal data. Band 8, with 15 meters, is used to collect panchromatic data. Band 9 is used to detect circus clouds. Bands 10 and 11 are especially used in investigating surface temperature exactly in a range of 100 meters. The approximate scene size is 170 kilometers north-south by 183 kilometers east-west.

Figure 2.9: Landsat 8 Spectral Bands and Wavelengths compared to Landsat 7 ETM+

Source: (USGS, 2016a)

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2.6 Geographic Information System techniques 2.6.1 GIS and role of GIS for land evaluation GIS is a powerful and sophisticate tool for analyzing and displaying spatial data from the real world (Burrough, 1986). A GIS has demonstrated virtually throughout the world and effectively when utilized for examining particular lands to build an environment (Baban et al., 2007). GIS is a technological tool for embracing geography and making intelligent determinations (Braun and Pantel, 2008). A GIS is a computer-based system for the collection, storage, integration and determination of geographic data. Geographic information has been processed into a form significant to the recipient. The data is commonly organized by separate thematic maps or sets of data as a map layer, coverage or level. Irrespective of spatial data organization, GIS is useful for making spatial decisions (Drobne and Lisec, 2009). With a powerful set of tool, GIS is helpful for visualization, analysis and evaluation of different scenarios. The strength of GIS is its ability to integrate various types of data from different sources on a common spatial platform without the need for each sector duplicating data collection efforts. GIS has particularly been a primary instrument used in LUP approaches, especially for agricultural management. It is information system including works and links together that has a capability to carry out many tasks utilizing both spatial and attribute data store in it. Meanwhile, land use suitability analysis requires handling both spatial and attributing data in different data layers. Thus, it is appropriate to use GIS to exploit its strong ability in managing geographic information.

Figure 2.10: GIS application in agriculture. Source: (Abz, 2011)

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GIS may help land-users and decision-makers understand the processes of land evaluation and make right decisions. It improves the efficiency of data processing, solves data integration problems, and supports spatial analysis (Bronsveld et al., 1994, Rossiter, 1996). In addition, GIS is helpful to improve the description of land utilization types required for land evaluation (Van de Putte, 1989, Bronsveld et al., 1994 & Rossiter, 1995). The intelligent query, analysis and integration mechanism of GIS make it becomes a scientific instrument to analyze data for land use planning. In recent time, GIS approaches are being effectively used as powerful tools, which assist in suitability assessment and management of the land resources (Obi Reddy et al., 2001). An agricultural GIS can help land users to plan and to support the data management during the agricultural production process, while ensuring the proper balance between competing resource value at the same time. Application of GIS may increase the accessibility and flexibility of data and may improve the linkages and understandings of relationship between multiple types of information (Chuong, 2008a). Appropriate management of agricultural resources based on their potential and limitation plays an essential role in development of land and other resources on sustainable basic. GIS technology has been used by different users for multiple purposes to create resource database and to give right strategies for agricultural development in such a sustainable way (Venkataratnam, 2001). One of the optimum features of GIS-based approaches to land use suitability is that it can propose a procedure involving mapping data on the ecological and human-associated attributes of the research site with overlaying techniques. Also, it can show information on separate maps from high suitability to low suitability as well as construct the entire map of suitability (Malczewski, 2004). Over the last four decades, GIS-based land-use suitability techniques have rapidly become integral components of urban, regional and environmental planning activities (Brail and Klosterman, 2001, and Collins et al., 2001). In the process of land suitability assessment, land unit inquiries on land use for a specific crop are called the input data and spatial distribution, boundary and scale of each crop are the output (Nabarath, 2008). Application of GIS techniques in land suitability evaluation for cultivation is explanation, illustration output data from the entering, storing and analyzing input data. Results of land suitability classification will be presented faster, more precisely and more economically by overlaying thematic maps and by analyzing attribute data with the assistance of GIS (Chuong, 2008a).

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2.6.2 Data structure There are two types of spatial data: geometric data and descriptive data. Spatial data is defined as the data that relates to geographic location or relationship of objects and features. It can be shown as points, lines or polygons to express location or shape of entities. Generally, spatial data has two formats: vector and raster data. Series of points, lines and polygons with attribute-associated data are known as vector data and series of cells forming a established pattern with geographic data are known as raster data. Descriptive data are the characteristics that are related to spatial data. It has three formats: Categorical data, Continuous data and Meta data.

2.6.3 Layers To show geographic datasets, layers or mechanisms are used. GIS has the capability to manage many layers of data. Layers display different sets of geographic information. Each layer pertains to a certain type of data. They can provide information on geologies, soil, land use, species distribution, vegetation, etc. By combining many layers of information about an area, GIS can offer more flexible and complete understanding of that area.

Figure 2.11: An example of GIS layer. Source: (Jamaica, 2015) 2.6.4 Multi-criteria analysis within GIS context for land suitability Decision-making applying a number of criteria can express weights, values or intensities of preference in land evaluation (Hossain and Das, 2010). Decision making is the process that leads to a choice between a set of alternatives based on numerous data concerning the problem at hand. A large set of feasible alternatives and multiple conflicting and incommensurate evaluation criteria is the types of decision problems (Drobne and Lisec, 2009). In a study of a new scientific method for management decision,

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Simon (1977) has suggested that a structure for analyzing human decision making process should be distinguished between the intelligence, design and choice phase. - Intelligent phase: the situation is analyzed for the problems and prospects. - Design phase: The problem is already understood and decision-makers develop alternative solutions. - Choice phase: Evaluation of the decisions and choose the best alternative. A number of studies, particularly in the regional economic planning and decision- making research fields have been certain weaknesses in the neoclassical view of decision- making and site location. A multi-criteria evaluation (MCE) is able to interprete alternatives with impacts of environment, society and economy (Carver, 1991). One of the primary objectives of MCE is to suggest a number of alternatives in the light of multiple criteria and contradictory purposes (Voogd, 1982). In order to implement the idea, it is needed to develop negotiated alternatives and a ranking of options according to their degree of attractiveness (Janssen and Rietveld, 1990). MCE has evolved from a mechanism for the choice of the best option from multiple competing alternatives, with a range of decision aid techniques. It offers the structuring of a decision problem and the exploration of the concerns of decision actors. The examination of alternatives under various perspectives and the evaluation of their robustness against uncertainty (Beinat and Nijkamp, 1998). Such systematic, intellectual and transparent opinions probably lead to more effective and efficient decisions by individuals or groups of decision-makers (Sharifi et al., 2004). Nowadays, the application of MCE methods in land evaluation process is widely ranked and rated. The major issue in MCE is combining the data from different criteria to a single index of examination. In land suitability analysis processes, identifying the most suitable spatial pattern for future land uses based on specified requirements, preferences, or predictors of some activity is strongly important and it is multiple criteria analysis (Hopkins, 1977, Collins et al., 2001). MCE can be applied to define the most appropriate areas for agricultural crops. It is helpful in the situation when an area needs to be divided according to the most suitable location for certain crops. Otherwise, the difficulty is to combine the maps in order to decide which crop is the best for a specific location. Choosing a major land utilization type, selection, organization of the criteria, and decision of suitability limits for each class of the criteria is one of the most important steps in the agricultural land suitability assessment. Those parameters can be examined to evaluate the suitability by MCE techniques (Sarkar et al., 2014). GIS and MCE are capable of assisting land users in

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State of the Art making a right decision in that GIS effectively controls assessment factors and MCE synthesizes them into a suitability index (Neupane et al., 2014). The combination between MCE and GIS techniques is both traditional and modern approaches to analyzing land evaluation, primarily aiming at evaluating factors and recommending feasible decisions (Sarkar et al., 2014). According to Mendoza (2000) there are many significant advantages of applying the integration of MEC and GIS to land suitability analysis and allocation. The GIS environment does not allow enabling the spatially explicit evaluation of site suitability and the assignment of various measures of suitability to specific sites, but also offers area allocations at specific geographic locations. In Vietnam, combining MCE within GIS context in land suitability analysis has just been applied in recent years. Khanh and Dinh (2004) developed a model for multi-criteria analysis including economy, society, and environment. A conclusion indicated that MCE within GIS context for land suitability evaluation in Lam Ha, Lam Dong state was highly feasible ad suitable with the local condition of the locality. The consequences obtained from land evaluation for fruit crop in central Vietnam by applying MCE approach within GIS context indicated that application of GIS and multi-factors evaluation could provide a superior database and guide map for decision-makers as considering to change non- suitable crops by the suitable ones (Chuong and Boehme, 2005, Chuong, 2008b). Integration of GIS and MCE for land use suitability was conducted by Duc (2006). The research proved that the method was a useful application for land suitability analysis. Tri et al., (2009) implemented land evaluation for Song Phu Village and Tam Binh District, Vinh Long Province in combination the cultivation system analysis and MCE method. The results presented the fundamental information for selection of promising land use types that was a basic for land evaluation, and supported effectively to multi-criteria evaluation of land use types. The finding also provided a basis for proposing different scenarios in land use planning. In many circumstances, it is comparatively difficult to calculate relative weights of the multiple criteria involved in decision making on suitability of land mapping unit for a specific kind of land utilization. Thus, it is indispensable to find out an appropriate method, which can help to determine the weights of different parameters. Analytical Hierarchy Process (AHP) pproach is a widely used method in multi criteria decision making process (Bagheri et al., 2012).

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State of the Art

2.6.5 Analytical Hierarchy Process theory for land suitability evaluation Combination of various requirements from socio-economic, natural conditions and other sources to determine the suitability is carried out for different purposes. Although there exist many methods for the definition of weight, one of the most promising in development of pairwise comparisons is AHP technique. AHP approach was introduced by Saaty (Saaty, 1977, 1980). In principle, the model is designed to select the best option from a number of alternatives with respect to several criteria. In this process, decision-makers have to develop overall priorities for ranking alternatives by evaluating simple comparison judgments. AHP allows for both finding inconsistency judgments and providing the means to improve the consistency (Zarkesh et al., 2010). The method is a decision-making tool to explain the general decision operation by breaking a complex problem into a multi-level hierarchical structure of objectives, criteria, sub-criteria and alternatives (Saaty, 1990). It is practical and effective to dealing with multiple decision problems (Guo and He, 1999) and useful for unifying different conflicting objectives to reach at an agreement decision (Bascetin, 2007). Land suitability assessment comprises many criteria and alternatives, which need to be examined by decision-makers in order to achieve an optimum goal. The study by Bello-Dambatta (2009) on contaminated land management showed that the AHP is capable of handling the related complication of contaminated land management and it has high values compared to most other decision analysis tools. The AHP uses a hierarchical structure to decompose decision problems, and uses both qualitative and quantitative data to infer ratio scales between decision factors at each hierarchical level using pairwise comparisons (Bello-Dambatta et al., 2009). The structure expresses the human tendency to organize parameters of a system into different ranks and to group like elements at each level (Saaty, 1982). Using pairwise comparison offer the AHP ability to incorporate multiple types of information and comparing two parameters simultaneously. Therefore, comparisons of pairwise and relative weights can be considered as inputs and results, respectively (Mohammadrezaei et al., 2013). As the base requirement, in the AHP process the decision-makers have to answers a series of questions such as ―how many time importance of factor A when compare to factor B, C, D, etc.‖. The results of these comparisons are used to obtain the important weights of the decision criteria and the relationship of the alternative in terms of each individual element s. A ratio scale is applied to the comparison processes as displaying in Table 2.9.

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State of the Art

Table 2.9: The fundamental scale used for the AHP (adaped from Saaty, 1977, 1990, 2008)

Relative intensity Definition Description 1 Equal value Two factors are of equal value 3 Slight more value Experience slightly favors one requirement over another 5 Strong value Experience strongly favors one requirement over another A requirement is strongly favored and its dominance is 7 Very strong value demonstrated in practice The evidence favoring one over another is of the highest 9 Extreme value possible order of affirmation Intermediate values to 2, 4, 6, 8 When compromise is needed reflect compromise

On comparing element A and element B, element A has one of the aforepresented numbers, and element B has a reciprocal value (Sirikrai and Tang, 2006). For instance, if A is considered to be very strong importance as a factor for the decision than B, then the value 1/7 would be assigned to B relative to A. Decision making by using AHP method involves four steps as follows (Saaty, 2008): (1) Assigning a problem in the form of a hierarchy with goals, factors and alternatives. (2) Structuring the judgment according to a decision-maker‘s preferences from the top with the objective of the decision, through the intermediate level to the lowest level. (3) Evaluating the priorities of the parameters and alternatives by using the numbers in the hierarchy. (4) Completing the synthesis of these results to examine the best option. These steps are also discussed separately below (Bascetin, 2007). Step1. Set up the ‗‗n‘‘ requirements in the rows and columns of an ‗‗n x n‘‘ matrix, then insert the ‗‗n‘‘ requirements into the rows and columns of a matrix of order ‗‗n‘‘. Step2. Perform pairwise comparisons of all the elements according to the criterion by using the fundamental scale for each pair of elements. Insert their determined relative intensity of value in the position and their reciprocal value. Step3. Estimate the eigenvalues of the matrix by averaging over nomalized columns. Step4. Use the estimated eigenvalues to assign each requirement to its relative value.. In this study, AHP integrated with GIS was applied to evaluate the suitability of the agricultural land of the study area for some main crops through the MCE technique.

48

Characteristic of the Study Area

Chapter 3. Characteristics of the Study Area

According to the purposes of this current study and throughout the availability of the existing data sources, this study chose Quang Xuong District in Thanh Hoa Province to conduct this present case study. This chapter presents the general background of the study area to support the implementation of appropriate land suitability evaluation. The selection of the district for this research was based on some major reasons as follows: (1) There is no study on land evaluation for the coastal sandy land of Thanh Hoa Province, although the area has much competitive advantages and land potential for development of agricultural crops. (2) There were a diversity of soils and different annual crop species in the coastal sandy land of Thanh Hoa. (3) Development of agricultural crops may lead to ensure food security, reduce land degradation, poverty, increase income, and living standard for the local famers. (4) The available data and the ability to sharing land information of the district. (5) States policies and strategies are also encouraging to manage agricultural land uses associated with sustainable developments.

3.1 Natural resources and environment conditions 3.1.1 Geographical location and topographical characteristics Quang Xuong is one of a coastal district of Thanh Hoa Province. It has 40 communes and one town, geographical location at 19034‘ - 19047‘N latitude and 105046‘ - 105053‘E longitude, with the north bordering Thanh Hoa City, Hoang Hoa District and Sam Son Town, the south with Tinh Gia District and Nong Cong District, the east with the South Sea and lasting 18.2 km from Quang Minh Commune to Quang Nham Commune, and the west with Dong Son District. The district has a natural area of 22,780.12 ha; occupied 2.05% are of the whole province. It is covered by two major river systems, River Ma to the north and River Yen to the south. The location of the study and its boundaries are displayed as a Figure 3.1. The topography of Quang Xuong District is saddleback and relative flat, which runs from the north to the south. The average height above sea level is from 3 to 5 meters. 3.1.2. Climatic condition Quang Xuong is located in the tropical and temperate zone, similar to the climate of the entire province. It is characterizes by strong monsoon influence, a considerable amount of sunny days, and with a high rate of rainfall and humidity. The weather of the district is

49

Characteristic of the Study Area divided into four distinct seasons: spring, summer, autumn and winter. It is hot and humid weather by influence of the south-westerly dry wind in the summer; dry and little rain, occasional appearance of frost in the winter (Appendix 3.1).

Figure 3.1: Location and boundary of the study area. Source: Department of Natural Resources and Environment Management of Thanh Hoa Province

Temperature regime (Figure 3.2): The total temperature of the research area is about 8300 – 84000C per year. The annual average temperature is around 240C. The average temperature of hot months ranges from 270C – 290C. That of the hottest month can reach the record of approximately 400C (May, June and July). This is a prolonged dry and drought period; a shortage of water has bad effects on production, living activities, and the cultivation of agricultural crops. From August to next January, the temperature descends significantly. The coldest month of the year is January with the average temperature of 17.20C. On the coldest day the temperature can drop to under 50C, and this has a big influence on the growing and development of paddy rice and annual crops, especially on tropical crops which are in need of high temperature.

50

Characteristic of the Study Area

35

30

25

20

15

10 Tmin (oC) Tmax (oC) T_mean (oC) 5 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 3.2: Monthly mean temperatures of the study area Average data of 20 years (1993 – 2012) recorded at weather station of Thanh Hoa City

Rainy regime (Figure 3.3): The annual average precipitation ranges from 1600mm to 2000mm and is irregularly distributed. Therefore, this causes some disadvantages to agricultural crops. The rainy season is from July to October. Due to impacts of low tropical atmosphere, the rainfall in this season is very high, from 190mm to 350mm, accounting for 80% of the annual average rainfall. The natural disasters are storms which mainly occur in September causing flooding and strongly affecting cultivation activities. The dry season is from December to March, often lasting for 4 months causing a shortage of water. In this season, the rainfall volumes are often lower than the amount of evaporation. This issue is drought to crops in some areas of difficult irrigation conditions. The low air humidity and hot dry south-westerly wind bring many negative influences on the growing of agricultural crops in December.

Precipitation (mm) 400

350 300 250 200 150 100

50 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3.3: Monthly mean precipitation distribution Average data of 20 years (1993 – 2012) recorded at weather station of Thanh Hoa City

51

Characteristic of the Study Area

Air humidity, sunny hours and water evaporation (Figure 3.4): The humidity is rather high. The average account is over 80% in most of the months and is rarely under 60%. In December, the humidity drops to around 60.5%. At the end of the winter season, on the days of drizzle, the humidity is up to 89.5% and sometimes reaches to the saturation level. The average number of sunny hours is relatively high, with about 128.50 hours per month. The highest record of sunny hours is 196.74 in August and the lowest record of 56.81hours in March of the year. Generally, the high and stable humidity and long high sunny hours for year have many advantages for the growing and development of agricultural crops. On the other hand, the humidity also is good conditions for the development of pests and fungus that may harm crops and local harvests.

250

200

150

100

50 Evaporation (%) 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3.4: Relative humidity, sunny hour and evaporation Average data of 20 years (1993 – 2012) recorded at weather station of Thanh Hoa City

Water evaporation is not stable during the years, rather high in months of long sunny hours. The average annual evaporation level is about 82.25mm. The highest water evaporation in June, July and November is over 100mm, and reaches the peak of 126.12mm in June. The evaporation gradually descends from January to April when the weather turns into the dry season. The average evaporation level is about 52mm and reaches the lowest level in February, with about 45.4mm. In general, the weather in Quang Xuong District is favorable for growing and developing many agricultural crops. However, there are some difficulties which negatively impact agricultural production, as crops of summer and summer-autumn season are influenced by hot dry south-westerly wind. The low temperature in December, January and February brings several disadvantages for cultivation of spring crops. Particularly, irregular precipitation is a cause of drought in months with less rain and flood in months with more rain, causing difficulty for production of summer and spring crops.

52

Characteristic of the Study Area

In short, thanks to sharing boundary with the south of Thanh Hoa City, the political economic center of the province and Sam Son Town, the study area has a favorable geographic location for comprehensive economic development like circulation of goods, industrial product, transferring scientific and technical progress, and economic development. The weather conditions allow the study area to arrange cultivation of three crops at the place with advantageous irrigation and drainage system, with a variety of crops including both tropical and temperate origin. Nevertheless, the district may be faced with many troubles in agricultural activities from climate conditions, the consequences of agricultural production largely depended on reasonable arrangement of cropping systems in order to achieve high productivity and avoid the disadvantages caused by the weather.

3.1.3 The water resources Surface water resource: Quang Xuong has a rich hydrological system. It is entitled an irrigation system of Chu, Ma, and Yen Rivers. With 28 kilometer long flows through the district, Yen River supplies water for the communes in the south of the district. The irrigation water of the communes in the north is provided by Chu River. The drainage systems include Thong Nhat, Ly and Hoang River. Thong Nhat River contains and drains away the excessive water for Dong Son, Quang Xuong District, and Thanh Hoa City. It is also the source of additional irrigation water for the northern communes of the district. Rivers Ly and Hoang do not only drain water for Quang Xuong, Nong Cong and Trieu Son district but also are important reserves of additional irrigation water for the southern communes of the study area. The survey results demonstrate that irrigation systems have certain difficulties, at the end of irrigated channel and head of drained channel so that vulnerable drought in the dry season and flood in the rainy season. Some non-irrigated areas have high product costs due to use the water pump mechine system to supply water for the fields. Groundwater resource: The groundwater resource of Quang Xuong District belongs to the strip of coastal plain of Thanh Hoa Province. It is divided into two layers due to relatively flat topography and thick sedimentary cover from 10 to 100 meters: (1) The groundwater from 10 to 15 meters (water is contained in soil, grained or medium rocks) flows at the wells at a speed of about 0.7 to 1.7 liters per second. Water quality has level of bicarbonate, calcium chloride, sodium chloride is about 1 gram per liter due to influence of the tide. (2) Groundwater layer is under 15 meters, which has weak pressure, but abundant

53

Characteristic of the Study Area amount of water. Especially, there are drilled holes which have the flow from 15 to 20 liters per second. This layer is soline and mineralized from 1.0 to 2.5 gram per liter.

3.1.4 Soil resources 3.1.4.1 Soil classification in the study area Data resources have gathered, selectively inherited from some existing different sources, together with field survey for updating the lack of information to create the new digital soil maps. The information about soil associating with spatial data and boundary is determined at the scale of 1:25,000. The data of soil classification in the study area has been using FAO-UNESCO classification method. All of the soil classes and soil characteristics displayed below have direct or indirect impacts to the current cultivation system and the evaluation of land suitability for the given agricultural crops. Furthermore, soil information could offer creating a new model for plant production, assisting local land-users to fulfill their obligation to provide information on the use of cultivable land, improving methods for direct communication with administrative authorities as well as extension services. According to soil classification methods of FAO-UNESCO, the study area has 6 major soil groups, with 12 soil units and 18 sub-units (Table 3.1 and Appendix 3.2). Soil units and sub-units of soil are examined, managed and stored in the GIS software. The major of soil groups and 18 sub-units of soil are presented in Figure 3.5 and 3.6. - Arenosols groups (đất cát biển – AR): with the area of 3127.86ha, accounting for 22.45% of the investigated land. There are three main units of soil involving Luvic Arenosols with 560.52ha (4.06%), Autric Arenosols with 1759.14ha (12.62%) and Cambic Arenosols with 808.20ha (5.80%). This group has 4 sub-units of soil, including Eutri luvic Arenosols, Hapli eutric Arenosols, Endogleyi eutri cambic Arenosols, and Dystri cambic Arenosols. The Arenosol group is formed by the deposition of raw materials originally from rivers and the sea. This group of develops the transformation process as the accumulation of iron or acidification. It concentrates mainly on the coastal communes of Quang Vinh, Quang Dai, Quang Hai, Quang Thai, Quang Luu, Quang Tho, Quang Thanh, and Quang Nham. This soil group is used for cultivation of rice, annual crops, short-term industrial crops, and growing casuarina to protect the invasion of the sand from the sea. - Salic Fluvisols group (đất mặn FLs): with the area of 264.63ha, occupying for 1.90% of the investigated area. It distributes near the coast, estuary of Quang Khe, Quang Trung, and Quang Thach communes. This group is formed by the affected circuit of salt water and overflow water from ocean. The group is divided into two soil units including Hyposalic Fluvisols with 234.81ha (1.68%) and Hypersalic Fluvisols with 30.42ha (0.22%) and two sub-units of soil including Epigleyi hyposalic Fluvisols and Epigleyi hypersalic

54

Characteristic of the Study Area

Fluvisols. Currently, it is used for growing rice, salty-tolerance plants and brackish water aquaculture.

Table 3.1: Soil classification in Quang Xuong District (Fertilizer, 2011 and field work)

Area Percent Vietnamese name FAO-UNESCO name Symbol (ha) (%) 1. Đất cát 1. Arenosols AR 3129.86 22.45 1.1. Đất cồn cát trắng, vàng trung tính ít 1.1. Eutri luvic Arenosols AR e 560.52 4.02 chua L 1.2. Hapli eutric 1.2. Đất cát trung tính ít chua điển hình AR h 1759.14 12.62 Arenosols e 1.3. Đất cát trắng có tầng đốm gỉ trung tính 1.3. Endogleyi eutri AR eg 750.06 5.38 ít chua glây sâu cambic Arenosols b 2 1.4. Dystri cambic 1.4. Đất cát trắng có tầng đốm gỉ chua AR d 58.14 0.42 Arenosols b

2. Đất mặn 2. Salic Fluvisols FLs 265.23 1.90 2.1. Epigleyi hyposalic 2.1. Đất mặn ít glây nông FL g 234.81 1.68 Fluvisols sw 1 2.2. Epigleyi hypersalic 2.2. Đất mặn nhiều glây nông FL g 30.42 0.22 Fluvisols shr 1 3. Đất phù sa 3. Fluvisols FL 9358.29 67.12 3.1. Hapli eutric 3.1. Đất phù sa trung tính ít chua điển hình FL h 520.56 3.73 Fluvisols e 3.2. Đất phù sa có tầng đốm gỉ trung tính ít 3.2. Hapli eutri cambic FL eh 565.47 4.06 chua điển hình Fluvisols b 3.3. Đất phù sa có tầng đốm gỉ chua điển 3.3. Hapli dystri cambic FL dh 608.31 4.36 hình Fluvisols b 3.4. Dystri cambic 3.4. Đất phù sa có tầng đốm gỉ chua FLbd 864.18 6.20 Fluvisols 3.5. Dystri epigleyi 3.5. Đất phù sa đốm gỉ glây nông chua FL gd 1138.14 8.16 cambic Fluvisols b 3.6. Hapli dystric 3.6. Đất phù sa chua điển hình FL h 1274.76 9.14 Fluvisols d 3.7. Dystri Gleyic 3.7. Đất phù sa glây chua Flgd 1294.38 9.28 Fluvisols 3.8. Eutri Gleyic 3.8. Đất phù sa trung tính ít chua FLge 717.03 5.14 Fluvisols 3.9. Đất phù sa glây trung tính ít chua điển 3.9. Hapli eutri epigleyic FL e h 2375.46 17.04 hình Fluvisols g 4. Đất glây 4. Gleysols GL 453.6 3.25 4.1. Hapli dystric 4.1. Đất glây chua điển hình GL h 453.6 3.25 Gleysols d 5. Đất xám 5. Acrisols AC 517.5 3.71 5.1. Đất xám điển hình có tầng bạc trắng 5.1. Albi haplic acrisols ACh al 517.5 3.71 6. Đất xói mòn mạnh trơ sỏi đá 6. Leptosols LP 219.33 1.57 6.1. Đất xói mòn mạnh trơ sỏi đá chua điển 6.1. Hapli dystric LP h 219.33 1.57 hình leptosols d Sum 13,941.81 100

- Fluvisols group (đất phù sa – FL): with the area of 9358.29ha, occupying for 67.12% of the investigated area. This group mainly spreads in Quang Phu, Quang Tam,

55

Characteristic of the Study Area

Quang Đong, Quang Dinh, Quảng Tan, Quang Trach, Quang Yen, Quang Hoa, Quang Hop, Quang Ninh, Quang Phong, Quang Nhan, Quang Long, Quang Van, Quang Ngoc, Quang Binh, Quang Truong, Quang Khe, and Quang Chinh communes. It is formed by the consodidation of the material deposition from rivers, seas and lakes every year. The group has been divided into 4 soil units including Autric Fluvisols, Dystric Fluvisols, Cambic Fluvisols, and Gleyic Fluvisols with 9 sub-units of soil involving Dystri cambic Fluvisols, Hapli dystric Fluvisols, Hapli eutric Fluvisols, Dystri Gleyic Fluvisols, Eutri Gleyic Fluvisols, Hapli dystri cambic Fluvisols, Hapli eutri cambic Fluvisols, Dystri epigleyi cambic Fluvisols, and Hapli eutri epigleyic Fluvisols. Fluvisols group has been using for cultivation of rice, annual crops, vegetables and industrial crops.

Figure 3.5: Soil classification map of Quang Xuong district (created from the source of Thanh Hoa Department of Natural Resources and Environment Management and field work)

56

Characteristic of the Study Area

- Gleysols group (đất glêy – GL): with the area of 453.60ha, accounting for 3.25% of the investigated area. It locates in Quang Van, Quang Vong, and Quang Ngoc communes. The group is mostly concentrated on low and hollow land of the terrain which is waterlogged all the time or periodically. It is used for cultivation of wet rice combining with fresh water aquaculture. This group has one soil unit that is Dystric Gleysols and one sub-unit is Hapli dystric Gleysols.

Figure 3.6: Sub-units of soil classification in Quang Xuong district (created from the source of Thanh Hoa Department of Natural Resources and Environment Management and field work).

- Acrisols group (đất xám – AC): With the area of 517.50ha, accounting for 3.71% of the investigated land, distributes throughout the hills and mountains of Quang Dong, Quang Tan, Quang Thinh, and Quang Duc communes and primarily develops on ancient alluvial. This group accumulates clay at B layer with the capacity of cation exchange <

57

Characteristic of the Study Area

24meq per gram of clay and the base saturation < 50%. One main soil unit in this group is Haplic Acrisols and one sub-unit of soil is Albi haplic acrisols. The group is used for cultivation of annual crops, plantation of forest and agroforestry as well. - Leptosols group (đất tầng mỏng – LP): with the area of 219.33ha, accounting for 1.57% of the investigated land. It locates in hilly and mountainous areas of Quang Hop, Quảng Linh, and Quang Loi communes. Being influenced by the dramatic erosion and strong washing process, the soil has a thin cultivating layer. There is one soil unit in this soil group that is Dystric Leptosols and one sub-unit of Hapli dystric leptosols. This soil group is suitable for indigenous forest plants or planting meadows for cattle-breeding. Most of the land area in Quang Xuong has been formed by the process of alluvial sedimentation from the river systems and the sea. Due to the long-term monoculture cultivation of rice, most soil has become acidic and short of nutrient. Therefore, it is necessary to choose the appropriate cropping patterns and to build a reasonable fertilizing regime for crops in order to increase efficiency of land use, while ensuring sustainable production. Soil classification map showed that although the total land area of Quang Xuong District is not quite large. However, the study area has diversity of soil types, especially in the main soil units and sub-units. This is a principle for expecting feasible results of various agricultural crops. The classification also illustrates that almost types of soil can be used to cultivate annual and short industrial crops, but with different level of suitability. Some soil types contain certain obstacles which will be further assessed in the following chapters.

3.1.4.2 Physical and chemical characteristics of major soil groups - Arenosols: the average percentage of organic matter content is very poor with 0.35%. The total phosphorus in soils ranges from 0.01 to 0.06%, the average is 0.035%. The total potassium in the soil layers fluctuates from 0.04 to 1.00% and the average is 0.50%. Phosphorus availability is medium rank, about 1.5 – 31.0mg per gram of soil. Potassium availability is very poor rank, about 0.8 – 10mg per gram of soil. Total cation exchange of alkalinity in soil fluctuates from 0.8 to 8.0meq per 100gram of soil; the average is 4.4meq per 100meq per 100gram of soil, accounting for 80% of cation exchange capacity (CEC). The average of CEC is about 6.2meq per 100gram of soil. Base saturation is ranged from

38% to 83%. This soil group is acidity, with pHKCL fluctuating from 3.9 to 5.9, the average of 4.9. The soil texture of the group is sandy composition throughout the profile. - Salic Fluvisols: the average percentage of organic matter content is about 2.7%. The total phosphorus in soils is at a medium level, fluctuating from 0.08 – 0.10% and average of 0.09%. The total potassium in the soil layers is at medium degree, which ranges

58

Characteristic of the Study Area from 1.25 – 1.62% and the average is 1.43%. Phosphorus availability fluctuates from 10 to 27meq per 100gram of soil, at a medium level. Potassium availability is poor, fluctuating from 6.0 to 13.0meq per 100gram of soil. Total cation exchange of alkalinity is from 8.0 – 13meq per 100gram of soil, the average is aboul 10.5meq per 100gram of soil, occupying for 70% of CEC. The average of CEC is 1.5meq per 100gram of soil. This group has ranges of base saturation from 78% to 87%. This soil group has slight acidity, its pHKCL fluctuating from 4.98 to 5.92, the average of 5.45. The soil texture of the group is clay loam to clay. - Fluvisols: the average percentage of organic matter content is about 2.5%. The total phosphorus in soils fluctuates from 0.02 – 0.15%, the average of 0.08%, getting at medium rank. The total potassium in the soil layers is at medium rank, which ranges from 0.2 – 1.9% and the average of 1.05%. Phosphorus availability is medium, fluctuating from 1.0 to 28meq per 100gram of soil. Potassium availability is poor, fluctuating from 3.5 to 12.0meq per 100gram of soil. The total cation exchange ranges from 4.8 to 12.0meq per 100gram of soil, the average of 8.4meq per 100gram of soil accounting for 80% of CEC. The average of CEC is 15.5meq per 100gram of soil. Base saturation ranges from 26% to 86%. This group is acidity, with pHKCL fluctuating from 3.7 to 6.2, the average of 4.9. The popular texture of soil is clay loam to clay. - Gleysols: the average percentage of organic matter content is medium, fluctuating from 1.1 – 2.6%, with the average of 1.85%. The total phosphorus in soils is poor, ranging from 0.04 – 0.06% and the average of 0.05%. The total potassium in the soil is medium, fluctuating from 1.45 – 2.0% and the average of 1.72%. Phosphorus availability ranges from poor to medium, fluctuating from 4.5 to 19meq per 100gram of soil. Potassium availability is poor, changing from 1.5 to 8.0meq per 100gram of soil. The total cation exchange ranges from 7.0 to 10.0meq per 100gram of soil and the average of 8.5meq per 100gram of soil accounting for 80% of CEC. The average of CEC is from 15.0meq per 100gram of soil. Base saturation ranges from 45% to 84%. This group is acidity, pHKCL fluctuating from 3.9 to 6.2, the average of 5.05. The group has soil texture of sandy loam to clay. - Acrisols: the average percentage of organic matter content of surface is very poor, fluctuating from 0.4 – 0.8%, with the average of 0.6%. The total phosphorus in soils is poor, ranging from 0.02 – 0.05% and the average of 0.035%. The total potassium in the soil is poor, fluctuating from 0.32 – 0.70% and the average of 0.51%. Phosphorus availability is poor, fluctuating from 1.5 to 5.0meq per 100gram of soil. Potassium availability is poor, changing from 2.4 to 10.0meq per 100gram of soil. The total cation exchange ranges from 3.0 to 5.0meq per 100gram of soil. CEC is fluctuated from 7.0 to 10.0meq per 100gram of soil. Base saturation ranges from 35% to 57%. This group has a

59

Characteristic of the Study Area

high level of acid, its pHKCL fluctuating from 3.9 to 4.7, the average of 4.3. The soil texture of the group is sandy loam to clay loam. - Leptosols: the group has an average percentage of organic matter content, it fluctuates from 1.63% to 2.74% and the average of 2.14%. The total phosphorus in soils is ranged from 0.09 – 0.14% and the average of 0.12% amounting at a medium level. The total potassium in the soil is fluctuated from 0.72 – 2.67% and the average of 2.3% getting at high rank. Phosphorus availability in the soil layers varies from 2.4 to 16.8 and the average of 7.0meq per 100gram of soil, evaluating at poor rank. Potassium availability is ranged from 4.1 to 40.5meq per 100gram of soil and the average of 21.02meq per 100gram of soil, getting at medium level. The total cation exchange ranges from 3.4 to 12.8meq per 100gram and of soil. The average of CEC is 16.88meq per 100gram of soil, getting at medium level. Base saturation ranges from 28% to 45%. The pHKCLof the group is changing from 3.70 to 4.56, very acidity. The soil texture of the group is sandy loam. The average characteristics of the major soil groups and the soil texture of sub-units of soil are presented in Table 3.2 and Figure 3.7

Table 3.2: Fertility of soil group in Quang Xuong district

Total Available Average Exchange concentration concentration CEC BS Soil group pHKCL OM cation (meq/100g) (meq/100g) (meq/100g) (%) (%) (meq/100g) P2O5 K2O P2O5 K2O Arenosols 4.90 0.35 0.035 0.50 16.25 5.40 4.40 6.20 38.0 – 83.0 Salic Fluvisols 5.45 2.70 0.09 1.43 18.50 9.50 10.50 1.50 78.0 – 87.0 Fluvisols 4.90 2.50 0.08 1.05 14.50 7.25 8.40 15.50 26.0 – 86.0 Gleysols 5.05 1.85 0.054 1.72 11.75 4.75 8.50 15.00 45.0 – 84.0 Acrisols 4.30 0.60 0.035 0.51 3.25 6.20 4.00 8.50 35.0 – 57.0 Leptosols 4.56 2.14 0.12 2.30 7.0 21.02 8.60 16.88 28.0 – 50.0 Source: (Fertilizer, 2011)

5.44% 3.25% Silty Loam 2.87% Clay loam 8.72% Loam

37.00% Loamy sand 7.68% Sandy loam 25.2% Coarse sand Silty clay loam 9.85% Silty clay

Figure 3.7: The soil texture for sub-units of soil in the Quang Xuong District

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Characteristic of the Study Area

In the study area, the soil textures involves coarse sand with 400.41ha (2.87%); sandy loam with 1215.99ha (8.72%); loamy sand with 1070.10ha (7.68%); loam with 3512.70ha (25.2%); clay loam with 1373.67ha (9.85%); silty loam with 5156.28 (36.98%); silty clay loam with 759.06ha (5.44%); and silty clay with 453.60 (3.25%). Almost types of soil texture in the study area can be used for cultivation of annual crops excepted coarse sand.

3.2 Social-economic conditions 3.2.1 Population and labour force The population density and its distribution is an important factor, deciding effects of the society on the ecological system. The environment is less affected in the low population density than in the high density. In the study area, most dwellers live by the main roads and the communes‘ center. According to the Division Statistic Office of Quang Xuong in 2012 (Table 3.3), the district has 40 communes and one town with 272,269 inhabitants, in which the man is 133,412 (49%), the women make up 138,857 (51%). Its density is 1,196 people per square kilometer. People in working age amount to 159,277, in which 122,130 people work in agriculture (76.68%), 18,434 people in industry (11.57%), 11,866 laborers in services (7.45%), and 6,847 laborers in other occupations. The labour force of Quang Xuong is relatively abundant and the annual agricultural leisure is rather long. After the ended farming season, most of agricultural labour force is jobless. Offering sufficient and permanent jobs for this labour force is a big concern, because most of them are unskilled and their degree of understanding of technology and science is also limited. A lot of young people are looking for jobs far away from their home so many elderly people still do farming. This issue is the biggest difficulty in the application of latest technology for agricultural development.

Table 3.3: Population and labor force information in the study area

Indexes Units Year of 2012 Natural area Km2 227.63 A number of communes Commune 41 Population Person 272,269.00 Density of population Person/km2 1,196.00 Rate of population % 9.0 Total labour force Person 159,277.00 Agroforestry labour Person 122,130.00 Industry labour Person 18,434.00 Service labour Person 11,866.00 Other labour Person 6,847.00 Poverty household proportion % < 30

Source: Division Statistic Office of Quang Xuong (2013)

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3.2.2 Economic growth According to the Division Statistics Office of this district, the economy of Quang Xuong has been developed comprehensively and relatively rapidly rate of the growth (Table 3.4). The economic structure is shifting in the right direction; the infrastructure has developed rapidly and production relations have been established. Together with the economic growth rate of the province, Quang Xuong economy has continuously developed. The economic growth rate has improved; the average of the period of 2008 to 2012 was 14.3%, in which the growth rate of industry and construction was 25.56%, commercial sector and services was 19.8%, agroforestry was 6.8% (Table 3.4).

Table 3.4: The average growth rate of period of 2008 - 2012

Years Percentage 2008 2009 2010 2011 2012 Indexes (%) The total value of 1.528.723 1.674.008 2.081.311 2.320.752 2.617.994 14.3 production Agroforestry and 587.580 557.560 647.441 661.568 669.729 6.8 fishery Industry and 262.747 281.909 449.114 534.445 598.578 25.56 contruction Trade in Services 678.369 834.539 984.756 1.124.739 1.349.687 19.8

Unit: million VND. Source: Division Statistic Office of Quang Xuong (2013) - The economic structure (Table 3.5 and Figure 3.8): The economic structure of the district has shifted towards positively. The annual proportion of industry, construction and service sectors in the structure has increased, while the total value of agricultural GDP has decreased. However, in terms of absolute value of agricultural economy which has been still growing fast. Table 3.5: The structure of economy in Quang Xuong in the period of 2008 - 2012 Indicators 2008 2009 2010 2011 2012 Average Economic structure (%) 100 100 100 100 100 100 1. Agroforestry and fishery (%) 45.3 44.5 40.4 40.0 36.2 41.28 2. Industry and construction (%) 23.4 24.0 27.0 27.5 29.6 26.3 3. Services (%) 31.3 31.5 32.6 32.5 34.2 32.42

Source: Division Statistic Office of Quang Xuong (2013)

The table has shown that Quang Xuong is an agricultural district although the economic value of agroforestry and fishery has gradually decreased over the years. However, it still remains an important position, especially in the social aspect of the district.

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Characteristic of the Study Area

50 45 40 1. Agroforestry and fishery 35 30 2. Industry and construction 25 3. Services 20

15 10 5 0 2008 2009 2010 2011 2012

Figure 3.8: The transfer of economic structure in Quang Xuong 2008 – 2012

- Current agricultural production: According to the annual report of the district, agricultural production has developed fairly equal with high growth rates of farming, livestock and fishery. The district has focused on appropriate investments in plant growing like implementation of regional planning, merging and changing the land parcels, applying scientific and technological advances into production; improvement of the construction of irrigation and transport systems. The district has put the new crop varieties such as hybrid rice and hybrid maize into production. Table 3.6: Some agricultural development indicators in the period of 2008 – 2012

Indicators 2008 2009 20010 2011 2012 Average Agricultural production 1,067,709 1,163,015 1,841,874 1,876,835 2,177,039 1,625,294 value (million VND) Food output (ton) 126,380 129,164 128,098 121,146 129,898 126,937 Paddy 116,010 119,749 121,298 114,268 121,752 118,612 Maize 10,370 9.415 6.800 6,878 8,146 8,321 Average food (kg per person) 491,00 503,2 496,1 464,00 485,80 488,02 Production value per 47,35 50,83 66,1 67,00 78,60 61,97 ha (million VND)

Source: Division statistic office of Quang Xuong (2013)

The results from the Table 3.6 have shown that food output was stable over the years with the average of 126,937 thousand tons. This number meant that the district has nearly 50 thousand tons of food commodities. The annual production value of agriculture has increased from 47.35 million VND per hectare 2007 to 78.6 million per hectare in 2012. These consequences demonstrate that the district have strongly concentrated on making investment policies for the development and advance of agricultural income.

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Characteristic of the Study Area

- Industry, home craft industry and construction: as thriving sectors, the growth rate from 2008 to 2012 has reached at 25.56%. In the past years, Quang Xuong has paid more attention to developing and restoring traditional crafts such as bamboo and rattan products; weaving sedge mats together with developing new crafts simultaneously such as crocheted yarn, spun from jute, thereby addressing thousands of workers with stable jobs. The value of exports in 2012 reached 15 million USD, twice as big as the export value in 2008 (7.5 million USD). The values of some major items such as sedge mat, clothes, fish sauce, and brick building have developed quickly and increased over the years. The annual industrial production values of non-state have significantly increased. The infrastructures of the district have received continuously synchronous investment, particularly the transport and the irrigation systems. Many social welfare facilities like schools, clinics, hospital, offices of the communes, and cultural constructions have been built. Implementing the rural traffic development project under the state and people work together, so far 100% inter-village routes and inter-commune roads are asphalt spread and concreted. The total investment period of 2008 - 2012 reached 2,847 billion VND (Quang Xuong Statistic Office, 2013).

- Services: diverse and abundant development of the service branches has met the consumers‘ demands. The growth rate of services sectors in 2012 reached at 19.8% increased 3.2% compared with 2008. The values of services in 2012 obtained 1,850 billion VND increased 2.8 times compared with 2008. The banks, credit funds systems in the district have basically met the demand of investment capital to develop productions. Postal and telecommunication services have rapidly developed, with the average of 42 telephones per 100 persons, increasing 17 times compared to 2008 figure. Investment in upgrading and developing the power supply system keeps progressing to satisfy the electricity for production and daily life of the people in the locality (Quang Xuong Statistic Office, 2013).

3.2.3 Rural infrastructures situation 3.2.3.1 Present rural transportation system National Highways 1A, 45, 47, and Trans-Provincial Highway 04 run through Quang Xuong, creating favorable conditions for the district in terms of economic, cultural and social exchanges with the other districts of the province and with other provinces in the whole country. The main roads in the district are asphalted or concreted with strong bridges, so they are really advantageous for trafficking. Inter-village and inter-communal routes are quite completed. The waterway traffic has also developed, which is beneficial for the importation and exportation between the districts in Thanh Hoa Province.

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Characteristic of the Study Area

3.2.3.2 Irrigation and drainage system Bac Canal of Chu River system is the main source of irrigation system, which has solidified and effectively performed. There are 49 pumping stations and 76 electric pumps with a capacity of 1000 - 1500m3 per hour in the entirely district. The interior canals are also concretized to improve the capability of irrigation. However, the irrigation system of the district is only powerful enough to supply water for 80% agricultural area and 20% of the cultivated land has to depend on natural water like water from rain and river. The river drainage systems of the district are including: River Quang Chau, Do, Ly, Hoang, and Rao. Although the drainage systems of the district are fairly distributed and reasonable to improve the ability of drainage, but the problem of water shortage or waterlogging still happens when there is heavy and prolonged rain due to the natural geography. 3.2.3.3 Electricity system Quang Xuong has developed power grid earlier than other districts in the province. The electricity source is mainly taken from the national power grid. Presently, the district has 138 electric stations with total capacity of 26000KVA and with more than 3 substations of transformer station per commune. 100% of the households in the district have access to electricity. The power is mostly used for lightening and agricultural production. However, the energy utilization rate is used for industrial production and handicraft still low. 3.2.3.4 School systems Education has been placed in priority until now. 100% of the communes and the town have secondary and primary schools. 98% of the pupils at schooling age go to school. There are 8 high schools in the whole district, one Center for continuing Education and vocational training, and 4 professional training schools. The equipment is synchronized to satisfy the demand of entertainment and learning needs. The percentage of students enrolling into universities has increased over years. The qualities of teachers in the district evenly have developed and teacher staffs are regularly retrained and standardized. Nevertheless, the number of kindergartens is still small. There is still a shortage of tools supporting learning and teaching is still lacking in some communes; thus, this has affected the quality and pupils‘ learning outcomes these places. 3.2.3.5 Health services The district has been focusing on developing healthcare servicestowards the socialization of both disease prevention and treatment. Medical facilities are more and more increasingly investing in quantity and quality. The qualifications of the medical staffs

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Characteristic of the Study Area are improving over years, initial fundament to meet the demand of the local examination and treatment. In addition, some important and the large medical facilities of the province are located in this district including maternity, national tuberculosis hospitals and epidemic prevention center. This favorable condition has significantly contributed to diagnosis and treatment not only for local people but also for the whole province. On contrary, nurses‘ knowledge in some local clinics is still limited and outdated equipment is a cause of difficulties in counseling as well as providing medical care for the people 3.3 Current status of land utilization of 2012 According to statistics in 2012 (Table 3.7 and Figure 3.9), the total natural area in Quang Xuong is 22,780.12ha, in which used land for different purposes is 22,186.14 ha, accounting for 97.39% of the natural area and unused land is 593.68ha making up 2.61%. Most natural land has been used and unused land has been slightly proportional. The agricultural land per capital is 470.57m2. Land for agricultural production is still fragmented, un-concentrated. This issue is quite difficult for concentrated management and intensive investment. The local farmers are expecting to have proper policies from the provincial government for changing and redistributing the agricultural land to obtain the bigger and more advantageous cultivation plots.

Table 3.7: Current status of land use of 2012

Order Indicators Area (ha) Percent (%)

Total natural area 22,780.12 100.00 1 Agricultural land 12,812.10 56.24 1.1 Agricultural production 11,228.63 49.29 1.1.1 Annual crop land 10,833.60 47.56 1.1.1.1 Paddy land 9,483.13 41.63 1.1.1.2 Grazing land 48.57 0.21 1.1.1.3 Other annual crop land 1,333.57 5.85 1.1.2 Perennial crop land 390.03 1.71 1.2 Forest land 309.34 1.36 1.2.1 Production forest land 173.54 0.76 1.2.2 Protective forest land 135.80 0.60 1.3 Aquacultural land 1,220.82 5.36 1.4 Salty land 26.64 0.12 2 Non-agricultural land 9,374.34 41.15 2.1 Inhabitant land 3,674.98 16.13 2.2 Specially utilization land 4,449.66 19.53 2.3 Religious land 12.83 0.06 2.4 Cemetery land 309.59 1.36 2.5 Water surface 927.28 4.07 3 Unused land 593.68 2.61 Source: Division of Natural Resources and Environment Management of Quang Xuong (2013)

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Characteristic of the Study Area

Figure 3.9: Map of land use status in 2012 of Quang Xuong District. Source: Departm ent of Natural Resources and Environment Management of Thanh Hoa Province

Figure 3.10: Structure of land use of 2012 in Quang Xuong District

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Characteristic of the Study Area

Agricultural land: The land is used for the agricultural purposes like farming, livestock, and aquaculture, etc. The area of agricultural land of Quang Xuong District in 2012 was 12,812.10ha, amounting for 56.24% of the total natural area. The farmland of the district was mainly exploited for planting annual crops (10,833.60ha). The largest of agricultural land was used for growing paddy rice with 9,483.13ha; other annual crops made up 1,333.57ha; area for perennial and grazing was 390.03ha and 48.57ha. The area of aquaculture and salt production was 1,220.82ha and 26.64ha, respectively, accounting for 5.36% and 0.12% of the natural area. In coming years, the area of agricultural crops is expected to cut down on account of residential land expansion and the encouragement and investment for industrial development. In 2012, the area of forest land was 309.34ha, accounting for 1.36% of the total natural land, in which used land for economic purposes was 173.54ha and for specialized was 135.80ha. The forest in the Quang Xuong District was rather poor, low in density and forest trees were mainly casuarina. It was mostly planted along the coastline to protect the farmland from encroachment of sand from the sea. In recent years, the area of newly planted forest has not grown a great deal, and the exploitation of unused land for afforestation did not increase. Nevertheless, as anticipated, the area of forest will increase in the next few years due to supporting policies from the provincial government for dealing with global climate change.

Figure 3.11: Structure of agro-forestry utilization in the study area

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Characteristic of the Study Area

Land for residential purpose: current area was 3,674.98ha, accounting for 16.13% of the total natural land of the district. According to the annual report of land use of Quang Xuong, the used land for inhabitance is increasing over the years because the causes of growth in the population and the number of households separated requires more land for building houses. Specialized land: the land was mainly used for social welfare buildings, road systems, bridges, markets, schools, hospitals, etc. Current area is 4,449.66ha, amounting to for 19.53% of the natural land. The area for specialized function of the district was also increased thanks to the economic and rural development and the employment of part of the other land type for welfare construction in the whole district. Unused land: in 2012, unused land of the district was 593.68ha, accounting for 2.61% of the total natural land. This type of land area decreased every year and was being exploited for many aims, especially agro-forestry and fishery one. Most of unused land was the plain. However, with proper evaluation and investment strategies together with applying advantageous techniques, this land will become a big potentiality for expanding agro-forestry land of production in the near future. In general, with the structure of agricultural land as mentioned above the district can grow food, short industrial crops such as groundnut, sesame, soybean, fruit trees, and especially for planting vegetation. Simultaneously with existing soil structure, a large proportion of agricultural land but per capita only reached 470.57m2 per person, this is still lower than the average of the whole province 681m2 per person (THSO, 2012). The limitation of coastal sandy land is low nutrients and water-holding capacity so that it is a strongly important need to have regional planning strategy, implementation of land concentration and exchanging blocks for development of agricultural production in the near future.

3.4 Cropping system and economic efficiency of some main crops 3.4.1 Cropping system There is no structured methodology to choose cultivated land use type for a certain area. The FAO guidelines discover different factors that examine alternative land utilization, namely, existing land utilization, climate conditions, physical and chemical characteristics of soil, and socio-economic status necessary for their success. Specifying land utilization types permits the identification of specific requirements of each land utilization one and associating with how the evaluated land will meet these requirements (FAO, 1976, 1983; Beek, 1978b).

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Characteristic of the Study Area

Research of cropping systems plays an important role for land use planning in general and for agricultural land use in particular. It is significant for the development of concentrated commodity production of agricultural crops. In 2012, the total area for annual agricultural production is 10,833.60ha. By conducting the field investigation combining with its results, interviewing farm households and yearly reports from the Division of Agriculture and Rural Development of Quang Xuong, the status quo of cropping system is arranged into three main cultivated reasons as presented in table 3.8. Table 3.8: Yield, area of main annual crops in Quang Xuong District (fieldwork and yearly data statistics office of Quang Xuong, 2012)

Annual cultivated reasons Annual Spring season Summer season Winter season Crops output Area Yield Area Yield Area Yield (ton) (ha) (ton/ha) (ha) (ton/ha) (ha) ton/ha) Maize 255.73 4.8 399.78 4.5 1207.92 4.5 8,462.15 Potato 57.12 10.5 - - 70.45 11.4 1,402.89 Sweet potato 90.84 7.4 134.65 6.8 483.12 7.0 4,969.68 Soybean 50.41 1.6 96.23 1.4 17.32 1.3 237.894 Groundnut 405.18 2.6 50.84 2.2 550.47 2.1 2,321.30 Rice 8,087.16 6.4 8,483.13 6.0 - - 102,656.60 Green pepper 96.11 14.5 125.67 12.3 - - 2,939.34 Sesame 123.36 0.91 168.72 1.0 - - 274.229 Tobacco 328.75 10.1 - - - - 3,320.38 Sedge 450.43 6.9 527.00 6.2 - - 6,375.37 Jute 15.00 2.9 - - 15 2.4 79.5 Sugarcane 75.18 40.2 - - - - 3,022.24 Vegetables 620.56 16.1 512.54 13.4 1985.56 12.7 42,075.66 Other crops 90.22 - 15.36 - 16.08 - -

Annual food crops: the major food crops in the study are included paddy rice, potato, sweet potato, and maize. The total area of rice field is 9,483.13ha (table 3.7), in which land with irrigation advantages can be grown two-rice season a year and whereas lands without irrigation advantages and mostly depending on rainy source can use to cultivate one-rice season. Rice is mainly cultivated in the places of hollow and low terrain which can be supplied water from irrigation systems. Its annual average yield is reached at 6.2ton per ha with 102,656.60 tons of the total output. The area of its plots is small and ragged. A lot of household have more than 3 fragmented plots of less than 500m2, so it is a cause of a big difficulty for intensive farming like irrigation, field care, harvesting and influence to rice production in both of quantity and quality. Other food crops including maize, potato and sweet potato are grown in almost communes and located in agricultural fields of low irrigation ability. The total area of these crops is 2699.61ha a year with

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Characteristic of the Study Area

14,834.72 tons of annual output. The rice production is often used for the daily feeding while the production of maize, sweet potato and potato are used for sale. Furthermore, a strong crop group is vegetables; the total area of cultivation is 3,118.66ha providing 42,075.66 tons of output. Their productions are provided to Thanh Hoa City, Sam Son town and the people in the province to increase local farmers‘ incomes. Annual industrial crops: cultivation of short term industrial crops involves soybean, peanut, sesame, tobacco, sedge, jute, and sugarcane. The area of these crops extends each year because the crops have a high price and there is a market so that they bring relatively high economic consequences for peasants. Sedge, jute and sugarcane are grown in order to supply raw materials for domestic production and exports. Most of the annual crops are cultivated in flat and high flat topography of the study area where the irrigation condition is initiative. However, the local farmers have to take care of these crops by using manual labour and hand irrigation due to the insufficient investment, limitation of intensive cultivation‘s technique and knowledge the production yield is not high.

Figure 3.12: Maize and paddy rice cultivation in the Quang Xuong

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Characteristic of the Study Area

3.4.2 Economic efficiency of the main annual crops Economic efficiency of the main yearly agricultural crops of the district has calculated through indicators such as productivity, yield, total cost of material, labor cost, the total value of the product, mixed income, and mixed income per intermediate expenditure or mixed income per total expenses of investment. Its criteria are calculated based on the survey results, interviews and the reports of district's agricultural land use. This is the basis for resolving the dispute, choice of crops in a specific region. The general principle is choose the types of land use which have the high total value of the product or high mixed income and high capital efficiency with low input costs. In order to facilitate the evaluation and select the land utilization type, economic indicators are divided into 3 levels: high, medium and low efficiency. This hierarchy is relatively meaningful and only applied to the specific conditions of the Quang Xuong District. The results of economic efficiency of the given land utilization types are presented in Table 3.10 and Appendix 3.3.

Table 3.9: Hierarchical level of economic efficiency evaluation

Mixed income MI/TE Symbol Level (million VND) (Time) H High > 20.0 > 1.5 M Medium 15.0 – 20.0 1.0 – 1.5 L Low < 15.0 < 1.0

The results from table 3.10 show that land utilization of rice have the lowest mixed income (12,339 thousand VND) and lowest capital efficiency (0.87). Land utilization of vegetables, soybean, green pepper, sedge, and jute are recorded at the high level of both mixed income and capital efficiency, in which sedge crop is reached at the highest level with mixed income of 36,705.17 thousands VND and capital efficiency of 2.23. Mixed income of sweet potato and potato crop reaches a low level of 12,876.93 and 13,052.33 thousands VND, respectively. Like paddy rice, despite low economic benefits these crops are still the main yearly crops in the district because they supply the needs of food for local people and ensure food security for the entire region. Besides, these crops are also encouraged by local and provincial government policies to develop Quang Xuong District as a key agricultural production of the Thanh Hoa Province. Although tobacco plant has high economic benefit with medium level of capital efficiency, it is not recommended development in the future due to lack of the support from society and not consistent with the strategy of health protection for the people of the province. Peanut and sesame crops are recorded at medium level of both financial gain and efficiency of investment, in turn is 18,373.11 thousands VND and 1.25 with groundnut and 15,399.15 thousands VND and 1.47 with sesame.

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Characteristic of the Study Area

Table 3.10: Economic efficiency of some main annual crops

GO IE VA TE MI Labor MI/TE MI/Labor Crops (1000đ) (1000đ) (1000đ) (1000đ) (1000đ) (1000đ) (Time) (1000đ)

Rice 33,751.18 7,299.25 26,451.93 14,112.93 12,339.00 138.00 0.87 88.97 Maize 37,409.52 6,944.21 30,465.31 12,897.45 17,567.86 130.50 1.36 135.48 Sweet potato 29,011.08 5,590.08 23,421.01 10,544.08 12,876.93 121.00 1.22 106.34 Potato 31,546.47 6,849.70 24,696.77 11,644.44 13,052.33 131.00 1.12 99.64 Vegetables 53,361.00 7,869.54 45,491.46 16,832.53 28,658.93 212.00 1.70 135.18 Groundnut 40,131.37 7,032.28 33,099.09 14,725.98 18,373.11 158.00 1.25 116.29 Sesame 30,529.78 4,659.74 25,870.05 10,470.90 15,399.15 132.50 1.47 116.55 Soybean 39,090.02 6,315.77 32,774.25 12,407.21 20,367.04 143.00 1.64 98.56 Green pepper 77,878.42 19,357.07 58,521.35 22,484.03 36,037.32 247.00 1.60 145.45 Sedge 61,981.30 8,842.50 53,138.80 16,433.63 36,705.17 270.00 2.23 135.95 Jute 54,471.64 9,766.68 44,704.96 17,046.21 27,658.75 254.00 1.62 108.89 Tobacco 79,749.58 19,803.07 59,946.51 26,861.25 33,085.26 240.00 1.23 137.86 Other crops 58,367.43 17,268.20 41,099.23 19,806.66 21,292.57 178.00 1.08 119.62 Note: at that time of investigation 1USD = 20.940 VND

3.5 Discussion There are some general summaries of natural, socio-economic status and manufacturing styles of the local farmers in the research area. This section will provide information to clarify what are favorableness and limitations of the ecological conditions including the soils, climates, geography, and infrastructures for cultivation of crops. This is fundamental content for calculation and evaluation of land suitability for potential annual crops in the study area.

3.5.1 Natural conditions The district is located in the coastal region of Thanh Hoa Province with the tropical monsoon climate and relatively flat topology so that it has favorable conditions for growth of different kinds of crops. However, harsh and unpredictable weather often occurs in the rainy season; thus, it brings many difficulties for the area such as flood, storm, even pestilent insect affecting the growth and development of agricultural plants. With diverse river systems, the cultivated land has many branches of streams supplying water agricultural crops. In contrast, in rainy season, fast floods often happen in some certain parts causing serious influencing traffic, gathering and storage of agricultural produce. The study area should pay more concentration on the investment of irrigation and drainage system to adjust water reasonably and exploit water resource for developing agriculture. Furthermore, water holding capacity of sandy soils (Arenosols) is so poor and then partial drought is usually happened in the dry season. This challenge affects the growth and crop yield. Therefore, the district should invest in the water pumps and

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Characteristic of the Study Area developing deep well to exploit underground water resources for both agriculture and daily activities of local people. The area of land used for agricultural purpose in the district is the biggest in comparison with the natural area and other purposes combined with diverse cropping systems. This is very good conditions for the development of concentration commodity of agriculture with high economic value. In addition, the unused land is recorded at 2.61% of natural land. This is a big potential for expanding cultivated land in the future. However, most of the soils here have a shortage of fertility with difficult farming conditions and farmers do not have enough knowledge to apply cultivation techniques and use producing capital appropriately. Agricultural land per capita is low and scattered; agricultural products not competitive in the market; the rate of processing, agricultural products and services is also low. Thus, the possibility of intensive cultivation is not highly effective.

3.5.2 Social-economic and infrastructure conditions Locating close Thanh Hoa City, the study district has been receiving a lot of interest and attention from the government and authorities in all fields. Many supported programs such as projects of upgrading infrastructure (electricity, roads, schools, health clinics, etc.) have been significantly contributed in promoting the development of local economy. Most of the rural road systems have been concreted and asphalted. This offers goods regulating much easier. The economy of the district is not really high developed due to lack of equipment and instrument in agricultural production, the local farmers mainly use manual labors. Those problems are major difficulties for developing annual crop production. On the other hand, the households‘ income is increasing each year, and then they can purchase and invest some agricultural tools, carry out mechanization, apply advanced techniques to improve the production capacity of the cultivated land as well as enhance the quality of agricultural products. The research area has a large proportion of young labor force, strong and experienced farmers. This is advantageous precondition to get rid of poverty, improve the income for the peasants and develop an agricultural production in a large scale. However, a large proportion of untrained laborers and out-of-date cultivating habits affect negatively to the productivity and quality of crops. With diverse cropping systems, the district can fully proactive in selecting suitable plants for the natural specific conditions as well as developing strategy of agricultural commodities consistent with market needs. In contrast, the research area still does not have specific and general plans. Information on agricultural planning is not widely to farmers. Therefore, goods production is still spontaneous and stable markets for agricultural products are not formed in the district.

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Data Sources and Methodology

Chapter 4. Data Sources and Methodology

This chapter describes the methods applied for land suitability evaluation and land cover change in different periods of time in the research area and the data resources used. It also mentions how to collect data, the procedures and pointing out the steps of the study. Furthermore, the chapter presents the criteria used for land evaluation of given crops and depicts how the simple limitation, parametric, and AHP method work.

4.1 Data resources used for this study 4.1.1 Spatial data resource The main spatial data resources need for this research including: (1) Landsat5 TM and Landsat8 OLI and TIRS images These images are used for classification and land cover change detection from 1989 to 2013. The data were selected for this study due to it has wide spectral coverage and availability of a high resolution band for enhancing spatial resolution and features. One image of Landsat5 and one image of Landsat8 were used for the analysis (table 4.1). These satellite images were acquired for relatively cloud free (maximum 10%) in both period of time for visual interpretation and on screen digitizing.

Table 4.1: Characteristics of landsat5 TM, Landsat8 OLI and TIRS data

Image Resolution Path Row Date of pass Landsat TM 30.0m (band 1-5 & 7) 127 046 September 11, 1989 Landsat OLI and TIRS 30.0m (band 1-7 & 9) 126 046 September 22, 2013

(2) Soil map (3) Topography map (4) Current land use map of 2012 (5) Infrastructural accessibility maps (6) Other existing spatial databases. The maps for this study were collected and inherited from Department of Natural Resources and Environment Management of Thanh Hoa province and from Quang Xuong district offices. A part of soil map data was inherited from a project of building soil classification map for Thanh Hoa province in 2010 according to FAO-UNESCO soil classification guideline implemented by Natural Resources and environmental Management faculty of Vietnam National University of Agriculture. All the maps are presented at scale 1:25,000. After collecting, all spatial data resources were examined the accuracy and edited

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Data Sources and Methodology to update the new information for soil map of the district by the field working. Coordinate system for these maps is UTM projection, Zone 48 North, Datum of WGS 84.

4.1.2 Attribute data resources The main attribute or non-spatial data sources need for this research involving data accompanying with spatial data above. The attribute data were collected for this research including: (1) The data of topography, location, climate conditions, and natural resources (2) The data of physical, socio-economic, infrastructural condition of the study area (3) The data of population, labor, and land use status of the district (4) The last census of agricultural production and annual crops production (5) Ecological requirements for selected annual agricultural crops. The attribute data associating with the spatial data and the major sources for socio- economic data conditions were gathered and collected from statistical records at Thanh Hoa province, Quang Xuong district, Department of Natural Resources and Environment Management, and other related offices. The data was also gathered by interviewing, discussing with local famers, authorities and experts. After collecting, these non-spatial data was calculated, reclassified, and combined by using Excel software.

4.1.3 Software used for management spatial and non-spatial data (1) MS Excel was used to create the attribute databases to import or export data to GIS environment for analyzing and storing. (2) Envi software version 5.2 was used for image classification and land cover change detection. (3) ArcGIS version 10.2 was also applied to analyze, store, query, outputs, covert the data, and building different kinds of map.

4.2 Study methodology 4.2.1 Methodologies for gathering data The fundamental survey documents, land classification recently used in the area, soil map data examined by researches of Thanh Hoa province in recent years and thematic maps such as administrative map, land use map, existing land planning map were inheritably selected for further analysis. In this study, FAO instructions of land assessment (FAO, 1976, 1985, 1993) and examining cultivation structure is applied in order to arrange the institution for collecting, calculating and evaluating data. Land suitability structure of FAO has been modified for Vietnamese conditions as below, which is used for this research:

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(1) Land suitability orders that indicate kinds of suitability: S (suitable) and N (non-suitable). (2) Land suitability classes that reflect the levels or degrees of suitability within

orders: S1 (highly suitable), S2 (moderately suitable), S3 (marginally suitable),

N1 (currently not suitable), and N2 (permanently not suitable). (3) Land suitability subclasses that point out kinds of limitation factor within classes. For instance: f (fertilizer), d (soil depth), etc. The data of climate, geology, geography, vegetation cover, infrastructure, socio- economy, and information about yearly agricultural crops of the district were collected from different organizations, centers and local offices like Department of Natural Resources and Environment, Department of Agriculture and Rural Development, Division Statistic of the province and the district, university libraries, available literatures, etc. Gathered materials were included yearly reports, the strategies for agricultural development of the state and the district, projects and researches of agriculture in the past as well as at the present. In addition, the interviewing and discussion methods were applied in order to obtain more information about agricultural production, cultivated methods, the preserving and processing of agricultural products. These approaches were also used to select the parameters for suitability evaluation and examine each evaluation factor for given annual crops. Interviewing and discussions have been organized among groups of 8 experts of agricultural land use and management (3 experts of land use planning, 3 experts of soil science, and 2 agronomist specialists) to examine the weight of main criteria and sub- criteria for land potential productivity assessment and land suitability evaluation by applying AHP method. Furthermore, field work observation and monitoring were carried out to edit and update new information to the used maps. Field observation were played an essential part for collecting more information on situation of land cover, crops production, land characteristics, infrastructural status, and taking photographs for this dissertation. Finally, GIS software was applied to overlay and establish databases, land mapping units and land suitability maps for evaluated specific crops in the study area. In this research, thematic maps needed include: organic matter, CEC, pHH2O, sum of exchangeable basic cation, base saturation, soil texture, irrigation, drainage, and soil depth map. All of thematic maps were created and overlaid on ArcGIS software, afterward converted into raster data and reclassified for land evaluation. All maps were displayed at scale of 1:25,000 with UTM projection, Zone 48 North, and datum of WGS 84.

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4.2.2 Methodologies used for analysis and evaluation of land suitability 4.2.2.1 Supervised classification and land cover change detection The most common method for the determination of change detection is comparative analysis of spectral classifications for different times (Güler et al., 2007). The principle of classification based on the land cover and land use classification system developed by A. Anderson et al., (1976) was applied first. Maximum likelihood approach was independently used for classification stage for each image to generate the thematic map of land cover; afterward change detection technique was also applied to examine how land cover change from 1989 to 2013 in Quang Xuong District by comparing independently classified images. Six separable land cover types have been identified in this research as water surface, built- up, agricultural land, forest land, salty land, and unused land (table 4.2).

Table 4.2: Land cover classification scheme

No. Classes Description Area covered by residential, commercial, industrial, public infrastructure and 1 Built-up areas services buildings, transportation, roads, mixed urban, and other urban. Characterized by agricultural area, crop fields, fallow lands, vegetable lands and 2 Agriculture land regularly planted crops. All area of open water with 95% covers of water, including rivers, streams, 3 Water surface lakes, ponds and reservoirs. 4 Forest Area covered by forest with relatively darker green colors. 5 Salty land Area used for salt production 6 Unused land Sandy, rock mountains and other disused areas

Landsat image

Landsat5 Landsat8

Geo-metric correction

Images clipping

Classification

Comparison Accuracy Post classification results assessment

Land cover changes analysis

Figure 4.1: Flowchart of the processing of satellite data

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Data Sources and Methodology

4.2.2.2 Linear Combination Method At present, the study area does not have a standard model of land capability evaluation for agricultural use. Thus, we examine and generate a potential productivity map for agricultural production in the coastal sandy land of Thanh Hoa Province based on the available data and the requirement for farming production. The Linear Combination Method developed by Hopkins (1977) with the help of GIS was used to express land potential for agricultural production. According to the guideline by FAO for land evaluation for irrigated agriculture (1985) and FAO framework (1976) for land evaluation, the model for land potential was modified to suit with certain conditions of the study area. The general process for land potential assessment is shown in Figure 4.2.

Land potential capability assessment for Agriculture use

Soil chemical property Soil Physical property Relative topography

Weight and score Weight and score Weight and score of each criterion of each criterion of each criterion

Soil chemical Soil physical Relative property assessment property assessment topography assessment

Overlay

Map of preliminary land capability for Agriculture use

Expert Field suggestion check

The final map of land capability for Agriculture use

Figure 4.2: The flowchart of land potential assessment for Agriculture use

Selection of factors, variables and database development: It is based on the available data of the research area, the experts‘ opinion and FAO framework (FAO, 1976, 1985) for land evaluation. Three main factors and ten variables have been chosen for the mentioned model above (table 4.3). These factors and variables are differently dependent on land capability productivity. The database was developed by using ArcGIS software.

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Table 4.3: Main factors and their variables for land potential assessment

Main factor Variables Units OM - CEC meq/100g soil chemical property Soil pH - Exchangeable cation meq/100g soil Base saturation % Soil texture - Irrigated condition - physical property Soil depth Cm Drainage capacity - Relative topography Relative Topography -

Land potential productivity model: All major factors and their elements, namely chemical property (OM, CEC, pH, sum of exchangeable basic cation, and base saturation), physical property (soil texture, irrigation condition, soil depth, and drainage capacity), relative topography (depression, low flat, flat, upper flat, and high topography) were separated in overlaid layer, afterward ArcGIS was used to multiple all generated layers together. The potential levels for agricultural production were dependent on the score distribution of each site. The final score of the land capability was calculated by the formula as below: 푛 푊푖푋푥푦푖 푆푐표푟푒 = 푖 (1) 푛 푊푖 푖 Where: n is the number of factors,

Wi is the weight of factor i,

Xxyi is the score of category for each variable of each factor i. The final scores were converted to a level of capability as descrbed in the following table.

Table 4.4: The level of land potential productivity

Score Potential capacity Description The land has few limitations for agricultural ≥ 3.5 Highly potential productivity production, its potential productivity is high The land has some limitations for agricultural 2.5 - 3.5 Moderately potential productivity production, its potential productivity is medium The land has a number of serious limitations for 1.5 - 2.5 Lowly potential productivity agricultural production, its potential productivity is low The land has a large of serious limitations for ≤ 1.5 Very low potential productivity agricultural production, its potential productivity is very low

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4.2.2.3 Simple Limitation method Land limitation is an approach to indicate the land qualities in a relative assessment scale. Limitations are deflection from optimal conditions of a land characteristic which contrarily affects a kind of land-use. If a land characteristic is optimal for growing crops, it means there is no limitation. However, when the same quality is unfavorable for plant growth, it has severe limitation (Sys et al., 1991). Depending on the level of limitations, the land characteristics are assigned numbers ranging from 0 to 4 of degree of limitation as follow:

Table 4.5: A schematic relation between the limitation and class level Limitation levels Class levels Description 0: no limitation S1 Very suitable 1: slight limitation

2: moderate limitation S2 Moderately suitable

3: severe limitation S3 Marginally suitable

4: Strongly severe limitation N1 and N2 Unsuitable and non-susceptible to correction Source: (FAO, 1976, Sys et al., 1991)

According to the simple limitation method (FAO, 1976), the crop requirement tables have to be generated for each land utilization type first, and then land suitability classes are defined basing on the lowest class level of only or more characteristic. The selected crops and their requirement for evaluation are presented in the next chapter. The flowchart of the simple limitation method is shown in Figure 4.3.

4.2.2.4 Square root method Square root method (Khiddir, 1986) is one of the parametric approaches in the evaluation of land qualities. It shows a numeral rating of the different limitation levels of the land characteristics in numerical scale from a maximum to a minimum value. If the land quality has no limitation for considered land utilization type the maximum rating of 100 is assigned. Conversely, if the same land characteristics have strongly limitation for plant growth then a minimal rating is applied (Sys et al., 1991). The square root method can be subsumed under the rubric of the parametric methods. The equation of square root method respectively shows:

= √ (2)

Where: I is index of square root method,

Rmin is the minimum rated criterion (%) A, B, C, etc. are remaining ratings (%)

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The rate for each criterion is obtained after the fields or measurements of the land properties compared with the crop requirements in the reference tables. After matching measurements, rating values from 0 to 100 is given to each criterion. The total score for a special land unit is also given a rate of 0 to 100 by calculation through the equation (2). The flowchart of square root method is shown in figure 4.4. Suitability classes are arbitrarily defined by determining specific land index and using the guideline suggested by Sys et al., (1991) (Table 4.6), the qualitative land suitability classes and the limiting elements of the given plant growth in different soil series for each crop are figured out.

Table 4.6: Determine classes of land suitability for different land indices

Symbol Description Land index

S1 Highly suitable 75 – 100

S2 Moderately suitable 50 – 75

S3 Marginally suitable 25 – 50

N1 Currently not suitable 12.5 – 25

N2 Permanently not suitable 0 – 12.5

Land suitability evaluation Land suitability evaluation

Selected data for the Land utilization types assessment

Crop growth requirements Crop growth requirements

Calculation of individual Land mapping units factor of each LMU

Calculating the final suitability Definition of preliminary score for all land mapping suitability class

Assigning land suitability classes based Definition of final suitability class on the final score

Figure 4.3: Simple limitation method Figure 4.4: Square root method

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Data Sources and Methodology

4.2.2.5 Integrated AHP method with GIS for land suitability evaluation Making decisions on the suitability assessment for annual agricultural crops was determined through using the general model as presented in Figure 4.5.

Select land characteristics which are necessary as input factors

Develop, analyze and store the parameters; create data to gain new information

Conver the vector data into rastor data

Apply AHP method Integrating with GIS

Development of Hierarchy structure Assigning the score to each criterion

Standardization of suitability classes Overlaying

0.1 <

(Weighed sum) on

Weigh of each Pairwise Pairwise criterion in the Suitable map for the specific comparis hierarchy agriculture use

Figure 4.5: The general flowchart of Land suitability evaluation for specific selected crops The flowchart shows that there are five steps of land suitability evaluation by combining multi-criteria evaluation and analytical hierarch process as follows: Step 1: selecting land characteristics as input factors for building land mapping units in order to evaluate the suitability of selected annual agricultural crops. Step 2: developing, analyzing, and storing all of the parameters needed for land evaluation by using ArcGIS software. Step 3: converting the vector data of each parameter into raster data then reclassify all of the raster data for assigning the score to each element. Step 4: developing AHP method and examining the weight value for each criterion and sub criterion. Step 5: integrating AHP with GIS to obtain the final result of suitability map for evaluated crops.

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Development of hierarchy structure: The selection of main criteria and sub-criteria for land evaluation by integrating MCE and AHP method for land suitability evaluation is iterative in nature and based on realistic conditions of the study area. The literature review which helps frame the study and the experts‘ opinions provide a repertoire of relevant tools in the selection of evaluation criteria. In the research, there are three main parameter groups considered involving infrastructural systems, relative topography and soil property and nine sub-criteria of soil property. Relationship between the main parameters and sub-parameters creates a hierarchical structure. The main criteria are at the highest levels; the next level is sub-criteria; the lowest level belongs to sub-unit criteria. The detail of the selection of the main criteria, sub- criteria, and sub-unit criteria development of hierarchical structure are presented in Figure 4.6. The suitability evaluation of physical land by integrating MCE and AHP method used the criteria and weight for different important rates of each parameter and sub- parameter. The multiplication process for different suitability levels used comparison developed by Saaty (1977, 1990 & 2008) in the context of a decision making process. In the procedure for multi-criteria evaluation applying a weight linear combination, it is required that the weights sum to one (1.0). According to Saaty‘s approach, weight of this nature can be inferred by taking the principal eigenvector of a square reciprocal matrix of pairwise comparisons between the criteria. The comparisons concern the relative important of the two criteria included in evaluating suitability for the stated objective. The weights of criteria and sub-criteria are determined depending on the importance of each criterion and sub-criterion. The greater the weight is, the larger the value and the more essential the decision criterion and sub-criterion. In this study, the weights were determined based on the ranking of three main criteria, four sub-criteria, and nine sub-unit criteria from most important to least important. Comparisons were used to determine the order of pairwises from the least to the most important. In this current study, AHP method was applied to describe the multi-criteria evaluation and the MS Excel supported for AHP method above. In addition, the climate condition such as rainfall, temperature, humidity, hours of sunlight and amount of evaporation are also taken into consideration. Nevertheless, these factors are consistent in the whole area of the research, they are not shown in the land mapping unit. Still, they are generally examined for the selection of annual agricultural crops in the study area.

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Goal Criteria Sub-criteria Sub-unit criteria

OM

CEC Soil pH

EC

Chemical condition Cn BS

Soil property Soil

Soil texture

Irrigation condition

Soil depth

Physical condition Drainage capacity

s Relative topographyRelative

Land suitability evaluation specific for agricultureuse

Rural road

drainage

Infrastructure system -

Irrigation

Figure 4.6: The main criteria, sub-criteria and sub-unit criteria in AHP structure 4.2.3 Description of AHP method AHP is a method to make decisions based on multi-criteria in ratio scales from paired comparison (Saaty, 1980). According to Saaty (1977, 1990), the AHP is based on three main consecutive principals as follows: (1) Definition of the overall goal (suitability assessment) (2) Comparative judgment of criteria and sub-criteria (3) Synthesis of the priorities

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Data Sources and Methodology

The first step of AHP technique is to structure the general goal into a number of criteria and sub-criteria in a hierarchy. At the top level of the hierarchy is the goal of the research problem. The next level comprises the main relevant criteria for this goal. The following levels consist of sub-criteria, and the evaluated alternatives are at the bottom level of the hierarchy. This research employs these steps to land suitability evaluation, and the relevant criteria and sub-criteria to the goal were defined and organized in the hierarchy structure demonstrated in Figure 4.6. The combination of both qualitative and quantitative criteria is facilitated by this hierarchy. The next step of AHP method is the comparison of the criteria and sub-criteria. To make comparisons, a scale of numbers is created to indicate how important one factor is over another with respect to what they are compared. The basic scale of Table 4.7 can be used for the relative comparison. This table expresses the comparisons that were translated in verbal terms after translation into the corresponding numbers. The value used when comparing the element on the vertical axis with the element on the horizontal axis changes from 1 to 9. In contrary, the value of reciprocal varies from 1/2 to 1/9. For instance, when comparing factor A with factor B, if A is three times important than B, then B is as 1/3 times important as A. Table 4.7: Basic scale for pairwise comparisons

Number of value Verbal scale 1 Equally important, likely or preferred 3 Moderately more important, likely or preferred 5 Strongly more important, likely or preferred

7 Very strongly more important, likely or preferred 9 Extremely more important, likely or preferred 2, 4, 6, 8 Intermediate values to reflect compromise

In this study, within each level of hierarchial structure, the relative importance between each pair of criteria and each pair of sub-criteria to the overall goal is evaluated. A scale of nine points is applied for these assessments. For example, in comparing factor A to factor B, a score of one implies that they are equally relevant to the evaluation and the score of nine indicates that A is of extreme importance relative to B. In order to figure out the weights of the different criteria a comparison of pairwises with 1s on the diagonal was applied (e.g., A to A is 1) and the lower left triangle contained reciprocal scores (e.g., if A to B is 5, then B to A is 1/5) (Nabarath, 2008). Pair-wise comparisons generated for the level of hierarchy that comprise of experts‘ opinions associating with the relative importance of the evaluated criteria (see Table 4.8)

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Table 4.8: An example of pair-wise comparison matrix in AHP (M)

Criteria A B C D E Weight (Wi) A 1 3 5 7 9 0.52 B 1/3 1 2 6 7 0.26 C 1/5 1/2 1 2 4 0.13

D 1/7 1/6 1/2 1 3 0.07 E 1/9 1/7 1/4 1/5 1 0.03 훌퐦퐚퐱 = 5.189 CI = 0.047 CR = 0.042 횺 = 1.0

Finally, the last step of the AHP technique is the synthesis of the comparisons to obtain priorities of the alternative with respect to each parameter and the weights of each criterion with respect to the main goal. Afterwards, the local priorities are multiplied by the weights of the respective factors. The consequences are summed up for the final priority of each option. It is crucial to look into the consitency of the pairwise comparison to accept the weight of each level in the structure (Nabarath, 2008). The parametric quantity that is used to confirm this consistency is named the Consistency Ratio (CR). It is a measurement of how much variation is allowed and must be less than 10%. Otherwise, it is needed to improve consistency (Saaty, 1990). The CR can be calculated as follows:

From the matrix M, can be obtained and later used to estimate CR and Wi which become the priority vector. The Wi can be calculated through an algorithm based on the eigenvector method (Saaty, 1990, Gao et al., 2010) as follows:

= T Where: Wi = (w1 , w2 , …, wn) is weight vector , Wi > 0 (i = 1 , 2 , …, n) , is the largest eigenvalue of M. This eigenvector solution should be simplified as = The formula of CR got from the Consistency Index (CI) is as follows: ( ) = ( )

=

Where:

is maximum eigenvalue CI is Consistency Index CR is Consistency Ratio RI is Random Index n is the number of factors in each pair-wise comparison matrix

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Data Sources and Methodology

RI is an average number of comparative matrix in pairs from 1 – 10 that is obtained according to a particular number of matrix rows and varies depending upon the order of the matrix, as shown in Table 4.9 (Alonso and Lamata, 2006). When the matrix is larger, the level is more inconsistent (Permadi, 1992). Table 4.9: The average random index based on matrix size

n 1 2 3 4 5 6 7 8 9 10 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49

According to Zeshui and Cuiping (1999), if CR ≤ 10% then the matrix is already consistent and AHP can be continued, if CR > 10% the matrix needs revising until it is of acceptable consistency. If CR in level 1 meets the requirement, the next step is making pair-wise comparison matrix for level 2 of each sub-criterion applied. In every matrix , CI and CR are calculated by using the same formula and requirement as well. The final weights for land suitability evaluation are presented in the next chapter. The attribute scores of sub-criteria are computed for each land mapping unit. The scores are examined based on experts as well as the local conditions. These scores (Xi) are aggregated with the weights (Wi) to provide suitability value for each land unit matching each selected agricultural crops. The land suitability index for each land unit is calculated by using the following formula:

= (3) Where:

is land suitability index,

is the weight of factor (i = 1, 2, 3….n),

is the attribute score of each sub-criterion. The process of land suitability is done in ArcGIS software despite the composite map of land unit. Spatial data and attribute data are the two important components of the composite map. The former shows the location and shape of land mapping units. The latter is presented in a table which shows the attribute values of criteria and its scores. ArcGIS function is applied to perform the evaluation based on the scores, the weights of elements by using the above equation. Incorporating both spatial data and land suitability index generates a continuously suitable map. This research implemented calculation of the weight and score value of each criterion and sub-criterion, created the theme layers of each sub-criterion. Then all of them

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Data Sources and Methodology were overlaid together to generate the final land suitability classification for selected agricultural crops (Table 4.10). Table 4.10: The level class of land suitability evaluation

Suitability index Suitable classification Description

> 4.0 S1 Highly suitable

3.5 – 4.0 S2 Moderately suitable

3.0 - 3.5 S3 Marginally suitable

2.5 – 3.0 N1 Currently not suitable < 2.5 N2 Permanently not suitable

A suitable map presents each evaluation criterion with alternative such as S1, S2, S3, N to indicate the level of suitability with respect to an evaluated factor. After weighting and rating of all parameters over the hierarchy were obtained, the final suitability maps for different crops were multiplied by applying equation (3). The illustration on how weighted sum works is shown in Figure 4.7.

Figure 4.7: Aggregation of the rating and weight of each factor. Source: http://pro.arcgis.com/

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Chapter 5. Results of the Research

This chapter presents the results of how changes in land from 1989 to 2013 happened, and it specifies the key soil properties and soil characteristics and qualities of the study area. In addition, the chapter determines the land potential productivity for agricultural production in Quang Xuong District by integrating the Linear Combination Method Hopkins (1977) with GIS. Finally, the crops requirements, the procedures as well as the consequences of land suitability evaluation are also performed by applying the simple limitation, square root, and AHP methods.

5.1 Land cover change 5.1.1 Image pre-processing Both unsystematic and systematic geometric errors often occur in the Landsat data. Before interpreting for further satellite image analysis, inherent distortion needs to be reduced. However, in order to integrate Landsat TM and Landsat OLI and TIRS with the vector polygon boundaries, it is necessary to register both of them to a common map coordinate system. This stage plays an important role in analyzing either either intergration of the images with other map information or for individual assessment. In this study, Landsat TM of 1989 and Landsat OLI & TIRS of 2013 were rectified to UTM zone 48, WGS 84. The geometric correction of the images was performed using topography of Quang Xuong district with help of Ground control points (GCPs). As to prevent possible changes to the original pixel values of the image data, neighbor resampling method was applied. Therefore, both images of 1989 and 2013 were geometrically corrected by using 35 control points. The root mean square errors (RMSE) for Landsat TM of 1989 and Landsat OLI of 2013 were 0.020 pixels and 0.017 pixels, respectively. The next stage was clipping the images to focus on the processing of the study area. Figure 5.1 shows the geometric shape of the images after clipping images based on administrative boundaries.

5.1.2 Landsat images classification All image processing functions required to complete the land use/land cover classification of the research area was performed by applying Envi software version 5.2. Normally, there are two common approaches to display the land-use mapping by using supervised or unsupervised classification techniques. In a supervised classification, the defining of training area is strongly responsible for the precision of the image analysis. It presents all of the cover types of land cover that wishes to extract from an image.

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Nevertheless, it is not simple to account for all the cover types in the certain image as well as the variability within the cover types. Unsupervised classification is really different from supervised classification one. In details, the scene was classified by using signature in company with the computer. After the consequences of classification process in a number of spectral classes, the analyst must assign to information classes of interest. Thus, the knowledge of terrain in the scene and its spectral characteristics are very important requirements for the results of image classification (Bourne and Graves, 2001). In the research, supervised classification was used to generate land use/land cover information from both Landsat images of 1989 and 2013. Finally, the results of the classification were compared by applying standard measures of accuracy and Kappa analysis.

1989 2013

Figure 5.1: The geometric shape of the study area 5.1.2.1 Selection of training samples for image classification Land use/land cover of both images was classified based on classification scheme developed by Anderson et al., (1976) for the interpretation of remote sensor data at different scales and resolutions. A false colour composite was implemented to collect the training data for the supervised classification. The simplest method is combining different bands for selecting training samples to enhance the image in a way to provide rough information of land use/land cover (Linh, 2008). The false colour composite image can help to enhance the visualization of the vegetation and boundary of each land cover type. The false colour images were generated with red = 4, green = 3, blue = 2 bands for Landsat

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TM, and Red = 5, green = 4, blue = 3 for OLI & TIRS images. They were separately presented over the images to locate the known land cover classes and determine training samples for image classification. Displaying bands 4, 3 and 2 for Landsat TM and bands 5, 4 and 3 for Landsat OLI & TIRS image as false colour composite is strongly helpful for the designation of various land cover types during training process (Figure. 5.2).

a b c

e d f

Figure 5.2: Three different False Colour Composite combinations

The combination of the image a, b, c was 4-3-2, 2-3-4, and 3-4-2 bands for Landsat TM of 1989. The combination of the image d, e, f was 5-4-3, 3-4-5, and 4-5-3 bands for Landsat OLI & TIRS of 2013.

Natural spectral clusters can be defined with a high degree of objectivity by using an unsupervised classification method (Güler et al., 2007). The ISODATA algorithm was employed to find out spectral clusters from the Landsat data. Minimum spectral distance will be assigned as a cluster for each pixel and this algorithm is relatively straightforward and has intuitive appeal for identifying clusters (Vanderzee and Ehrlich, 1995). As a result of ISODATA algorithm, ten land use classes were generated to collect training data. The training samples were selected based on the basis of the unsupervised classified image and

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Results of the Research the current land use map of 2012. These training samples were selected from all cover land found in the study area with the average of 26 training areas for each land cover type of 1989, 30 training areas for each land cover type of 2013, and a minimum average of 12 pixels for each training sample of both images. Besides, the statistical analyses were computed based on Jeffrey-Matusita distance (Xiuping et al., 2006). The number of land use/land cover classes were defined based on field work and available land use statistics for the study area, and the defined classes for image classification were Built-up, Agriculture, Water surface, Forest, Salty, and unused land area. The description of those types of classes is mentioned in Table 4.2 in Chapter 4.

5.1.2.2 Maximum-likelihood classification In the next step, the supervised classification is applied for the classification process. It is performed with the maximum-likelihood algorithm, where the training samples are homogeneous reflectance of certain areas. According to Hanh et al., (2015) the maximum likelihood classification method is the most well-known parametric classifiers used for supervised classification to generate a land use/land cover map. This approach demonstrates that data is best collected from remote areas if each class contains some Gaussian distribution (Bolstad and Lillesand, 1991). In this stage, the maximum likelihood classifier was conducted, since it could obtain some reliable results. Contrarily, parallelepiped classifier would bring problem when overlapping and minimum distance classifier is insensitive to the discrepancy in each class. Finally, the classified images were smoothed by using a 3 × 3 majority filter to reduce the number of misclassified pixel in the land use/land cover maps (ERDAS, 1999).

5.1.3 Post classification processing The land use/land cover classification was generated by two Landsat TM and Landsat OLI & TIRS images acquired in September of 1989 and 2013. After classification, detection of land cover changes was achieved by overlaying and post-classification comparison of the land cover/land use maps of the different time periods. This step gave not only the size and distribution of changed areas, but also the percentages of other land cover classes that share in the change each land cover class individually. For the maximum quality of spectral data from classification process, the original resolution of the satellite images was used and determining the quantity of the conversions (Hanh et al., 2015). The map of the change was accompanied by the respective cross tabulation matrices showing the change pathways.

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5.1.4 Accuracy assessment One of the most important steps in the classifying images is the accuracy assessment as it presents how reliable the information from remotely sensed data is extracted. There are several ways to perform accuracy assessment such as qualitative, quantitative, expensive, inexpensive, quick, and time consuming. The main objective of accuracy assessment is finding out the precision of image classification. The results of an accuracy assessment are shown in an error matrix, which compares information from references to data of a map for number of samples areas. The column of the matrix shows the observed data and the row is displaying the classified data. The number of the reference pixels is a key component in computing the precision of classification. According to Congalton (1991), it needed more than 250 reference pixels to determine the mean of a class to within plus or minus five percent. He also suggested that at least 50 test points per land use/land cover category are collected to produce an error matrix. In this research, a standard method suggested by Congalton (1991) was used to assess the overall accuracy, producer‘s and user‘s accuracy. The most importance of determining accuracy is to indicate the results of the image classification. After performing the image classification, the results of the accuracy assessment were presented in the confusion matrix by using quantitative analysis. The land use map of 2012 and historical data were considered as the reference data of generating random sample points for the accuracy assessment. A stratified random sampling technique with 591 and 494 pixels was used to determine the precision of land cover classifications of 1989 and 2013. The overall accuracy between Landsat image classification and the reference data was calculated as follow:

=

The producer‘s accuracy was computed by

=

and the user‘s accuracy was computed by

=

Where: k = the number of categories

ni+: is the proportion of area mapped in land-cover class i

n+j: is the true proportion of area in land-cover class j. n = total number of observations

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Furthermore, a discrete multivariate approach of Kappa analysis is also used in accuracy assessment from the confusion matrix (Jensen, 1995). It is known as a Khat statistic approach to measure the agreement or accuracy (Cohen, 1960). The Kappa statistic illustrates the agreement between the classified land use and the observed land use. Unlike the overall, producer‘s and user‘s accuracies, in general, Kappa analysis can take the chance allocation of class labels into consideration by using the main diagonal, columns, matrix rows, and error matrix (Tso and Paul, 2009). The Kappa statistic is calculated as:

( ) ( ) = ( )

Where r is the number of rows in the matrix,

Xii is the number of observations in row i and column i,

xi x+i is marginal totals for row i and column i respectively, and N is the total number of pixels.

5.1.5 Classification and change maps and statistics 5.1.5.1 Classification False Colour Composites were used to display both Landsat images with band 4, 3, 2 for Landsat TM of 1989 and band 5, 4, 3 for Landsat OLI & TIRS of 2013. The same band combination was also used for generating the training samples on the images in order to perform supervised classification. The Maximum Likelihood algorithm was selected to digitize the images. It requires a good knowledge of image processing, image interpretation and sufficient acquaintance with the real world. The selection of training samples requires close interaction between the image analyst and the image data. Therefore, the training samples were examined many times by using statistical analyses in order to provide satisfactory results. The statistical analyses were computed based on both the Jeffries- Matusita and Transformed Divergence reparability measures. If the values are greater than 1.9, it means that they are highly separated. If the values are lower than 1.0, it means that they have very low reparability (Richards, 1999). The training classes were grouped based on the land use types and the results of the field survey. The classification of the land use/land cover in 1989 and 2013 were conducted with 6 classes, including agriculture land, built-up areas, water surface, forest land, salty land, and unused land.

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5.1.5.2 Land use status in 1989 and 2013 The land use/land cover classification was examined based on the results from the interpretation of two Landsat images acquired in September of 1989 and 2013. After completing image classification, both classified images were smoothed by using a 3 x 3 majority filter to eliminate the small areas that may appear through the classified images. Afterwards, the results of classification were exported to ArcGIS for further processing. The land cover classification of 1989 and 2013 are presented in Figure 5.3.

Figure 5.3: Supervised Maximum likelihood classification of 1989 and 2013 Classification maps were generated for two different periods of time from 1989 to 2013 and the individual for the two periods were summarized in Table 5.1 and Figure 5.4. Table 5.1 shows that approximately 63% and 49.17% of the total area was for agricultural uses in 1989 and 2013. The built-up area covered approximately 22.7% and 40.32% of the total geographical area of Quang Xuong District in 1989 and 2013, respectively. The water surface covered about 9.3% and 7.72% of the total area of the region in 1989 and 2103, respectively. About 0.3% and 0.42% area was under salty practices in 1989 and 2013, respectively. There was about 0.2% and 1.97% of the total study area under the forest cover in 1989 and 2013, respectively. The unused area covered about 4.5% and 0.40% of the total natural area in 1989 and 2013. The spatial pattern reveals that the study area is flat

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Results of the Research and more than a half of the natural area was used for agricultural practices in 1989 and nearly half of the area used for agricultural activities in 2013. However, the natural area for agricultural productivity is decreasing due to the expansion area for inhabitants as well as for rural infrastructure development. Table 5.1: Result of land use/land cover classification in 1989 and 2013

1989 2013 Number Class Change Area (ha) % Area (ha) % 1 Water surface 2122.29 9.3 1759.48 7.72 -362.81 2 Salty land 60.51 0.3 95.47 0.42 35.16 3 Build-up area 5172.48 22.7 9185.44 40.32 4012.96 4 Agriculture land 14362.12 63.0 11200.30 49.17 -3161.82 5 Forest land 52.69 0.2 448.45 1.97 395.76 6 Unused land 1010.25 4.5 91.00 0.40 -919.25

5.1.2.3 Accuracy assessment Accuracy assessment was examined for image classification of 1989 and 2013. A stratified random sampling design was adopted in the accuracy assessment. For the land use/land cover classification of 1989, a total of 591 pixels were randomly selected. The results indicated that an overall accuracy is of 94.08% and a Kappa index of agreement is of 0.91 (Table 5.2). In examining the producer‘s accuracy, all classes are over 85%, except salty land which was 77.78%. In examining of the user‘s accuracy, all classes are over 90%, except forest land which was 87.50%. Table 5.2: Accuracy assessment of the image classification of 1989 from Landsat TM data Reference data 1989

and

land

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up

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Classified data

Agricultural l Build Water surface Salty land Unused land Forest land Row total (%) accuracy User's Agricultural land 289 6 5 1 2 1 304 95.07 Build-up area 6 112 0 0 4 0 122 91.80 Water surface 3 0 87 0 0 0 90 96.67 Salty land 0 0 0 7 0 0 7 100.00 Unused land 0 1 3 1 47 0 52 90.38 Forest land 1 1 0 0 0 14 16 87.50 Column total 299 120 95 9 53 15 591 Producer's accuracy (%) 96.66 93.33 91.58 77.78 88.63 93.33 Overall accuracy = 94.08% Kappa index = 0.91

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For the land use/land cover classification of 2013, a total of 494 pixels were selected. The results presented that an overall accuracy is of 92.91% and a Kappa index of agreement is of 0.896 (Table 5.3). In term of the producer‘s accuracy, all classes are over 90%, except salty land class which made up 66.67%. In terms of the user‘s accuracy four classes exhibit over 90% with the exception of salty and unused land classes, which are 54.55% and 68.75%, respectively. The salty and unused land classes show clear confusion because of similar reflection value of them. Table 5.3: Accuracy assessment of the image classification of 2013 from Landsat OLI & TIRS data

Reference data 2013

cy (%)

land

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up

- Classified data

Agricultural land Build Water surface Salty land Unused land Forest land Row total accura User's Agricultural land 157 1 1 0 0 11 170 92.35 Build-up area 2 198 0 2 1 1 204 97.06 Water surface 3 2 63 0 0 0 68 92.65 Salty land 0 0 5 6 0 0 11 54.55 Unused land 0 3 1 1 11 0 16 68.75 Forest land 0 1 0 0 0 24 25 96.00 Column total 162 205 70 9 12 36 494 Producer's accuracy (%) 96.91 92.09 90.00 66.67 91.67 92.31 Overall accuracy = 92.91% Kappa index = 0.896

5.1.2.4 Land Use/Land Cover change detection The surface distribution (in ha) of the proportion of each land use/land cover class in the different time from 1989 to 2013 is displayed in Figure 5.4. Cultivated land remains the largest land use type in the region throughout the 24 years. The land for forest production occupies the least in 1989 but by the end of the study period, unused and salty lands have turned into the smallest land cover types in the district. All the land cover types have been changed from 1989 to 2013, the largest change namely build-up area, cultivated, unused, and forest lands. Table 5.1 shows that about 3,161.82ha, 919.25ha and 362.81ha decrease is observed in agricultural areas, unused land, and water surface areas. Meanwhile there is an increase of 4,012.96ha in build-up area, 395.76ha in forest, and 35.16ha in salty lands between 1989 and 2013. This clearly points out that the land use/land cover change during the examined period, mostly due to expansion of build-up areas and human activities are the reasons for the change.

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Figure 5.5 illustrates the magnitudes of change in ha for the six main land use/land cover categories from 1989 to 2013 throughout the district. Figure 5.6 presents the spatial distribution of the different change types over the research time. The detail dynamics of the land use/land cover changes in the study area between 1989 and 2013 is shown in Table 5.4. The table is a cross tabulation matrix of the land use/land cover change, displaying the conversion from each class to another class. For instance, from 1989 to 2013, 10130.41ha agricultural area production remained stable, 1069.89ha of new cultivated land are mostly generated at the expense of water surface, unused land and build-up area. Contrarily, 4231.71ha of agricultural land are lost to build-up areas (3323.18ha), water surface (757.06ha), forest (126.23ha), unused land (15.62ha), and salty land (10.62ha). The land cover categories of forest and build-up areas are expanded the most over other types of land use, with 395.76ha and 4012.96ha, respectively, mostly from cultivated, water surface, unused areas. During this period of 25 years, the area of agricultural, unused lands and water surface are the greatest reduction in area, with 4231.71ha, 1156.11ha and 988.11ha respectively.

14000.00

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10000.00

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6000.00 Area-1989 (ha) 4000.00 Area-2013 (ha) 2000.00 0.00

Figure 5.4: land use/ Land cover types within the study are from 1989 to 2013 Table 5.4: The change of land use/land cover in the study area from 1989 to 2013 in ha Forest Agriculture Water Unused Salty Build-up 1989 -2013 Total Expansion land land surface land land area Forest land 25.43 126.23 15.34 233.56 7.2 40.69 448.45 423.02 Agriculture land 13.95 10130.41 473.61 98.68 0.1 483.55 11200.3 1069.89 Water-surface 0.13 756.06 966.18 26.89 0.41 9.81 1759.48 793.3 Unused land 0 15.62 41.28 22.14 1.98 9.98 91 68.86 Salty-land 0 10.62 41.22 29.43 13.86 0.54 95.67 81.81 Build-up area 13.18 3323.18 584.66 499.55 36.96 4727.91 9185.44 4607.53 Total 52.69 14362.12 2122.29 1010.25 60.51 5173.66 22781.34

Reduction 27.26 4231.71 1156.11 988.11 46.65 594.57

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4000.00

3000.00

2000.00

1000.00

0.00

-1000.00

-2000.00

-3000.00

-4000.00 Water Salty Build- Agri- Forest Unused surface land up area land land land Change (ha) -362.81 35.16 4012.96 -3161.82 395.76 -919.25

Figure 5.5: The change of land use/land cover categories types in ha

Figure 5.6: The change of land use/land cover from 1989 to 2013

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5.2 Land potential productivity assessment for agriculture use Agriculture is one of the most important economic sectors in Quang Xuong District. Based on the annual statistic report by this district in 2012, agricultural land occupied more than 50% of the entire area. However, the cultivated lands have been decreasing over years because of the population growth and the demand for expansion of build-up areas and rural infrastructure development. Thus, appropriate land use planning will be the best way to increase agricultural yield as well as protect cultivated land. Potential productivity assessment is a prerequisite step of evaluating whether or not a specific land is suitable for development of sustainable agriculture. In other words, potential productivity assessment is essential for successful agricultural production and an important stage in the process of land use planning. Evaluation of agricultural land capability will provide essential information about potentials and constraints. Its results will help land users and decision makers to suggest the best suitability of the land use. This part presents the results of spatial analysis to specify potential areas for agricultural production with GIS techniques based on the FAO guideline for land evaluation (FAO, 1976, 1985, 1993). Data analysis was implemented by using ArcGIS software, a combination of three main factors consisting of ten variables and their impacts are based on Linear Combination Method Hopkins (1977).

5.2.1 Determination of factors and variables development The selection of factors and their variables needs to have a highly close relationship with soil productivity. It should be emphasized that this determination of component is not comprehensive, only the remarkable factors for which information is reasonably readily reachable were considered. According to affecting soil productivity, three main components and ten variables have been chosen for assessment of land capability, namely: (1) chemical property factor including organic matter (OM); cation exchange capacity (CEC); pH; sum of exchangeable basic cation (EC); and base saturation (BS), (2) physical property involving soil texture; irrigated condition; soil depth; and drainage capacity, (3) relative topography including depressed; low flat; flat; upper flat; and high topography. These elements and their variables are distinctive in their dependence on land potential productivity. The ArcGIS 10.2 version was used to develop, analyze and store data of land capability assessment.

5.2.1.1 Chemical factor Chemical properties were mapped from different data resources. The generation of thematic map of chemical property was based on combining soil survey, a topographic

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Results of the Research map, soil and current land use map available. In order to produce the chemical property map, the topographic map soil map and the status of land use map were first used as a base map to provide a synoptic view of study area for determining landscape, land use patterns and geomorphology. In addition, soil samples were grouped by considering texture. Then, some samples were selected as representatives of soil mapping units for analyses. The purpose of analyses is to determine the distribution of OM, CEC, pH, EC, and BS at different layers of soil profile for high precision of classifying the soil types. Finally, the boundaries of soil types were identified and adjusted after checking sample points. The thematic property maps such as OM, CEC, soil pH, EC, and BS were created and presented at 1:25,000 scales from reconnaissance survey maps which were implemented by the Faculty of Land Management, Vietnam National University of Agriculture. Afterwards, the thematic maps were overlaid together to generate chemical property map. - Organic matter (OM): it is highly considered as an important role of revolving nutrient fund; and as an agent to improve soil structure, maintain cultivated land and minimize erosion. It has a strong impact on agricultural productivity and its values vary according to soil and vegetation types and the topography. This is one of the main causes of degradation of soil. Organic matter was divided into four classes: 1) High: > 2.0% 2) Medium: 2.0% – 1.5% 3) Low: 1.5% – 0.8% 4) Very low: < 0.8% - Cation exchange capacity (CEC): It is an important criterion and its ability is holding positively charged ions. Also, it has direct impacts on the stability of soil structure, availability of nutrients, soil pH and soil‘ reaction to fertilizers and other ameliorants (Hazelton and Murphy, 2007). Thus, CEC is a useful indicator of soil fertility because it shows the soil's ability to supply three important plant nutrients: calcium, magnesium and potassium. In this study, CEC was divided into three classes: 1) High: > 15meq/100g soil 2) Medium: 15 – 10meq/100g soil 3) Low: < 10meq/100g soil - Soil pH: It is an essential element consideration for agricultural production for several reasons including the fact that many kinds of crop prefer either alkaline or acidic conditions. Therefore, nutrients available in the soil are probably influenced by soil pH. It alters depending on soil types, topography, and vegetation types. Soil pH was divided into following classes:

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1) Very slightly acid: > 6.5 - < 7.0 2) Moderately acid: 6.5 - 6.0 3) Strongly acid: 6.0 – 5.5 4) Extremely acid: < 5.5 - Sum of exchangeable basic cation (EC): It is known as the number of exchangeable cations and can be extracted from the soil by adding neutral salt solution. The total exchangeable cation characterizes the nature of the soil. It may be the same in terms of quantity with CEC, but it may be basically different from CEC because CEC depends on soil pH. Thus, its value may be higher or lower the value of exchangeable cation. The suit of exchangeable cation and the nature of the measured exchangeable cation are dependent to a large extent on the chemical and mineralogical makeup of the soil. The sum of exchangeable basic cation (EC) was divided into three classes: 1) High: > 8.0meq/100g soil 2) Medium: 8.0 – 4.0meq/100g soil 3) Low: < 4.0meq/100g soil - Base saturation (BS): BS is used to characterize how completely occupied the adsorbing sites of soil mineral and organic particles with basic cations are. It describes how the basic cations act in the soil particle surface. BS is also used to examine whether a soil should be limed or not. Base saturation was divided into three classes: 1) High: > 50% 2) Medium: 35% - 50% 3) Low: < 35%

5.2.1.2 Physical factor Physical properies are one of the biggest factors that affects the growth and productivity of annual crops. In this study, the elements were concerned as physical properties for the assessment of land potential productivity including soil texture, soil depth, irrigated condition, and drainage capacity. Like the chemical factor, those physical properties were analyzed to measure their distribution in the process of classification the soil characteristics of a certain soil profile. After that, the thematic map of soil texture, soil depth, irrigated condition, and drainage capacity were produced at 1:25,000 scales. The final map of physical property was prepared by overlaying all the thematic together with GIS capability. - Soil texture: It refers to ratios of sand, silt or clay in soil, which is the basis for grouping soils, known as soil textural class. How easily soil can work, how much water and air are held and how easily water can be absorbed can be influenced by texture. It is

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Results of the Research also a key element in the coupled relationship between climate, soil and vegetation, and it can impact the ecological structure. Soil texture also plays an important part in the extent to which nutrients retain. Therefore, it is strongly meaningful for assessment of land potential productivity for agricultural use. Soil texture was classified into eight classes, which were silty loam, loam, silty clay loam, silty clay, cay loam, sandy loam, and coarse sand. - Soil depth: It refers to the extent to which plants can enter a soil from the Earth‘s surface. It plays a significant role in both annual and perennial crop performance. The yield can be determined by soil depth and its capacity of nutrients and water. If it is deep, well drained, with desirable texture and structure it will be suitable for cultivated production. Deep soils can hold more tree nutrients and water than can shallow soils with similar textures. Soil depth was divided into four classes: 1) Very deep: > 70cm 2) Deep: 50cm - 70cm 3) Moderately deep: 30cm - 50cm 4) Shallow: < 30cm - Irrigated condition: It is an indispensable variable in the land evaluation as well as in assessment of land potential for agricultural production. The condition of irrigation can affect to the yield and the growth of annual agricultural crops. It also influences the quality of land use and soil characteristics. Irrigated condition was classified into four classes: 1) Actively irrigated 2) Somewhat irrigated 3) Poorly irrigated 4) None irrigated - Drainage capacity: This term is referred to as a soil‘s capability of holding and transmiting water. It is characterized according to a soil's physical properties such as soil texture and soil structure. Based on the characteristics of the study area, the drainage capacity was divided into two classes: good and moderate.

5.2.1.3 Relative topography Topography is an important factor for land evaluation in general and for examination of land capability for agricultural use in particular. In geology and edaphalogy, topography indicates the elevation of a parcel, an area of land compared to certain standards. The term of relative topography is the height difference between the parcels of land or plots in an agricultural area such as a district, a commune, a village, and a farm level. It is used to determine the water supply, edaphic fertility and plants structure

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Results of the Research in a certain area of production. Its value is determined by the different height between the parcels of land when compared to each other in a cultivated area and expressed in the longstanding language of Vietnamese farmers. The scientific value and practicality is only significant to a specific local area. In Vietnam, relative topography is commonly used for the investigation of agricultural lands in the plain with absolute altitude areas which do not vary significantly. However, between parcels or plots, this type of topography is quite different in water supply, fertility, soil texture, and plant structure. Therefore, in order to assess the agriculture of land use in plain areas at districts, communes, and farm levels, the relative topography applied correspond with the land use requirements of each crop in the region. According to the characteristics of the study area, the relative topography was classified into five classes, including depression, low flat, flat, upper flat, and high.

5.2.2 Determination of weights of main factors and variables Different factors and their variables may provide dissimilar functions and values and have different levels of significance in requirement of land potential productivity for cultivation. Thus, each factor and its variable should have respective weight. The weight depended on the importance of each factor and variable. The greater the weight, the larger value and the more importance the decision of factor and variable would bring about. The most necessary rule in the procedure of land capability assessment is that the factor and variable have to be summed up to 100%. In this research, the weights were determined from an average value based on the result of interviewing people who have experience and knowledge in the agricultural field. Simultaneously, the ranking of three main factors and ten variables were also evaluated from the most importance to the least importance, the pair-wise comparisons between each of the factor and variable were also used to examine which is the most important, the next most important and so forth, until the least important. For example, the ranking from the most important to the least important variables of chemical factor were determined as OM, CEC, soil pH, EC, BS, respectively. A relative preference opinion of local experts for all factors and variables using pair-wise comparison is synthesized into a general weight in Table 5.5. The score of each variable category associated with requirements of potential productivity levels was defined by discussing with local officers. Based on the result of the discussion, the ranking of each variable was clarified from 1 to 4, with 1 is being the worst for agricultural use and 4 the best. These are very low potential, low potential, medium potential and high potential corresponding to Arabic numerals of 1, 2, 3 and 4. The assigned value of potential productivity is presented in Table 5.6.

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Table 5.5: Weight of each factor and variable

Weight 1 Weight 2 Overall Main factor Variables Unit (%) (%) Weight (%) OM - 30 12 CEC meq/100g soil 25 10 Chemical properties 40 Soil pH - 25 10 EC meq/100g soil 10 4 BS % 10 4 Soil texture - 30 10.5 Irrigated condition - 30 10.5 Physical properties 35 Soil depth cm 25 8.8 Drainage capacity - 15 5.2 Topography 25 Relative Topography - - 25

Table 5.6: Score of each variable category for land potential productivity assessment

Variable Category Score Variable Category Score Silty loam, Loam, > 50% 4 4 Silty clay loam Silty clay, 35% - 50 % 3 3 Base saturation Soil texture Clay loam Loamy sand, 2 < 35 % 1 Sandy loam Coarse sand 1 > 2 4 Actively irrigated 4 2 - 1.5 3 Somewhat irrigated 3 OM (%) Irrigation 1.5 - 0.8 2 Poorly irrigated 2 < 0.8 1 None irrigated 1 4 > 15 > 70cm 4 CEC (meq/100g soil) 15 – 10 3 < 10 1 Soil depth 50cm – 70cm 3 > 6.5 - < 7.0 4 30cm – 50cm 2 6.5 - 6.0 3 < 30cm 1 pH 6.0 - 5.5 2 Good 4 Drainage Moderate 2 < 5.5 1 Flat 4 > 8.0 4 Low flat 3 8.0 - 4.0 3 Relative Upper flat 3 EC (meq/100g soil) topography High 2 < 4.0 1 Depression 2

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5.2.3 Land potential productivity model Combining Linear combination Method of ranking and weighting score was applied to generate the land potential productivity for annual cultivation (Hopkins, 1977). The model was run in the environment of ArcGIS 10.2 because of its ability to adapt with various file formats in common use. All classes, which were stored in vector-based databases, were converted into raster-based datasets with 30 meters grid cell size, since overlaying analysis of raster data has more advantages in map algebra method. All factors and its variables were calculated and overlaid in separated layer, after that the multiple overlays were made step by step inside GIS. The land potential productivity levels for agricultural use were generated based on the score distribution of each site. The final score of the land potential productivity was determined by formula (1) and converted to a level of potential productivity as defined in Table 4.4 in Chapter 4.

5.2.4 Provisional land potential productivity assessment 5.2.4.1 Assessment of chemical factor The cultivated lands are considered as a main production element and its properties are a vital precondition for agriculture development and ensuring food security for the district. The classification of agricultural soil‘s chemical properties is based on field surveys, the variability of soil map as well as the results of laboratory analysis under the soil classification method of FAO-UNESCO. The agricultural soils in the study area were classified into six groups, namely: Arenosols; Salic Fluvisols; Gleysols; Fluvisols; Acrisols; and Leptosols. The chemical analyses show that the concentration of OM, CEC, soil pH, EC, and BS are varied between these soil types.

- Organic matter: It is one of the most important elements in land evaluation and assessment of land potential productivity for agricultural uses. It is especially an essential feature for providing nitrogen, phosphorus, sulphur, and iron. Under the present climatic and land use conditions and prevailing human intervention in the study area, based on the available data and the results of soil analysis show that the organic matter values varied depending on the types of soils and vegetation types. OM provides information of soil quality to help land users manage the land more effectively. High values are mostly found in the topsoil of the Fluvisols group. Nevertheless, very low values are mainly concentrated on the Arenosols, Acrisols and leptosols groups. In general, Fluvisols, salic Fluvisols and Gleysols groups can be rated as rich and medium humus. Poor humus contents is only concentrated on Fluvisols and Arenosols, and very poor humus is found in different soils groups such as Arenosols, salic Fluvisols, Acrisols and Leptosols.

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The classification of different OM classes is presented in Table 5.7. It indicated that four categories of the OM contents are defined and mapped, namely: rich humus, medium humus, poor humus, and very poor humus which occupy 33.72%, 16.46%, 28.60% and 21.21% of the investigated area, respectively. Table 5.7: Distribution of OM in the study area

OM Soil group Area (ha) Percent (%) > 2.0 salic Fluvisols, Fluvisols, Gleysols 4701.69 33.72 1.5 - 2.0 Fluvisols 2295.09 16.46 0.8 - 1.5 Arenosols, Fluvisols 3987.63 28.60 < 0.8 Arenosols, salic Fluvisols, Acrisols, Leptosols 2957.4 21.21 Total investigated area 13,941.81 100

- Cation exchange capacity: It is a measure of soil ability to hold and release various elements and compounds by electrical attraction. It is positively charged ions such as calcium (Ca2+), magnesium (Mg2+), and potassium (K+), sodium (Na+) hydrogen (H+), aluminum (Al3+). Therefore, CEC is widely used for agricultural assessment because it can measure the general fertility of soil. CEC is expressed in terms of milliequivalents of adsorbed cations per one-hundred grams soil (me/100g). The detailed analysis of CEC of agricultural land is shown in Table 5.8. The table shows that three categories of the CEC are identified and mapped, namely: high CEC, medium CEC, and low CEC which occupy 16.61%, 50.44%, and 32.95% of the investigated area, respectively. The analysis results indicate that, in the study area the medium CEC values only dominated in Fluvisols group and CEC values from low to high changed in the rest of soil groups. Table 5.8: Distribution of CEC values in the study area

CEC Soil group Area (ha) Percent %) > 15 Arenosols, salic Fluvisols, Fluvisols, Gleysols 2315.16 16.61 10 - 15 Fluvisols 7032.60 50.44 < 10 Arenosols, salic Fluvisols, Fluvisols, Acrisols, Leptosols 4594.05 32.95 Total investigated area 13,941.81 100

- Soil pH: it is another important soil characteristic and a vital element in land evaluation. It is directly influenced to a number of mineral elements, particularly phosphorus and the micronutrients. If a soil becomes more acid, its pH will decrease and vice versa if the soil becomes more alkaline, its pH will increase. The assessment of soil pH in the research area shows that nearly a half of the investigated area is acidic, accounting for 49.97% and soil acidity is distributed in all of the soil groups.

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Based on FAO system and the requirement for crop growth, four categories of the soil pH values were classified and mapped, including: slightly acid (> 6.5 - < 7.0), moderately acid (6.5 - 6.0), strongly acid (6.0 – 5.5), and extremely acid (< 5.5). Classified categories of soil pH are given in Table 5.9. The results show that 4% of the investigated area is favorable condition for agricultural use. 23.67% of the area is found to have minor limitation. 22.37% of the investigated area is limited for cultivation. 49.96% of the area is extremely acid in the study area. Table 5.9: Distribution of soil pH values in the study area

Area Percent pH Soil group (ha) (ha) (%) > 6.5 - < 7.0 Fluvisol 557.73 4.00 6.5 - 6.0 Arenosols, Fluvisols 3300.30 23.67 6.0 - 5.5 Arenosols, salic Fluvisols, Fluvisols, Acrisols 3118.14 22.37 < 5.5 Arenosols, salic Fluvisols, Fluvisols, Gleysols, Acrisols, Leptosols 6956.64 49.96 Total investigated area 13,941.81 100

- Sum of exchangeable basic cation (EC): It is a sum of basic alkali cation (Ca2+, K+ Mg2+ and Na+) in agricultural soil. Its concentration can be given in meq/100g soil. Thus, the value indicates the quantity of the cations provided by the soil which is relevant for plant nutrition and is an important indicator of soil fertility. The analysis results imply that the value of EC is different between soil groups, even in the same soil groups such as Arenosols, Fluvisols, and Acrisols. The more specific analysis of EC is provided in Table 5.10. Three categories of EC were named as high (> 8.0), medium (8.0 – 4.0), and low value (< 4.0) of EC. The consequences demonstrate that 5310.27ha occupying for 38.09% of the area is high value, more than a half of the area occupying for 51.72% is medium value, and about 1421.10ha accounting for 10.19% is low value of EC. Table 5.10: Distribution of EC in the study area

Area Percent EC Soil group (ha) (ha) (%) > 8.0 Arenosols, salic Fluvisols, Fluvisols, Gleysols 5310.27 38.09 8.0 - 4.0 Arenosols, Fluvisols 7210.44 51.72 < 4.0 Arenosols, salic Fluvisols, Fluvisols, Acrisols, Leptosols 1421.10 10.19 Total investigated area 13,941.81 100

- Base saturation: It refers to the percentage of the soil CEC that is occupied by a nutrient saturation or the sum of a group of nutrients. This information is used for predicting the soils ability to provide adequate crop nutrients, and indicate needed changes

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Results of the Research in fertilizer or lime programs. The implementation of this approach will make recommendations to adjust the soil quality for growing crops. Thus, in land evaluation and assessment of land potential productivity the percentage of base saturation should be considered as an important element in the process of land examination. The classification of different percentages of BS is shown in Table 5.11. It indicates that there are three percentage levels of BS, including high, medium, and low values of BS in soils which occupy for 63.17%, 33.12%, and 3.71% of the total investigated area, respectively. The percentage of base saturation less than 35% only appears in the Fluvisols group. Table 5.11: Distribution of BS in the study area Area Percent BS Soil group (ha) (ha) (%) > 50% Arenosols, salic Fluvisols, Fluvisols, Gleysols, Acrisols, 8806.68 63.17 50% - 35% Arenosols, salic Fluvisols, Fluvisols, leptosols 4617.18 33.12 < 35% Fluvisols 517.95 3.71 Total investigated area 13,941.81 100

- Potential productivity assessment of chemical factors Chemical properties of soil include five variables (OM, CEC, pH, EC, BS) as determinants of agricultural land quality such as agricultural productivity. It is commontly regarded as an important predictor of potential productivity of farmlands. The results of chemical factor examination for agricultural potential use are presented in Table 5.12 and Figure 5.7. Based on chemical property classification map, 1464.57ha of cultivated land is classified under high potential level, accounting for 10.50% of the research area and only located on the Fluvisols group. 5797.51ha is assessed as moderate potential category, accounting for 42.89%, prevailing in Fluvisols, Gleysol, and Arenosols groups. The low potential level is about 5293.80ha, occupying for 37.97% and is distributed in the Fluvisols, Gleysols, salic Fluvisols and Arenosols. The area for very low potential of agricultural use is about 1203.93ha or 8.64% of the entire investigated area and mainly distributed in the Acrisols, Arenosols, and Leptosols groups. Table 5.12: Potential productivity level of chemical factor for annual cultivation

Land use purpose Potential level Area (ha) Percent (%) High potential 1464.57 10.50 Annual Moderate potential 5979.51 42.89 cultivation Low potential 5293.80 37.97 Very low potential 1203.93 8.64 Sum 13,941.81 100

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Figure 5.7: Potential productivity classification for soil chemical factor 5.2.4.2 Assessment of physical factor Physical factors also play an indispensable role in land evaluation and assessment of land potential for cultivation. They usually control the suitability of the soil, insofar as they can affect directly economic and other inputs, outputs within the context of a specific type of land use. They provide information to help the land users protect or enhance the quality of soil for a certain use. They are the results of climatic factors, topography and life forms over times and any change in these influences may generate the different results of soil formed types. The variables of the physical factor are taken into account in this research including: soil texture, soil depth, irrigated condition, and drainage capacity.

- Soil texture: Based on the survey, the results of soil analysis and the requirement for growing annual crops, soil texture of the study area were classified into eight classes as described in Table 5.13. The table shows that the largest soil texture of agricultural area is silty loam with 5156.28ha, accounting for 36.89% of the investigated area and mainly concentrates in the Fluvisols and Acrisols groups. The next largest soil texture is loam with 3512.70ha or about 25.20% of the entire area and its distribution is spread in different soil

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Results of the Research groups such as the Acrisols, Arenosol, Fluvisols, and salic Fluvisols. 1373.67ha or 9.85% of the investigated area is examined as clay loam texture and only located in the Fluvisol group. 1070.10ha is texture of loamy sand, equivalent to 7.68% and distributed in Arenosol, salic Fluvisols. The land with sandy loam prevailing in Arenosols and Leptosols occupy 1215.99ha or 8.72% of the area. The smallest area of soil texture is coarse sand and is found in the Arenosols group with an area of 400.41ha, equivalent to 2.87%. It would make difficult conditions to land development for agricultural production. Silty clay loam texture is also found in the Fluvisols group with 759.06ha, accounting for 5.44%. Silty clay texture is found in the Gleysols group with an area of 453.60ha or 3.25% of the investigated area. Table 5.13: Distribution of cultivated land with different soil texture

Texture Soil group Area (ha) Percent (%) Silty loam Fluvisols, Acrisols 5156.28 36.98 Clay loam Fluvisols 1373.67 9.85 Loam Acrisols, Arenosol, Fluvisols, salic Fluvisols 3512.70 25.20 Loamy sand Arenosol, salic Fluvisols 1070.10 7.68 Sandy loam Arenosol, Leptosols 1215.99 8.72 Coarse sand Arenosols 400.41 2.87 Silty clay loam Fluvisols 759.06 5.44 Silty clay Gleysols 453.60 3.25 Total investigated area 13,941.81 100

- Soil depth: It is another important element in assessment of land potential productivity. The classification of different soil depth categories is described in Table 5.14. The table indicates that agricultural soils with the depth from 50 cm to 70cm prevail in the district. The area is about 8866.44ha, accounting for 63.60%. The soil which is from 30 cm to 50 cm deep covers about 1164.15ha or 8.35% of the entire investigated area. The soil which is less than 30cm in depth is only found in the Leptosols group with 219.33ha, occupying 1.57%. The soil with the depth of more than 70 cm mainly locates in the Acrisols and Fluvisols groups, accounting for 26.48% (3691.89ha). Normally, the soil depth with more than 70 cm can provide favorable conditions for cultivation. Table 5.14: Distribution of cultivated land with different soil texture

Area Percent Soil depth Soil group (ha) (%) < 30 cm Leptosols 219.33 1.57 30cm - 50cm Acrisols, Arenosols, Fluvisols, salic Fluvisols 1164.15 8.35 50cm - 70cm Acrisols, Arenosols, Fluvisols, salic Fluvisols, Gleysols 8866.44 63.60 > 70cm Arenosols, Fluvisols 3691.89 26.48 Total investigated area 13,941.81 100

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- Irrigated condition: Irrigation is strongly necessary for annual crops growth and development. It also directly affects crop yields, thus agricultural water use is intrinsically consumptive. Different crops have different water requirements, and these vary depending on local climatic conditions. Based on soil survey, topographic map, and land current status map of 2012, the irrigation of agricultural land was divided into four classes (Table 5.15). The results show that the largest area of cultivated land under the actively irrigated capability is about 5949.72ha or 42.68%. The next largest area is the land of somewhat irrigated situation with 4942.35ha, equivalent to 35.45% of the survey area. The area with poor irrigation is about 2430.00ha, accounting for 17.43%. Approximately 619.74ha or 4.45% of the agricultural area is completely under none irrigated condition. Table 5.15: Distribution of cultivated land with different irrigated condition Area Percent Irrigated condition Soil group (ha) (ha) Actively irrigated Acrisols, Arenosols, Fluvisols, salic Fluvisols, Gleysols 5949.72 42.68 Somewhat irrigated Arenosols, Fluvisols, salic Fluvisols 4942.35 35.45 Poorly irrigated Acrisols, Arenosols, 2430.00 17.43 None irrigated Arenosols, Leptosols 619.74 4.45 Total investigated area 13941.81 100

- Drainage capacity: The classification of drainage capacity in the cultivated area is presented in Table 5.16. The class of good drainage is about 2686.50ha, occupying 19.27% of the entire area. The second class covering 11,255.31ha, accounting for 80.73% of the area, is under moderate drainage capacity. This capacity of drainage is a great condition for cropping development in the study area. Table 5.16: The drainage capacity in the study area Drainage Area Percent Soil group capacity (ha) (%) Good Arenosols, Leptosols 2686.50 19.27 Moderate Acrisols, Arenosols, Fluvisols, salic Fluvisols, Gleysols 11,255.31 80.73 Total investigated area 13,941.81 100

- Potential productivity assessment of physical factor: The potential productivity of physical map was the result of overlaying thematic maps of soil texture, soil depth, irrigation condition, and drainage capacity (Figure 5.8). The details of physical factor assessment for cultivation use are described in Table 5.17. Based on the results examination, there is no agricultural area under the very low potential situation in the study area, and most of the investigated lands are of moderate capability for agricultural development with 11152.26ha, covering 79.99%. The areas with low potential productivity amounted to a small proportion compared with entire area for agricultural use. They cover about 619.74ha, equivalent to

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4.45%. The results also demonstrate that the lands with high potential productivity for crops growth is about 2169.81ha, occupying 15.56% of the total examining area. In general, the research area has a good condition of physical properties for agricultural development. Table 5.17: Potential productivity level of physical factor for annual cultivation

Land use purpose Potential level Area (ha) Percent (%) High potential 2169.81 15.56 Annual cultivation Moderate potential 11,152.26 79.99 Low potential 619.74 4.45 Sum 13,941.81 100

Figure 5.8: Potential productivity classification for soil physical factor 5.2.4.3 Assessment of relative topographic factor The study area is a costal sandy land, so most of the areas are plain except for an area of 219.33ha of the Leptosols group whose slope is more than 250 and considered as unsuitable for annual agricultural crops. In Vietnam, the term of relative topography is usually used in land evaluation projects for the plains at districts, communes, villages or small areas. Based on the natural condition of the area, the observation, experts‘ opinions,

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Results of the Research and discussion with local farmers, the topography was classified into five classes as flat, low flat, upper flat, depression and high. The potential levels of classification are shown in Table 5.18 and Figure 5.9. This factor of the enquired area is assessed as follows: high potential for cultivated activities is 1785.06ha or 12.80%; moderate potential is 8699.13ha, covering of 62.40%, 2132.10ha of which belongs to low flat, and 6567.03ha is topography of upper flat. The total assessed area of low potential is 3328.29ha or 23.23%, 2837.88ha of which is under the relative topography of depression, and 400.41ha is under the highly topographic condition. Table 5.18: Potential level of relative topographic factor for annual cultivation

Land use Potential level Relative topography Area (ha) Percent (%) purpose High potential Flat 1785.06 12.80 Annual Moderate potential Low flat, upper flat 8699.13 62.40 cultivation Low potential Depression, high 3238.29 23.23 Very low potential Slope > 250 219.33 1.57 Sum 13941.81 100

Figure 5.9: Potential productivity classification for relative topographic factor

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5.2.5 Final land potential productivity assessment Land potential for agricultural production was examined by using GIS modeling. The model used was a weighted linear combination method which derives from dimensionless analysis site selection techniques suggested by Hopkins (1977). The result of the multiplication of all the score points out which site is better for agricultural use based on the scores and weights expressed in the model. The weights for the other factors and the scores for its variables were calculated based on agricultural experts‘ opinions as well as local conditions. Three main factors of current environmental conditions in the study area, including chemical soil property, physical soil property, and relative topography were overlaid together in one layer. The information about multiple overlays was input into GIS to to find out land potential map for cultivation. Its results were mainly classified as high, moderate, low, and very low potential suitability level for agricultural use. The consequences of the classification indicate that 11585.97ha or 83.10% of the total investigated area is under low to medium potential for agricultural activities, while the smallest area with only 219.33ha, making up 1.57% was determined as very low potential productivity category and only concentrated on the Leptosols group with the soil depth of less than 30cm. The classification of land potential assessment is showed in Table 5.19 and Figure 5.10. The results of land potential productivity assessment indicate that the highly potential level for agricultural production is only located in the Fluvisols group and with soil depth of more than 70cm and soil texture of silty clay loam. It was 733.77ha, covering 5.26% of the evaluated area. The results also show that the moderate potential level prevails in different types of soil groups such as the Fluvisols, salic Fluvisols, Acrisols, Arenosols, and Gleysols with different types of soil texture and the soil depths fluctuated from 50cm to 70cm. The low potential level for growing crops is mainly located in the Arenosols, Acrisols and a part of the Fluvisol groups with 1402.74ha, equivalent to 10.06%. It has different soil textures such as loam, clay loam and coarse sand with soil depth levels from 30cm to 50cm. Table 5.19: Land potential productivity assessment for agricultural use

Land use Potential level Area (ha) Percent (%) purpose High potential 733.77 5.26

Annual Moderate potential 11585.97 83.10

cultivation Low potential 1402.74 10.06 Very low potential 219.33 1.57 Sum 13,941.81 100

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Figure 5.10: Assessment of Land potential productivity for agricultural use

5.3 Land suitability evaluation of the selected crops in the study area This part presents the results of the land suitability evaluation for selected annual crops by using the simple limitation, parametric methods, and GIS techniques combining with multi-criteria approaches. The suitability will perform the cultivated area of different suitability classes for each land assessed unit by specific figures on the maps. Based on the data of the land unit maps of the district, together with the current land use information of 2012, and the investigation, it can be noted that various kinds of crops may be located in the same land unit, in other words, in the same land unit may grow different agricultural crops. This reflects the diversity of land utilization types. Therefore, it is necessary to evaluate the suitability level of main annual crops and the capability of each land unit in order to meet the requirement of sustainable land use. The classification of land suitability level for a certain crop is the comparison between the ecological crop requirement for growth and the characteristics of each land unit. FAO guidelines for land evaluation (FAO, 1976, 1985, 1993) have been modified to be compatible with Vietnamese condition by the Vietnamese soil scientists. To particularly apply to the fact of natural

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Results of the Research condition of the study area, three methods as aforementioned were used to evaluate the land suitability for selected annual crops.

5.3.1 Selection of criteria for building land mapping units (LMU) In land evaluation, land utilization system is generated by combining land mapping unit and land utilization types. LMU is defined as a piece of parcel of land which is specifically described in the map of land units with distinct characteristics of land and nature homogeneously suitable for certain crops, under the same land management condition, production and land improvement capacity. LMU is produced by synthesizing different thematic maps, which show various characteristics and properties of soils. LMUs have a close relationship ecological with the naturally environmental condition of each area. Thus, it is necessary to collect materials of the ecological environment, land resources and production capacity of the examined area before carrying out the identification of LMUs. These data are generalized and analyzed to define the quality standards of LMU and arrange criteria on building the map of land units. LMUs are created through overlaying different kinds of thematic maps. Thus, identifying a set of appropriate criteria for generating the land mapping unit influences the ability of land use effectively, so that the achievements of land evaluation are more reliable and meet the requirements of practical production. The land unit data presented on land unit map is the main information source to connect to the land requirement of each agricultural crop. Choosing and decentralizing thematic criteria for building a map of land units have depended on the process of land evaluation by FAO (1976), which was modified to be suitable for the condition of Vietnam and was ratified by Ministry of Agriculture and Rural development (1998). The chosen criteria and the decentralized levels for building land mapping unit is shown in Table 5.20. Quang Xuong is the plain district of Thanh Hoa Province. Based on the natural conditions, experts‘ opinions and the available data sources, decentralized criteria were chosen for building land mapping unit including: soil unit, irrigated condition, drainage capacity, relative topography, soil texture, soil depth, OM, CEC, pH, EC, and BS because of several reasons as the described below: - The kinds of soil under the classification method of FAO-UNESCO reflect the relatively initial concept of good or bad use to a specific crop. To a certain extent, this has impacts on the growth and development of plants. Each crop is only suitable for one or several of certain types of soils. - Irrigated condition, drainage capacity, and relative topography directly influence the growth and development of plants as well as crop yields. Irrigated condition is especially important in the dry season, while drainage capacity is an essential criterion for crops‘ drainage in the rainy season. Relative topography relates to the farming activities

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Results of the Research such as land reclamation, especially affecting irrigation and drainage capability. Thus, it influences land use arrangement and seasonal crop structure. - Soil texture is closely related to the ability to hold water, nutrients, air, and soil temperature. Therefore, it affects the water permeability, porosity, air quality in the soil as well as land reclamation. - Soil depth is the effective depth of a soil for plant growth and development from the surface to a layer that essentially stops the downward growth of plants roots. Each crop may have different requirement of depth for growth. The greater effective depth of soil is, the better the ability to provide more nutrition for crops is. - OM, CEC, pH, EC, and BS are all important criteria of soil chemical properties and provide information about soil fertilizer and the ability of metabolism. This information is strongly important to help land-users make a decision what crops should be planted and what measures they should use rehabilitate and protect the soil after harvest. Table 5.20: Decentralized level and its symbol of criterion for building mapping unit

Criteria Decentralized level Symbol Criteria Decentralized level Symbol Good DR1 > 6.5 - ≤ 7.0 pH1 Drainage capacity Moderate DR2 6.0 - 6.5 pH2 pH > 75cm D1 5.5 - 6.0 pH3 50cm- 75cm D2 < 6.0 pH4 Soil depth 30cm -50cm D3 > 15.0 CEC1 < 30cm D4 CEC 10.0 - 15.0 CEC2 Flat R1 < 10.0 CEC3 Low flat R2 > 8.0 EC1 Relative EC 4.0 - 8.0 topography Upper flat R3 EC2 High R4 < 4.0 EC3 Depressed R5 Luvic Arenosols S1 Actively irrigated I1 Eutric Arenosols S2 somewhat irrigated I2 Cambic Arenosols S3 Irrigation Poorly irrigated I3 Hyposalic Fluvisols S4 None_irrigated I4 Hypersalic Fluvisols S5 Silty loam TX1 Eutric Fluvisols S6 Soil unit Clay loam TX2 Cambic Fluvisols S7 Loam TX3 Dystric Fluvisols S8 Loamy sand TX4 Gleyic Fluvisols S9 Soil texture Sandy loam TX5 Dystric Gleysols S10 Coarse sand TX6 Haplic acrisols S11 Silty clay loam TX7 Dystric leptosols S12 Silty clay TX8 > 50% BS1 > 2.0 OM1 Base 35% - 50% BS2 OM 1.0 - 2.0 OM2 saturation < 35% BS3 < 1.0 OM3

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Table 5.21: Characteristics of land unit in the study area

Land Characteristic of land unit Area (ha) unit I R TX S OM CEC EC pH BS D DR 1 4 5 6 1 3 3 3 4 2 3 1 400.41 2 3 3 4 1 3 3 3 3 1 2 1 160.11 3 3 3 4 2 3 1 1 3 1 2 1 781.38 4 3 3 3 2 3 3 3 4 1 2 2 483.66 5 3 3 5 2 2 3 3 4 1 2 1 494.1 6 3 3 4 3 3 3 3 4 2 1 1 58.14 7 2 1 5 3 3 3 3 2 1 2 1 502.56 8 1 2 3 3 3 3 3 3 1 1 2 247.5 9 1 2 3 4 1 1 1 3 1 2 2 164.34 10 2 3 4 4 3 3 3 4 2 3 1 70.47 11 2 2 3 5 1 1 1 3 1 2 2 30.42 12 2 3 1 6 2 1 1 1 1 2 2 155.79 13 2 3 7 6 1 2 2 2 1 2 2 301.41 14 2 3 3 6 1 2 2 2 1 1 2 63.36 15 1 1 1 8 2 2 2 3 2 2 2 275.4 16 2 3 1 8 3 3 3 3 1 3 2 529.47 17 2 3 3 8 2 3 3 2 1 2 2 469.89 18 2 1 1 7 3 2 3 4 2 2 2 133.92 19 2 3 3 7 1 3 3 4 2 2 2 440.91 20 2 3 2 7 2 2 2 3 1 1 2 122.76 21 2 3 1 7 2 1 2 4 2 2 2 336.78 22 2 1 1 7 2 1 2 4 2 2 2 139.41 23 2 3 2 7 2 2 2 4 3 2 2 517.95 24 2 3 3 7 2 2 2 3 2 2 2 243.45 25 1 2 2 7 2 2 2 4 2 2 2 376.74 26 1 1 7 9 1 2 1 1 1 1 2 300.06 27 1 2 3 9 1 2 1 1 1 3 2 101.88 28 1 4 2 9 2 2 2 4 2 2 2 356.22 29 1 4 1 9 2 2 2 4 1 1 2 930.15 30 1 2 1 9 1 1 1 3 1 2 2 95.85 31 1 2 7 9 1 1 1 2 1 2 2 157.59 32 1 1 1 9 1 2 2 4 1 2 2 433.71 33 1 4 8 10 1 1 1 4 1 2 2 453.6 34 3 3 3 11 3 3 3 4 1 2 2 455.58 35 1 4 1 11 3 3 3 3 1 3 2 61.92 36 4 6 5 12 3 3 3 4 2 4 1 219.33 37 1 2 1 9 1 2 1 4 2 2 2 258.39 38 1 4 3 9 1 2 1 4 2 2 2 406.17 39 1 4 1 9 1 2 1 2 2 2 2 629.82 40 2 3 1 7 1 2 1 2 1 2 2 458.64 41 2 3 3 7 1 2 1 3 2 2 2 405.54 42 1 2 1 9 2 2 2 2 1 2 2 717.03 Sum 13,941.81

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Figure 5.11: Land mapping unit of Quang xuong District

After overlaying all thematic maps together with the help of ArcGIS software, 42 land units were found on the map of Quang Xuong district with the scale of 1:25,000. The more detail information of the properties of 42 land units is presented in Table 5.21. Table 5.21 reveals the fact that the study area has a larger number of land units. The area of each land units varies greatly, from 30.41ha (unit 11) to 930.15ha (unit 29). There is a big difference from the characteristics of soil criteria, and there is no homogeneity. This is a distinctive feature of the coastal sandy land of the district in comparison with other regions of Thanh Hoa province. Therefore, examining soil properties at district level is essential to find out where is the best to allocate suitable crops. Among the 42 land units, Gleyic Fluvisols and Cambic Fluvisols soil has more land units (11 and 10 units, respectively) than the others. Hypersalic Fluvisols, Dystric Gleysols, and Dystric leptosols have only one land unit (unit 11, 33, and 36 respectively). Each soil unit of Luvic

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Arenosols, Hyposalic Fluvisols, and Haplic acrisols has two land units on the map. All of the rest of kinds of soil unit have three land units on the land mapping unit. Each unit of land is located with different land utilization types, and one land utilization type is also distributed in many different units of land, especially in the rice-crop and specialized crop patterns. The survey shows that the district has had a diversity of land use types. However, the achievement of agricultural productivity has only reached an average level due to farming habits, low investment and other disadvantages of the natural conditions. Therefore, in order to use agricultural land stably and effectively, it is necessary to examine the soil characteristics as well as soil properties for the suitability of agricultural use. The attribute and spatial data sources of land units of the Quang Xuong district are stored and managed with ArcGIS software. The land mapping units associated with attribute data of the study area are shown in Table 5.21 and Figure 5.11.

5.3.2 Selection of the main agricultural crops The selection of a crop for cultivation is based on both sides of its adaptability to current environmental conditions and the acceptance of potential use by land users involved. According to the assessment framework for land management at the International Conference (1991) in Kenya, a type of sustainable land use must satisfy five following key principles: - Maintaining and improving the quality of crops - Reducing the risk of production to a minimum - Protecting natural resources and preventing the land degradation process. - Acceptability in the economic terms. - Being able to be accepted by the society. According to the principles above, in Vietnam, a sustainable type of land use must achieve the most importance of three rules as described below: - Economic sustainability: land utilization types include crops of high economic efficiency, generate large volume of products, and are accepted by the market systems. - Social sustainability: land utilization types attract employees, create more jobs with high income, and ensure a stable life for employees. - Environmental sustainability: types of land utilization need to protect natural resources, soil fertility, and prevent soil from degradation process. From the analysis of economic efficiency (Chapter 3) and the consequences of the investigation indicate that the economic efficiency of rice, potato and sweet potato are lower than the other crops. On the other hand, in order to ensure food security and local cultivation practices as well as consistency in the agricultural development strategies until

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Results of the Research to 2030 of the district and the province of Thanh Hoa (PCTH, 2015) these plants are still concerned as the main annual crops that need to be developed and evaluated. Although sedge, jute, and tobacco plants have reached the highest level of economic efficiency, in this research they are not considered as the major crops for evaluation. They lack information on ecological requirement for the development and the support from the society and are not associated with the agricultural development strategy of the district. The economic efficiency of other land utilization types like maize, groundnut, soybean, sesame, and green pepper are from medium to high level. These crops are encouraged to grow and develop not only for the economic value they bring but also the ability to rehabilitate and protect land environment. For the types of land use for specialized vegetables, this type of land use should be concerned in the future for high economic efficiency because it significantly improved the local residents‘ income. However, they do not take into account to evaluate the suitability level for each land unit in this study. Thus, the annual crops are selected for evaluation of land suitability involving rice, maize, potato, sweet potato, sesame, groundnut, soybean, and green pepper.

5.3.3 Land requirement and determination classes for selected crops Land requirement is the ecological requirement of different crops regarding edaphic and environmental conditions. It suggests that each land utilization type mentioned in the land assessment can grow and sustainably develop. Each type of land use has different basic requirements for growing (Thu and Khang, 1998). Thus, in order to classify the accurate level of suitability, the determination of land requirements of a specific land use type needs to be carefully considered for match with real natural conditions based on three basis groups of requirement as follows: - The growing or ecological requirement for development of crops: each kind of crop is characterized by its own growth and development, so each plant has different requirements for possible growth and development. The factors of crop requirement may include soil type, topography, soil texture, soil fertility, and soil depth. To determine the favorable land requirements for a certain crop, it is needed to refer the research results and documentation related to crops requirements and the local experts‘ opinions on crop growth and development. - Management requirements: these are the requirements relating to the technical features of a specific crop such as land reclamation, market system involving product salability, or cultivation qualification. Therefore, different kinds of crops also have different management requirements of land.

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- Protection requirements: these requests are necessary for sustainable development of land utilization type associated with the input investments to maintain and enhance soil fertility and do not cause any adverse impact to the environment of agricultural production. It also contributes to protect the land from degradation and crops from degeneration. According the guideline by FAO (1976) for land evaluation, crops requirement for growing and developing are determined in the direction of the suitability from high level to low level. S1: Highly suitable, S2: Moderately suitable, S3: Marginally suitable and N: Not suitable. Thanks to the results, the specialists‘ opinions on the relationship between the crop requirement, the natural conditions and the precious documents (MARD, 2005; Beek, 1978a; Sys et al., 1993 ), the land characteristics for suitability classification of given crops are displayed in Table 5.20. All of the characteristics are used to evaluate land suitability for chosen annual crops by using the simple limitation, square root method. For AHP method together with multi-criteria approach, besides the use of the characteristics as mentioned in Table 5.22, the parameter of rural infrastructure (transport, irrigation system) and its variables are also used for assessment of land suitability for different selected crops. Table 5.22: Land characteristics for determination of suitable classes for growing selected annual crops

Level of suitability Land S1 S2 S3 N1 N2 quality 100 95 85 60 40 25 0 4 3 2 1 Rice Soil texture - SiC; SiCL; CL SiL; L SL; LS - cS Topography Low flat Flat Upper flat Depression High

Soil depth > 90 90 - 75 75 - 50 50 - 20 < 20

Irrigation Active Somewhat Poor None - Drainage - Moderate Good Very poor - - BS > 80 80 - 50 50 - 35 35 - 20 < 20

pH 6.5 - 6.0; 6.5 - 7.0 6.0 - 5.5 5.5 - 5.0 5.0 - 4.5 - < 4.5 OM > 2.0 2.0 - 1.5 1.5 - 0.8 < 0.8 - - EC > 6.5 6.5 - 4.0 4.0 - 2.8 2.8 - 1.6 < 1.6 - CEC > 15.0 15.0 - 10.0 < 10 - - - Sweet potato Soil texture SiC; CL; SiCL; SiL SL; L LS - - cS Topography Flat Upper flat High Low flat Depression

Soil depth > 100 100 - 75 75 - 50 50 - 20 - < 20 Irrigation Active Somewhat Poor None - Drainage > 50 50 - 35 35 - 20 < 20 - - BS > 50 50 - 35 35 - 20 < 20

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pH 6.6 - 6.0; 6.6 - 7.2 6.0 - 5.2 5.2 - 4.8 4.8 - 4.5 < 4.5 - OM > 3.0 3.0 - 2.0 2.0 - 1.0 < 1.0 - - EC > 5.0 5.0 - 3.5 3.5 - 2.0 < 2.0 - - CEC > 15.0 15.0 - 10.0 < 10.0 - - - Groundnut Soil texture L; SCL; SL SiCL; SiL; CL LS - - cS Topography Flat Upper flat High Low flat Depression

Soil depth > 100 100 - 75 75 - 50 50 - 25 - < 25 Irrigation Active Somewhat Poor None - Poor and Poor but Drainage Good - Moderate Imperfect imperfect drainable BS > 50 50 - 35 < 35 - - - pH 6.8 - 6.5 6.5 - 6.0 6.0 - 5.6 5.6 - 5.4 < 5.4 - OM > 2.0 2.0 - 1.2 1.2 - 0.8 < 0.8 - - EC > 4.0 4.0 - 2.8 2.8 - 1.6 < 1.6 - - CEC > 20.0 20.0 - 15.0 15.0 - 10.0 < 10.0 - - Maize Soil texture SiC; SiCL; SiL; CL L SL; LS - - cS Topography Flat Upper flat High Low flat Depression

Soil depth > 100 100 - 75 75 - 50 50 - 20 - < 20 Irrigation Active Somewhat Poor None - Poor and Poor but Poor - not Drainage Good Moderate Imperfect imperfect drainable drainable BS > 80 80 - 50 50 - 35 35 - 20 < 20 - pH 6.6 - 6.2; 6.6 - 7.0 6.2 - 5.8 5.8 - 5.5 5.5 - 5.2 < 5.2 - OM > 2.0 2.0 - 1.2 1.2 - 0.8 < 0.8 - - EC > 8.0 8.0 - 5.0 5.0 - 3.5 3.5 - 2.0 < 2.0 - CEC > 15.0 15.0 - 10.0 < 10.0 - - - Potato Soil texture L SiL; SiCL; CL; SL LS SiC - cS Topography Flat Upper flat High Low flat Depression

Soil depth > 90 90 - 60 60 - 40 40 -20 - < 20 Irrigation Active Somewhat Poor None - Poor and Poor but Poor - not Drainage Good Moderate Imperfect imperfect drainable drainable BS 65 - 50; 65 – 80 50 - 35; 80 - 100 < 35 - - - pH 6.3 - 6.0; 6.3 - 6.5 6.0 - 5.6; 6.5 - 7.0 5.6 - 5.2 5.2 - 4.8 < 4.8 - OM > 1.5 1.5 - 1.2 1.2 - 0.8 < 0.8 - - EC > 5.0 5.0 - 3.5 3.5 - 2.0 < 2.0 - - CEC > 15.0 15.0 - 10.0 < 10.0 - - - Sesame Soil texture L; SL; SiCL; CL SiL; SiC LS - - cS Topography Flat Upper flat High Low flat Depression

Soil depth > 100 100 - 75 75 - 50 50 - 30 - < 30

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Irrigation Active Somewhat Poor None - Poor and Poor but Poor - not Drainage Good Moderate Imperfect imperfect drainable drainable BS > 80 80 - 50 50 -35 < 35 - - pH 6.3 - 6.2; 6.3 - 6.5 6.2 - 5.8; 6.5 - 7.0 5.8 - 5.5 5.5 - 5.2 < 5.2 - OM > 2.0 2.0 - 1.2 1.2 - 0.8 < 0.8 - - EC > 6.5 6.5 - 4.0 4.0 - 2.8 2.8 - 1.6 < 1.6 - CEC > 15.0 15.0 - 10.0 < 10.0 - - - Soybean Soil texture SiC; SiCL; CL; SiL L SL; LS - - cS Topography Flat Upper flat High Low flat Depression

Soil depth > 100 100 - 75 75 - 50 50 - 20 - < 20 Irrigation Active Somewhat Poor None - Poor and Poor but Poor - not Drainage Good Moderate Imperfect imperfect drainable drainable BS > 50 50 - 35 35 - 20 < 20 - -

pH 6.5 - 6.0; 6.5 - 7.0 6.0 - 5.6 5.6 - 5.4 5.4 - 5.2 < 5.2 - OM > 2.0 2.0 - 1.2 1.2 - 0.8 < 0.8 - - EC > 5.0 5.0 - 3.5 3.5 - 2.0 < 2.0 -

CEC > 20.0 20.0 - 15.0 15.0 - 10.0 < 10.0 - - Green pepper C<60v; LS; Soil texture SL; L; SiCL; SiL SiC; CL - - cS C<60s; S Topography Upper flat Flat High Low flat Depression

Soil depth > 100 100 - 75 75 - 50 50 - 30 - < 30 Irrigation Active Somewhat Poor None - Poor but Poor and Poor but Drainage Good Moderate Imperfect not imperfect drainable drainable BS > 50 50 - 35 35 - 20 < 20 - -

pH 6.6 - 6.2; 6.6 - 6.8 6.2 - 6.0 6.0 - 5.5 5.5 - 5.2 < 5.2 - OM > 1.5 1.5 - 0.8 < 0.8 - - - EC > 5.0 5.0 - 3.5 3.5 - 2.0 < 2.0 - - CEC > 20.0 20.0 - 15.0 15.0 - 10.0 < 10.0 - - Notes: LS: loamy sand, SL: landy loam, L: loam, SiL: silty loam, SiC: silty clay, SiCL: silty clay loam, CL: clay loam, cS: coarse sand For example, it is a favorable condition and highly suitable for growth of paddy rice if the land has soil texture of silty clay, silty clay loam or clay loam with the effective depth thicker than 75cm, active irrigation, base saturation from 50% to more than 80%, pH from 5.5 to less than 7.0, organic matter from 1.5 to more than 2.0, more than 4.0meq/100g soil of sum of exchangeable basic cation, and more than 10.0meq/100g soil of cation exchange capacity.

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5.3.4 Calculation method 5.3.4.1 Simple limitation method The simple limitation method implies that the crop requirement tables are made for each land utilization type. In the table, each characteristic is assigned the class-level of suitability corresponding to its attributes. Namely, the suitable level of a land unit with a certain type of land use is the most suitable level which has been classified based on land characteristics. The use of simple limitation method is the expression of land characteristics or land properties of land in a relative rating scale. If a certain soil has the best optimal characteristics for the growth of crops means that there are no restrictions. In contrast, when the same soil characteristic is not conducive to the growth of the plant then it has serious limitations. Relatively assessment of land characteristics is usually described in some specific levels of restrictions. Normally, five level scales in the range of degree of limitation are applied when expressing unfavorable features of land to farming. The limitation levels are described below: - No limitations: the characteristic is favorable for plant growth. - Slight limitations: the quality of land is nearly optimal for the type of land use and effects productivity for no more than 20% comparing to optimal yield. - Moderate limitations: crop yields decrease by influence of the characteristic, however, benefit from land utilization type still can be obtained and remains profitable of land use. - Severe limitations: the characteristic has such influence on productivity of certain crops and causes many difficulties for the considered land utilization type. - Strongly severe limitation: this limitation will not only decrease the yield below the profitable level, but also entirely inhibit the use of the land for evaluated land utilization type. The limitation levels are usually expressed as the land classes. This means that each quality of land can be classified as S1 level (highly suitable), S2 level (moderately suitable),

S3 (marginally suitable), N1 level (possible for correction), and N2 level (impossible for correction). In this case, relatively slight or no limitation is known as S1, and moderate and severe limitations are defined as S2 and S3 respectively. N (N1, N2) refers to very servere limitation level. The final level of suitability of each land unit for a specific land utilization type is determined according to the lowest class level of one or more characteristics. For example, if one factor of land unit is at S3, the other factors are at S1 and S2, and then the final suitability of the land unit is ranked at S3.

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5.3.4.2 Square root method This is one of the parametric methods and derives from numerical inferred influences of different land qualities on the potential behavior of a land utilization type. This is an advantageous method because any important productivity factor which can control the rating and the overall rating cannot be a negative number. In this method, the most significant factors are defined and accounted for interactions between such significant factors by simple multiplication or by addition of single-factor indexes. The final rating index of a certain land unit for a particular kind of use is examined by multiplying the ratings of several land characteristics together (De la Rosa et al., 2004). However, a limitation of this method is that the overall final rating may be lower than the rating of each individually considered factor. In this model, each considered factor or characteristic was separately calculated first. Then all of the ratings of separated factors were multiplied together in order to obtain the final rating for defining the suitability level. Mathematical equations (2) as stated in Chapter 4, which can be used to incorporate land characteristics as effects on argicultural productivity. In this study, a numerical rating with a scale from 0 to 100 is assigned to different suitability categories for the selected annual crops. If a land characteristic has no limitation for crop growth or production, ratings from 100 to 95 are allocated. Ratings from 95 to 85,

85 to 60, 60 to 40, 40 to 25, and 25 to 0 are applied for S1, S2, S3, N1, and N2, respectively. Determining classes of land suitability for a selected crop is based on fitness indicators obtained from the Square root method as shown in Table 4.6 in Chapter 4. For example, the value for OM is 1.0 in land unit A, so what is a rating score of OM for maize in the land unit A? Firstly, reference table 5.20 shows that the measured value falls in S2 class, between 1.2 and 0.8, and its rating should be something between from 85 to 60. A simple mathematical addition and subtraction is made as follows: 85 – 65 = 20, and 1.2 – 0.8 = 0.4. Look through the reference table again; the subtraction should be 1.2 – 1.0 = 0.2. The equation is generated as below: = { => = = = = = The rating of all of the factors in a given land unit needs to be calculated, after that all of them are multiplied for the final index for a special crop‘s assessment. A detail explanation of the algorithm used for rating calculation of all chosen factors or characteristics can be found in SYS et al., (1991).

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5.3.4.3 AHP method combining with multi-criteria approach - The evaluating parameter: Many evaluated parameters and sub-criteria are defined from various viewpoints for the development of agricultural plantation in general and annual crops in particular. After careful consideration and selection based on the specialists‘ opinions conformable to the factual conditions of the locality, three main groups are set up for the evaluation process, including soil property, relative topography, and infrastructural system (level 1). Among the main groups of parameters, chemical property, physical property, rural transportation system, and irrigation and drainage system are defined as the sub-criteria (level 2). Based on the same method, nine unit-criteria (level 3) are identified belonging to the sub-criteria. In which, five sub-unit criterion belong to sub-criterion of chemical property, and four sub-unit criterion belong to sub-criterion of physical property. The detail information on evaluating goal, main parameter, sub-criteria, and sub-unit criteria in the order of priority hierarchy are shown in Figure 4.6 of chapter 4 and Table 5.23. Table 5.23: Name of evaluating main parameter, sub-criteria and sub-unit criteria

Name of evaluated criteria Main parameter Sub-criteria (level 2) Sub-unit criteria (level 3) (level 1) Organic matter (OM) Cation exchange capacity (CEC) Chemical property soil pH reaction (pH) Sum of exchangeable cation (EC) Soil property Base satuaration (BS) Soil texture Irrigation condition Physical property Soil depth Drainage capacity Relative topography - - Rural transportation system - Infrastructure system Irrigation – drainage system -

- Procedure of weight and score of land suitability for selected crops: Deffirent ecological parameters required for selecting annual crop species have deffirent contributions the suitability level; hence, different weights should be given to each parameter and sub-parameter. The identification of three main parameters, four sub- criteria, and nine sub-unit criteria indicate that the suitability evaluation is rather complicated. The weight of each criterion was examined to calculate its influence to the final consequence. Thus, it is the fact that the greater the weight is, the larger the value is and the more important decisive criterion is.

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The opinions of experts are collected and separately assessed. If all of the results are accepted, then all experts‘ opinion will be aggregated to generate a new pairwise comparison matrix by using a geometric mean method. In addition, Consistency Ratio (CR) index is the basic for deciding which comparison result is the best and the most reliable. If the CR is lower than 10%, the answer of each specialist is consistent. On the contrarily, if the CR is above 10%, the expert‘s opinions are not consistent and need to look for the answering again because the matrix in AHP method (Saaty, 1977) is not acceptable. The process of working out the weight and pairwise comparison at all level was through interviewing eight specialists of the land use management first (three experts of planning of land use, three soil scientists, and two agronomist specialists). The results are presented in Table 5.24. Afterward, the geometric mean method with the formula:

= (∏ ) was used to aggregate a comparison matrix of experts‘ judgments. Finally, the eigenvector method was used to calculate the weight of each criterion at all levels as shown in Table 5.25. All results are shown in Appendix 5.1. Table 5.24: The value comparison of different experts for all criteria

Comparison Expert (k)

Level 1 (main parameter) K1 K2 K3 K4 K5 K6 K7 K8 Relative topography 1 4 2 4 2 5 1 4 2.446 Soil property Infrastructure system 4 6 3 6 5 5 4 5 4.643 Relative topography Infrastructure system 2 2 2 3 3 2 3 2 2.328 Level 2 (sub-criterion) Chemical Physical 1.5 1 2 2 2 3 0.5 0.75 1.384 Irrigation; drainage Transportation systems 3 2 1 4 1 1.5 2.5 3 2.013 Level 3 (sub-unit criterion) CEC 2 4 3 3 3 2 4 2 2.769 pH 1 2 1 2 2 0.75 1 1 1.251 OM EC 6 4 4 7 3 3 5 5 4.441 BS 7 5 4 5 4 5 5 7 5.144 pH 0.5 1 0.75 1 0.75 0.75 0.5 0.5 0.692 CEC EC 3 2 2 2 1 1.5 2 2 1.861 BS 5 3 3 2 2 2 2 3 2.611 EC 5 3 2 3 2 4 4 3 3.105 pH BS 6 3 4 2 3 3 5 5 3.663 EC BS 3 1 1 1 1 2 2 2 1.488 Irrigation condition 0.5 1 1 1 2 1 1 1 1.000 Soil texture Soil depth 4 3 3 3 3 3 3 3 3.110 Drainage capacity 2 2 3 2 2 1 3 5 2.276 Soil depth 3 4 4 2 2 2 2 3 2.632 Irrigation condition Drainage capacity 3 1.5 2 1 2 2 1 5 1.914 Soil depth Drainage capacity 0.75 0.5 1 0.75 1.5 1 1 3 1.030

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Table 5.25: Pairwise comparison matrix and calculating in level 1

Soil Relative Infrastructure Main parameters Weight property topography system Soil property 1 2.446 4.643 0.610 Relative topography 0.409 1 2.328 0.267 Infrastructure system 0.215 0.430 1 0.123

CR = 0.005 = CI = 0.003 RI = 0.58 1.000

CI: Consistency Index; = ( )

CR: Consistency Ratio; = ( )

RI: Random Index; : The maximum eigenvalue n: The numbers of criteria in each pairwise comparison matrix

Table 5.25 illustrates that the weight of three main parameters including soil property, relative topography, and infrastructure system by conducting AHP method are 0.610, 0.267, and 0.123 respectively in the study area for annual agricultural crops. Of those weights, the soil property is claimed to be the most important factor because it is the highest weight. The second weighted criterion is the relative topography criterion. The least important is the infrastructure system with the weight of 0.123. The CR index in Table 5.23 is 0.005, less than 0.1 or 10%, so the weights of main parameter are satisfactory and reliable. The same procedure and the method were also employed for determining the weights of level 2 and level 3. The CR values of both levels are less than 0.10, so the weights are acceptable (Appendix 5.1). The score of class for each criterion is based on the crops requirements for growth, the experts‘ judgments and as well as local natural conditions. The ranking score implies the suitability level of each class‘ factor for a specific selected crop. The ecological factors on environmental suitability were classified into four suitable levels, involving highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and not suitable (N). The values of each suitable level were assigned as 4, 3, 2 and 1, respectively (Table 5.20 and Appendix 5.2). After the values of the weights and scores were figured out, they were transferred to and stored in ArcGIS to evaluate the final suitability level for each kind of selected crop corresponding to each land unit. The weights and score values of each criterion were created as thematic maps for the overlaying process, following the formula (3) presented in Chapter 4. The suitability level of different land units for annual agricultural crops is defined according to the suitability indexes in Table 4.10 of chapter 4.

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5.3.5 Suitability of land area for paddy rice crop The ultimate evaluation of the physical land suitability for paddy rice crop using simple limitation, parametric (square root), and AHP combining with MCE is given in Table 5.26 and land area suitability in Figure 5.12. Table 5.26: Suitability level area for growing paddy rice in Quang Xuong District

SL SQ AHP Suitability Area Percent Suitability Area Percent Suitability Area Percent class (ha) (%) class (ha) (%) class (ha) (%) - - - S1 395.91 2.84 - - - S2 5266.17 37.77 S2 3177.36 22.79 S2 2877.57 20.64 S3 7997.76 57.37 S3 5992.47 42.98 S3 7614.63 54.62 N1 58.14 0.42 N1 3467.61 24.87 N1 2776.32 19.91 N2 619.74 4.45 N2 908.46 6.52 N2 673.29 4.83 Sum 13941.81 100 Sum 13941.81 100 Sum 13941.81 100

Table 5.26 reveals the range changes amongst class levels for rice cultivation according to simple limitation, parametric and AHP methods that are S2 – N2, S1 – N2, S2 –

N2, respectively. Based on Limitation method, 5266.17ha of the investigated area or

37.77% is classified at moderate suitability (S2) level for paddy rice production, 7997.76ha or 57.37% of marginal suitability (S3), and 58.14ha, making up 0.42% of corrigible suitability class (N1). 619.74ha, occupying 4.45% of the study area is found to be as permanently unsuitable (N2) class for paddy rice. According to the parametric method, about 395.91ha or 2.84% of the investigated area is classified at highly suitable level (S1), 3177.36ha or 22.79% at S2, and 5992.47ha accounting for 42.98% at S3 class for rice production, respectively. This method also shows that the currently unsuitable and permanent unsuitable classes for rice in this research area are 3467.61ha or 24.87% and 908.46 or 6.52%, respectively. The AHP method combining with multi-criteria evaluation approach was also used to assess land suitability map for rice cultivation. Like the simple limitation method, no land area at S1 level is found out for rice crop in the district regarding to AHP method. The number of hectares available to each suitability class, identified by weight overlay using spatial analyst toots in ArcGIS 10.2, shows that the research area has 2877.57ha at moderately suitable, 7614.63ha at marginally suitable, 2776.32ha at currently unsuitable, and 673.29ha permanently unsuitable levels for rice crop which represented 20.64%, 54.62, 19.91%, and 4.83% of land area, respectively.

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5.3.6 Suitability of land area for sweet potato crop Table 5.27: Suitability level area for growing sweat potato in Quang Xuong District

SL SQ AHP Suitability Area Percent Suitability Area Percent Suitability Area Percent class (ha) (%) class (ha) (%) class (ha) (%) S1 733.77 5.26 S1 300.06 2.15 - - - S2 3153.60 22.62 S2 2406.33 17.26 S2 4823.37 34.60 S3 6596.82 47.32 S3 7299.09 52.35 S3 6661.35 47.78 N1 2837.88 20.36 N1 2324.43 16.67 N1 2142.10 15.36 N2 619.74 4.45 N2 1611.54 11.56 N2 414.99 2.98 Sum 13941.81 100 Sum 13941.45 100 Sum 14041.81 100

Table 5.27 and Figure 5.13 illustrate that three applied methods produced different results of the suitability class for sweet potato. According to the simple limitation method, the highly suitable land for sweet potato crop is 733.77ha, accounting for 5.26% of the agricultural area. Regarding this method, 3153.60ha, accounting for 22.62% of the study area is examined as moderately suitable and 6596.82ha or 47.32% of the cultivated land is classified as marginally suitable levels. The total unsuitable area for sweet potato is about 3457.62ha, amounting to 24.81%, of which 2537.88ha is defined as currently unsuitable land, accounting for 20.36% and 619.74ha or 4.45% of the agricultural area is evaluated as permanently unsuitable area for sweet potato crop.

The results from the square root method show that the best land suitability (S1) for sweet potato is about 300.06ha, accounting for 2.15% of the cultivated area. The moderately suitable land for that plant by applying this approach is 2406.33ha, equivalent to 17.26% of the investigated area. Table 5.25 indicates that more than a half of the total agricultural land area is classified under the marginally suitable for sweet potato crop. It is about 72.99ha, making up 52.35%. The total unsuitable area for the crop by using parametric method is about 3935.97ha, equivalent to 28.23%, of which 2324.43ha or 16.67% is assessed as corrigible unsuitable area and 1611.54ha, amounting to 11.56% of the area is classified under permanently unsuitable class for growing sweet potato. AHP method shows that the land with a high level of suitability for sweet potato crop is not found out in the study area. Regarding this method, the largest evaluated area is fell into the marginally suitable level for growing sweet potato. It is about 6661.35ha, accounting for 47.87%. The next largest evaluated area belongs to the moderately suitable class with 4823.37ha or 34.60% of the investigated area. Only 414.99ha, equivalent to 2.98% and 2142.10ha, accounting for 15.36% of the agricultural land are classified under the permanent suitable class and the current unsuitable class for sweet crop, respectively.

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5.3.7 Suitability of land area for groundnut crop The final evaluation of the physical land suitability for groundnut is shown in Table 5.28 and land area suitability in Figure 5.14. Table 5.28: Suitability level area for groundnut in Quang Xuong District

SL SQ AHP Suitability Area Percent Suitability Area Percent Suitability Area Percent class (ha) (%) class (ha) (%) class (ha) (%) S2 1920.87 13.78 S2 880.02 6.31 S2 1119.87 8.03 S3 5280.93 37.88 S3 4386.33 31.46 S3 7239.96 51.93 N1 6120.27 43.90 N1 6024.24 43.21 N1 4083.57 29.29 N2 619.74 4.45 N2 2651.22 19.02 N2 1498.41 10.75 Sum 13941.81 100 Sum 13941.81 100 Sum 13941.81 100

Table 5.28 shows that there is no any instance of highly suitable level in the study area by conducting three different methods as mentioned above. The dominant suitability classes are as follows: N1 for simple limitation with 6120.27ha, N1 for parametric (square root) with 6024.24, and S3 for AHP method with 7239.96ha. These classes make up

43.90%, 43.21%, and 51.93% of the cultivated area, respectively. The S2 class for the crop using simple limitation method, parametric method, and AHP approach is 1920.87ha or 13.78%, 880.02ha or 6.31%, and 1119.87ha, or 13.78%, respectively. The assessed results of the simple limitation show that 5280.93ha, equivalent to 37.88% and 619.74ha, amounting to 4.45% are classified as S3 and N2 levels while these levels by applying the square root method are 4386.33ha, equivalent to 31.46% and 2651.22ha, accounting for 19.02%, respectively. Regarding the AHP method, 4083.57ha or 29.29% and 1498.41ha or

10.75% of the area are found out as N1 and N2 level for groundnut cultivation, respectively.

5.3.8 Suitability of land area for maize crop The final evaluation of the physical land suitability for maize is presented in Table 5.29 and land area suitability in Figure 5.15. Table 5.29: Suitability level area for maize crop in Quang Xuong District

SL SQ AHP Suitability Area Percent Suitability Area Percent Suitability Area Percent class (ha) (%) class (ha) (%) class (ha) (%) S1 300.06 2.15 S1 300.06 2.15 - - - S2 2496.24 17.90 S2 1847.25 13.25 S2 2186.19 15.68 S3 5534.73 39.70 S3 3906.90 28.02 S3 6702.39 48.07 N1 4991.04 35.80 N1 6276.06 45.02 N1 4002.48 28.71 N2 619.74 4.45 N2 1611.54 11.56 N2 1050.75 7.54 Sum 13941.81 100 Sum 13941.81 100 Sum 13941.81 100

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(a) Simple Limitation (b) Square root (c) AHP Figure 5.12: Suitability map for paddy rice using different methods

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(a) Simple Limitation (b) Square root (c) AHP Figure 5.13: Suitability map for sweet potato using different methods

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Table 5.29 and Figure 5.15 describe that the highly land suitable (S1) for maize cropping covers an area of around 300.06ha, accounting for 2.15% of the investigated area by using both of simple limitation, and square root methods. On the other hand, no land with a high level of suitability for the same crop is found out based on AHP method. According to the Table, the prevalent suitability classes are as follows: marginal suitability for maximum limitation with 5534.73ha, current unsuitability for square root with 6276.06ha, and marginal suitability for AHP with 6702.39ha. These classes make up 39.70%, 45.02%, and 48.07% of the surface area, respectively. Regarding those applied methods, there are about 2496.24ha, equivalent to 17.90%, 1847.25ha, accounting for 13.25%, and 2186.19ha, making up 15.68% of the cultivated area classified as moderately suitable class by applying simple limitation, square root, and AHP methods respectively. The results of the land suitability evaluation also shows that the total unsuitable classes for growing maize crop related to simple limitation method are 5610.78ha, of which 4991.04ha or 35.80% is assessed as currently unsuitable level and 619.74ha, occupying 4.45% of the area is classified under permanently unsuitable level while the total unsuitable classes from the consequences of AHP approach are 5053.23ha, of which 4002.48ha, equivalent to28.71% is classified as the currently unsuitable class and 1050.75ha, accounting for 4.45% of the area is defined under the permanently unsuitable class. According to the square root method, about 3906.90ha or 28.02% and 1611.54ha or 7.54% of the whole evaluated are respectively found out as marginally suitable and permanently unsuitable for maize cultivation in the study.

5.3.9 Suitability of land area for potato crop The results of the physical land suitability for potato crop by conducting three different methods are presented in Table 5.30 and land area suitability in Figure 5.16. Table 5.30: Suitability level area for potato crop in Quang Xuong District

SL SQ AHP Suitability Area Percent Suitability Area Percent Suitability Area Percent class (ha) (%) class (ha) (%) class (ha) (%) S1 300.06 2.15 S1 300.06 2.15 - - - S2 2929.95 21.02 S2 3120.03 22.38 S2 4322.07 31.00 S3 6020.91 43.19 S3 6144.84 44.07 S3 4179.96 29.98 N1 4071.15 29.20 N1 2925.45 20.98 N1 2371.86 17.01 N2 619.74 4.45 N2 1451.43 10.41 N2 3067.92 22.01 Sum 13941.81 100 Sum 13941.81 100 Sum 13941.81 100

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The assessment results of land suitability from Table 5.30 and classified maps in Figure 5.16 reveal that 300.06ha, equivalent to 2.15% of the investigated area is found as highly suitable land for potato crop by using both methods of simple limitation and square root. However, there is no instance of highly suitable class found out by applying AHP method to evaluate land suitability for potato cropping. According to the simple limitation method, nearly a half of the agricultural land is under the marginally suitable for potato cultivation, it covers 6020.91ha, or 43.19%. About 2929.95ha or 21.02% is categorized under the moderately suitable level. Regarding this method, the total unsuitable classes are 4690.89ha, of which 4071.15ha, equivalent to 29.20% of the total area is categorized as currently unsuitable class and 619.74 ha or 4.45% is classified as permanently unsuitable. In comparison, the results of the square root method show that 6144.84ha, making up 44.07% of the cultivated land is assessed as marginally suitable level. The results of this method are also presented that the total area of moderately suitable class is about 3120.03ha, equivalent to 22.38%. Based on this method, 2925.45ha or 20.98% of the cultivated area is corrigible not suitable and 1451.43ha, covering 10.41% is fell under permanently unsuitable for potato cropping in the study area. The results by the AHP method, on the other hand, show that the largest area with 4322.07ha, accounting for 31.00% of the study area is classified under the moderately suitable class for potato cropping. The next largest evaluated area belongs to the marginally suitable class with 4179.96ha, or 29.98%. The total unsuitable area for potato crop based on this method is about 5439.87ha, of which 2371.86ha, equivalent to 17.01%% of the total area is examined as currently unsuitable class and 3067.92ha or 22.01% is classified as permanently unsuitable for growing potato crop.

5.3.10 Suitability of land area for sesame crop The results of the physical land suitability for sesame crop by applying three different methods are presented in Table 5.31 and land area suitability in Figure 5.17. Table 5.31: Suitability level area for sesame in Quang Xuong District

SL SQ AHP Suitability Area Percent Suitability Area Percent Suitability Area Percent class (ha) (%) class (ha) (%) class (ha) (%) S1 300.06 2.15 S1 300.06 2.15 - - - S2 2496.24 17.90 S2 1847.25 13.25 S2 2347.56 16.84 S3 5534.73 39.70 S3 3906.90 28.02 S3 7455.24 53.47 N1 4991.01 35.80 N1 6276.06 45.02 N1 3127.77 22.43 N2 619.74 4.45 N2 1611.54 11.56 N2 1011.24 7.25 Sum 13941.78 100 Sum 13941.81 100 Sum 13941.81 100

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(a) Simple Limitation (b) Square root (c) AHP Figure 5.14: Suitability map for groundnut using different methods

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(a) Simple Limitation (b) Square root (c) AHP Figure 5.15: Suitability map for maize using different methods

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As the consequences of land suitability evaluation show in Table 5.31and Figure 5.17 for sesame crop, there is only 300.06ha of agricultural land, accounting for 2.15% of the assessed area land classified as highly suitable by using simple limitation and square root methods. Nevertheless, no area is classified as highly suitable class for sesame growth by applying the AHP method. Regarding the simple limitation method, the largest area with 5534.73ha, making up 39.70% of the total area is categorized under the marginally suitable class, and 2496.24ha, equivalent to 17.90% is evaluated as moderately suitable class. Together, the two categories of moderate suitability and marginal suitability classes make up 8030.97ha of the total area. This method also shows that there is about 4991.01ha, equivalent to 35.80ha examined as currently not suitable level and 619.74ha, equivalent to 4.45% of the total area is under permanent unsuitable for growing sesame crop. The results by the square root method show that nearly a half of the investigated area, with 6276.06ha, accounting for 45.02% is classified as corrigible land for sesame cropping. The moderately suitable land for the crop is 1847.25ha, covering about 17.26% of the cultivated area. According to this method, about 3906.90ha, making up 28.02%, is found as marginally suitable level, while the permanently unsuitable assessed is about 1611.54ha, equivalent to 11.56 for the cultivating sesame crop. In comparison, the results by the AHP method show that more than a half of the study area is at the marginally suitable level, which about 7455.24ha, or 53.47% of the total area. Based on this method, 2347.56ha is evaluated as the moderately suitable, 3127.77ha as currently not suitable, and1011.24ha as permanently not suitable for sesame cultivation. These classes cover 16.84%, 22.43%, and 7.25% of the surface area, respectively. The results from the AHP method are quite different in comparison with the results of the simple limitation and square root methods.

5.3.11 Suitability of land area for soybean crop The final results of the physical land suitability for soybean by applying three different methods are presented in Table 5.32 and the land area suitability in Figure 5.18. Table 5.32: Suitability level area for soybean in Quang Xuong District

SL SQ AHP Suitability Area Percent Suitability Area Percent Suitability Area Percent class (ha) (%) class (ha) (%) class (ha) (%) S2 2796.30 20.06 S2 1251.27 8.97 S2 1794.24 12.87 S3 5158.00 37.00 S3 5139.72 36.87 S3 7475.85 53.62 N1 5367.77 38.50 N1 5939.28 42.60 N1 3660.48 26.26 N2 619.74 4.45 N2 1611.54 11.56 N2 1011.24 7.25 Sum 13941.81 100 Sum 13941.81 100 Sum 13941.81 100

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Table 5.32 and Figure 5.18 demonstrate that different methods develop different results of the land suitability assessment for soybean crop. According to the Table, there is no area classified as highly suitable for all methods of simple limitation, square root, and AHP combining with multi-criteria evaluation approach. Regarding the simple limitation method, the moderately suitable land for soybean is 2796.30ha, covering about 20.06% of the agricultural area. 5158.00ha, accounting for 37.00% of the study area is considered as marginally suitable class. The total unsuitable area for this kind of crop is about 5987.51ha, equivalent to 42.95%, of which 5367.77ha, accounting for 38.50% is defined as currently unsuitable land and 619.74ha or 4.45% of the agricultural area is evaluated as permanently unsuitable, respectively. The results by the square root method show that the best class land suitability for soybean crop with 1251.27ha is of moderate suitability, accounting for 8.97% of the cultivated area. The marginally suitable area for that kind of plant is 5139.72ha, equivalent to 36.87%. Based on this method, about 7550.82ha is classified under the unsuitable class, covering 54.16% of the investigated land, of which 5939.28ha or 42.60% is found as currently unsuitable level and 1611.54ha, occupying 11.56% of the area is classified under permanently unsuitable class for growing soybean crop, respectively. The results by the AHP method, however, show that the largest area with 7475.85ha, accounting for 53.62% of the study area is classified as the moderately suitable class for soybean crop. The next largest evaluated area belongs to the currently not suitable class with 3660.48, covering about 26.26%. Based on this method, the moderately suitable level for that kind of crop is 1794.24ha, amounting to 12.87% whereas only 1011.24ha or 7.25% of the study area is classified as permanently not suitable for cultivating soybean crop.

5.3.12 Suitability of land area for green pepper The final results of the physical land suitability for green pepper by applying the three different methods are shown in Table 5.33 and the land area suitability in Figure 5.19. Table 5.33: Suitability level area for green pepper in Quang Xuong District

SL SQ AHP Suitability Area Percent Suitability Area Percent Suitability Area Percent class (ha) (%) class (ha) (%) class (ha) (%) S2 2326.41 16.69 S2 1155.42 8.29 S2 4133.70 29.65 S3 6062.76 43.49 S3 5235.57 37.55 S3 6267.60 44.96 N1 4932.9 35.38 N1 6346.89 45.52 N1 3010.86 21.60 N2 619.74 4.45 N2 1203.93 8.64 N2 529.65 3.80 Sum 13941.81 100 Sum 13941.81 100 Sum 13941.81 100

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(a) Simple Limitation (b) Square root (c) AHP Figure 5.16: Suitability map for potato using different methods

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(a) Simple Limitation (b) Square root (c) AHP Figure 5.17: Suitability map for sesame using different methods

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(a) Simple Limitation (b) Square root (c) AHP Figure 2 Figure 5.18: Suitability map for soybean using different methods

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(a) Simple Limitation (b) Square root (c) AHP Figure 5.19: Suitability map for green pepper using different methods

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The results in Table 5.33 and the location suitability map in Figure 5.19 indicate that there is no class of high suitability classified for green pepper cropping by using the three different methods of simple limitation, square root, and AHP. According to the simple limitation method, the best suitable land for green pepper crop is at moderately suitable level with 2326.41ha, accounting for 16.69% of the agricultural area. This model also shows that 6062.76ha, making up 43.49% of the study area is examined as marginally suitable class. The total unsuitable area for this crop is about 5552.24ha, equivalent to 40.83%, of which 4932.90ha is assessed as currently unsuitable land, accounting for 35.38% and 619.74ha or 4.45% of the agricultural area is evaluated as permanently unsuitable area for the green pepper crop. The results by the square root method show that the currently unsuitable class makes up the largest area for green pepper with 6346.89ha, accounting for 45.52% of the cultivated area. The next largest area belongs to marginally suitable level with 5235.57ha, equivalent to 37.55%. Based on this method, the moderately suitable class is only 1155.42ha, at 8.29% of the investigated area whereas 1203.93ha, covering 8.64% is classified as the permanently unsuitable for green pepper in the research area. According to the AHP method, the largest evaluated area falls into the marginally suitable level for cultivating green pepper crop. It is about 6267.60ha, accounting for 44.96%. The next largest evaluated land for this kind of crop belongs to the moderately suitable class with 4133.70ha, making up 29.65% of the investigated area. Regarding this model, 3540.51ha, equivalent to 25.40% of the total surface area is evaluated as unsuitable class for green pepper growth, of which 3010.86 is assessed as currently unsuitable land, accounting for 21.60% and 529.65ha or 3.80% is evaluated as permanently unsuitable area for green pepper crop, respectively. More details of land suitability assessment for the selected agricultural crops of rice, sweet potato, groundnut, maize, potato, sesame, soybean, and green paper by applying the simple limitation and parametric method (square root) are presented from Appendix 5.3 to Appendix 5.18.

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Chapter 6. Discussion of the Results

6.1 Image classification and land cover change from 1989 to 2013 Land cover monitoring of Quang Xuong District over-time demanded a specific dataset of Landsat imageries in order to meet different local land use changes. This was one of the first important tasks in the project of land use planning and land evaluation. Moreover, monitoring of land cover also provided precious information for land users, decision makers, and land planners to make reasonable development strategies of land use in the short-term as well as in the long-term. On the other hand, not any studies had been applied remote sensing and satellite images to analyze land use change in the district before. At the time, the land cover distribution of the proportion from 1989 to 2013 in this research was the very first study of land cover change detection by applying remote sensing techniques and Landsat images in the district. In this study, both of the satellite images of 1989 and 2013 were acquired in September. In addition, the crop calendar, related official ancillary data, the author‘s knowledge, and interview data from local people were also used for references. The results of image classification in Chapter 5 show that the overall accuracy of the land cover was 94.08% for the image classification of 1989, and 92.91% for the image classification of 2013. The consequences were quite good, although there was no existing standard for image classification (Treitz and Rogan, 2004). Especially, the producer accuracy of salty land class and the user accuracy of salty land and forest land classes of classified image 2013 were below 70%, the majority ranged from 77.78% to 100%, which was satisfactory considering the diverse land use categories in Quang Xuong District. There were some classification errors due to the spectral mixture between cultivated lands and built-up areas after harvest, between unused lands and built-up areas, between water surface and agricultural areas, between salty lands and built-up areas, or between the forest lands and cultivated lands. According to Marble et al., (1983) the results from classified images were deficient, and one of the method to improve such results was generating training classes. Thus, careful selection of training sites and majority filter supervised classification were applied in order to reduce these issues. The comparison of different classes in 1989 and 2013 shows that there was a witnessed land use and land cover change during the study period of 24 years. The major land use transitions were the conversions of agricultural lands, water surfaces, and unused lands into built-up areas, agricultural lands into water surfaces and unused lands into forest lands. During the 1989–2013 periods the percentage area covered by agriculture, water

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Discussion of the Results surface, and unused classes decreased by 22%, 17%, and 91% respectively. By contrast, the area covered by built-up, forest, and salty classes increased by 78%, 751%, and 58% respectively. The ―Doi Moi‖ policy in 1986, the economic reforms initiated by Vietnamese government with the reorganization of generating a ―socialist-oriented market economy‖ from the centrally planned economy or the commanded economy, was one of the main reasons for the change of land use area in Vietnam (Boothroyd and Nam, 2000). The results show an increase in forest land with 395.76ha from 1989 to 2013, of which 233.56ha from unused lands, 126.23ha from cultivated lands, 15.34ha from water surface areas, 7.2ha from salty lands, and 40.69ha from built-up areas. The major causes of this conversion included the policy mentioned above, and the strategy to expand the protected forest land of coastal area in order to protect agricultural lands from the salt intrusion issue, and sandy invasion from the sea (PCQX, 2012). Moreover, population growth, the economic development, the expansion of rural infrastructures such as new transportation and irrigation networks, and socio-development can account for the regarded extent in built-up areas (PCTH, 2015). More specifically, new administrative buildings as well as new schools, markets, and clinics had been constructed by all of the communes of the district. The increasing built-up lands in 2013 comparing to 1989 was about 4011.78ha, in which 3323.18ha, 584.66ha, 499.55ha, 36.96ha, and 13.18ha were conversed from cultivated lands, water surface areas, unused lands, salty lands, and forest lands, respectively. This expansion of built-up areas correlated with the increase of 11% in population observed over 20 years (PCQX, 2012). The development and increase of the built-up areas reflected an improvement of the economic and social situations within Quang Xuong District. In contrast, the growth of rural infrastructure had been considered as a cause to destruct the environment and bring more negative impacts from natural environment such as water and air pollution, decrease of biodiversity and natural resources, and breaking food security (Stewart et al., 2004, Yin et al., 2005, Grimm et al., 2008, andShi et al., 2012). The consequences of land cover change statistics indicated that agricultural lands decreased about 3161.82ha from 1989 to 2013. The reduction of cultivated areas was mostly impacted by the rise of aquaculture ponds and the expansion of built-up areas. The main reasons for the change of cropping lands into built-up areas were the growth of population and the socio-economic development. The trends are similar to those from other case studies in Vietnam or Asia (Weng, 2002, Binh et al., 2005, and Byomkesh et al., 2012). A significant conversion of agricultural lands into aquaculture ponds was mostly

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Discussion of the Results located in Quang Trung and Quang Chinh Communes of the district. However, cultivated land was still the largest land use type in the case study over 24 years. The results of image classification in this study illustrate that the Maximum Likelihood supervised method is a useful tool for classifying and mapping broad categories of land cover. In addition, change detection statistics is a helpful approach for determining the change of land cover/land use in different period of times. These results clearly suggest satellite images of Landsat could be used to identify, classify and compare the change of land cover types in Quang Xuong District in particular and Thanh Hoa Province in general.

6.2 Land potential productivity assessment Optimizing land use to obtain the highest benefit of agricultural production is the major objective of the soil investigations. The use of land is not only depending on the land user but it is also determined by the land capability. The land capability is controlled by its different attributes such as the soil types, soil physical, and soil chemical properties, topography, and climate conditions. The land qualities are the most important input factors contributing to the growth and development of the agricultural crop and its yields. The land capability is a primary step in land evaluation process for agricultural purposes (Klingebiel and Montgomery, 1961). Any abused exploitation of land or exceeded utilization of its capability will lead to degradation and yield reduction in a long-term (Loi, 2008). Thus understanding the land with respect to its potential is an appropriate way to increase production per unit area and protect the land from the degradation of quality. Application results of the land potential productivity assessment is meaningful for making land use planning policies and developing strategies for farm management, or for each particular site and socio-economic concern. In this study, land potential productivity assessment was regarded as the chemical, physical properties of the land (Soil pH, CEC, OM, EC, BS, soil depth, soil texture, irrigation condition, and drainage capacity) plus relative topographical factor which all examine how a specific land could be used for cultivated annual crops production without destroying its potential. It also took into account limitations that might affect use of agricultural land. Land potential productivity assessment also included features of the climate, and did not take into account the socio-economic factors such as the market systems, ability of management, or agricultural development strategy as the parameters in the process of assessment. Land potential productivity assessment was quite different from land suitability evaluation. Assessment of land potential provided a ranking of land for general scales of cultivated uses, whereas land suitability evaluation requires much more detailed land resource data and it was applied to define more specific level of suitability for

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Discussion of the Results a certain kind of land use (Grose, 1999) such as the suitability level for rice or for groundnut. Several models such as Cervatana, Storie, Parametric, or USDA were applied to evaluate land potential for agricultural uses in different parts of the world and the different results could depend on land characteristics or natural condition of the study areas (Sayed, 2013). After 1975s, a large number of studies on land evaluation under the FAO framework in Vietnam had been conducted by Vietnamese scientists such as Pham Quang Khanh (1990), Cao Liem (1991), Ton That Chieu and Nguyen Cong Pho (1991), Le Duy Thuoc (1992), Le Van Khoa (1993), Dao Chau Thu (1993), Nguyen Khang and Do Dinh Dai (1994),Vu Thi Binh (1995), Nguyen Cong Pho (1995), and Pham Quang Khanh (1995). However, most of these studies concentrated on natural conditions, and soil fertilities to generate the soil maps and the land mapping units for general land use planning in larger regions like the Red River delta, Mekong delta or the Midland and Northern mountainous region at map scales from 1:250,000 to 1:500,000. According to Loi (2008), most of the studies on land potential conducted in Vietnam paid attention to agricultural production. Furthermore, these studies used traditional methods to create maps and collect information on the land potential productivity which required time and finance, and GIS technique could be a useful tool in land potential productivity assessment to cope with the dynamics and competitive nature of agricultural production. More recently, several studies (Chuong, 2008a), (Tri and Tri, 2004), (Khanh and Dinh, 2004), (Giap et al., 2005), and (Khoi and Murayama, 2010) applied GIS techniques combining with multi-criteria analysis in land evaluation at district and commune levels for a particular use of land. The findings demonstrate that integrating GIS with multi-criteria evaluation in land evaluation achieved better results than traditional methods. The land potential productivity assessment in this case study attempts to point out the inherent capability of the land characteristics for agricultural production and does not try to depict a specific kind of land uses, or grade the land value for any particular agricultural use. It is an important step in determining a particular land regardless of its level of arability, and figuring out the limitations or hazards for cultivated crop commodity based on soil characteristics. The results of defining potential areas for agricultural production using GIS techniques in combining with Linear Combination Method (Hopkins, 1977) in this study were divided into four levels as highly potential, moderately potential, low potential, and very low potential productivity, respectively, in which, highly potential, moderately potential and low potential are considered as suitable for agricultural

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Discussion of the Results production, while very low potential is not suitable for croplands because of extremely severe limitations or hazards, but it can be used for permanent vegetation like forest or natural plant covering. Based on the outcome analyses in Table 5.17 and Figure 5.10 of Chapter 5, it can be concluded that 733.77ha was classified as high potential for annual cultivated crops in the case study, which was mainly located in soil unit of the Gleyic Fluvisols group, with flat topography, silty loam and silty clay loam of soil texture, and high concentration of organic matter content. It also had an actively irrigated system and its effective depth of soil was more than 75cm. These lands are highly suited to a wide range of intensive cropping and agricultural activities. This result also indicates that there were virtually no limitations to agricultural practices or any reasonable management inputs were needed to protect the resource from degradation. The land with a high level of suitability was highly productive and capable of being cultivated for a long-term. Regarding the consequences in Table 5.17, only 219.33ha was classified under the very low potential level. It was located in soil unit of the Dystric Leptosols group with sandy loam of soil texture and very high topography. In addition, it had very low organic matter content in the soil surface, and the soil depth less than 30cm. This class had low productivity, high risk of erosion, low natural fertility, and other limitations that restrain cultivated activities. Therefore, this land was unsuitable for agricultural development, especially for annual crops and it should have been used for natural vegetation cover. According to the consequences of land potential productivity assessment, most of investigated area, about 83.10% was determined as moderate potential class. This class was distributed in different types of soil groups such as Fluvisols, salic Fluvisols, Acrisols, Arenosols, and Gleysols with the relative topography of flat, low flat or high flat, and the average of soil depth was over 50cm. Its average of surface content of organic matter was also higher than 1.5%. These lands of moderate potential level were suitable for cultivation over long period of time. These areas were highly productive, but they had several slight hazards and limitations in comparing with high potential level, which restricted the choice of crops or require moderate conservation and was in need of slight management inputs to maintain and prevention of degradation of the resource. The analysis of land potential productivity also shows that 5293.80ha, making up 10.06% of the total investigated was classified as low potential level for agricultural production. This level mainly concentrated on Arenosols, Fluvisols, and Arcrisols of soil groups with different texture of soil such as loam, clay loam, and coarse sand and the fluctuation of surface content of organic matter from 0.14% to 1.65%. These lands could

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Discussion of the Results be used for occasional cropping, but severe limitations which will restrict the choice of plants and the length of cropping phase. The limitations of these areas were due to their relative topography, low fertility, soil texture or the shortage of water supply from irrigation system. Therefore, in order to use these lands for agricultural production, it is necessary to apply major conservation treatments and careful management practices to reduce the degradation of the resource. The findings from this study demonstrate that the process of determining the cropland potential productivity was significantly useful to support the land users and land managers finding out the problems in a certain use of agriculture land and provided more information for appropriate investment for cultivated production. Furthermore, the results of land potential productivity assessment was also significant for local land users to make possible strategies for development of agricultural land in short term as well as in a long term of use. Finally, it was helpful for setting up a land information system for sustainable use of land resources and land management not only in the case study, but also in the whole of Thanh Hoa Province.

6.3 Land suitability evaluation for selected crops by using different methods The coastal sandy area in Thanh Hoa Province is characterized by scarcity of land resource for agricultural production and has to confront with natural catastrophes every year such as storms, flood, soil erosion, droughts, and the intrusion of salinity. Therefore, it is needed to pay more attention to protect the land from degradation, especially the land used for cropping cultivation. Furthermore, in order to guarantee food security for the present and the future as well as provide more information about land potential and land suitability for sustainable use and management, it is taken into account that agricultural land is needed to be evaluated for those purposes. In this study, the selected crops for land suitability evaluation were rice, sweet potato, maize, groundnut, potato, soybean, sesame, and green pepper. The three different methods implemented for the land evaluation process, were the simple limitation, parametric (square root) and AHP methods. These methods were adopted by Vietnam after the FAO framework with modifications to suit the local environment. The consequences from this present study figured out different levels of suitability of land units for chosen annual crops. For example, the results of land evaluation suitability for rice in the study area shown that 5266.17ha, 3177.36ha, and 2877.57ha were classified as moderately suitable class (S2) by using the simple limitation, parametric, and AHP methods respectively. The marginally suitable class (S3) made up 7997.76ha, 5992.47ha, and

7614.63ha respectively. Currently unsuitable class (N1) was 58.14ha, 3467.61ha, and

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2776.32ha respectively and permanently unsuitable class (N2) for cultivated crops was 619.74ha, 908.46ha, and 673.29ha respectively. The results also indicate that 395.91ha was classified as highly suitable class (S1) for paddy rice crop by using the parametric method, while no area was determined as level S1 for this crop by applying the simple limitation and AHP methods. The different results of land suitability evaluation for the selected crops presented in Chapter 5 can explain that the suitability level depended on matching of the collected data for each land unit with the crop requirement as mentioned in Table 5.20. This means that, different suitability classes were determined for different land characteristics. In the simple limitation method, the final suitable class of land units for a specific crop was considered to be as the lowest level among suitable classes. In parametric method, differences in results can be explained by the consequences of multiplication of the land suitability ratings in calculating of the land suitability index. Due to multiplication of different land suitability ratings of each parameter, so the final suitability level of each land unit for a selected crop might have been lower, equal or higher in comparing with the simple limitation and AHP methods. In this method, the theory of limitation factors applied (Rabia and Terribile, 2013).

Rmin in Equation (2) of Chapter 4 might be used to control the final results with a minimum weight and misleading results, so the results of land suitability for a particular crop may not reflect a real situation. However, in AHP method, the final suitability class depended not only on the score of land properties, but also counted on the weight of each parameter used in the land evaluation procedure. In this method, the suitability ratings and weights are aggregated over hierarchy levels. Eventually, final suitability map for each agricultural crop was generated by overlaying all of the thematic maps together. Regarding this method, in the weight calculation step of pairwise comparison matrix at the level 1, soil property was assessed as the most important parameter with the weight value of 0.610. The next important parameters were relative topography and infrastructure system with the weight values of 0.267 and 0.123, respectively (Table 5.23). The weight values of sub-criteria and sub-unit criteria at level 2 and level 3 of AHP structure were presented in Appendix 5.1. The results of land suitability evaluation in this study illustrate that applying AHP method in land suitability was more advantageous than using the limitation and the parametric methods. The findings by Rabia and Terribile (2013) suggest that the parametric method may lead to incorrect results of suitability class because of the multiplication of different land suitability ratings and the effect of Rmin. According to Giang (2012), the simple limitation method does not take into account the interaction of the chosen criteria and all of the criteria are considered as equally important roles in the assessment process.

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However, in the AHP method, different criteria play differently important roles and weigh values in the land assessment. In addition, the study conducted by Loi (2008) demostrates that AHP approach can provide superior outcomes in comparing with other methods for determining the weight values and the application of AHP is more effective for standardizing the values of the natural criteria involved in land evaluation. Goma et al., (2015) also concludes that AHP method can manage independence among criteria and alternatives better than parametric method. Thanks to the results of suitability evaluation, the study has identified the hazards and limitations of the region in growing annual crops and other plants. The consequences of land suitability assessment by using three mentioned methods in this study also find out that the most serious limitations for cultivating and developing of selected annual crops are relative topography, soil chemical and physical properties such as low percentage of organic matter content, high acid in soil, shortage of irrigated condition, shallow soil depth or some soil textures not suitable for several agricultural crops. These limitations can affect to evaluated results by themselves or with together. In AHP method, the distance from the rural transportation and irrigation systems to each land unit is also considered as a main parameter for land suitability determination. Therefore, in order to choose the most suitable for a particular crop production, land users should base on the suitability level of each land unit. Moreover, information of hazards and limitations is strongly helpful for land users to make significant strategies and rightful decisions so that the agricultural practices will be economically effective, socially accepted and sustainable in term of natural environment.

6.4 Response to research questions In Vietnam, the application of information technology in land evaluation, land use planning, and management of land resources to develop the society, economy in general and agriculture in particular is still limited and weak. Most studies in the past applied traditional methods in the investigation and evaluation of land resources. Those studies tended to pay more attention to high levels such as a province or a large region and did not combine with or support for other projects. Thus, the findings were not highly effective and required a lot of time and finance. In order to resolve these obstacles, applying new techniques and information technology to land evaluation is concerned as an intelligent method for comprehensive, systematic, and updated information of land resource for cultivated production, especially at the district level where the natural resources is directly explored for livelihood of local people. In this current study, Remote Sensing and GIS were not only applied for generating the land cover maps, analyzing land cover changes, building land potential map, land

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Discussion of the Results suitability maps of different selected crops for agricultural development. They were also used for storing, updating, analyzing data of land resources of the district. The main consequences associated with the research questions described in Chapter 1, are as follows:

Question 1: What are the characteristics of land and land use condition in the coastal sandy area of Thanh Hoa province? The description of natural and socio-economic conditions of the case study area is carried out in the Chapter 3 of this dissertation. This is the baseline data for the selection of the evaluating criteria for the land potential productivity assessment and for selected annual crops in the investigated area. After the analysis of the data collected from the study area, there are several conclusions as follow: Together with the relative flat of topography, the climate conditions of the study area with high precipitation, temperature, sunlight hours, and air humidity which can support a wide range of vegetable crops. This is a favorable precondition for developing special tropical crops and offering high productivity. Nevertheless, irregular annual precipitation distribution could result in waterlogging in rainy season and shortage of water in dry season. The cold Northeastern monsoon and the dry and hot Southwestern monsoon are unfavorable conditions for growing annual crops and other plantations during the flowering and fruiting period. In addition, storms, floods and typhoons in the rainy season also bring many negative effects on agricultural development. The water resources are rich with many small rivers, lakes and ponds which can sufficiently supply the water for the local people‘s demand and cultivation. However, the water flow of rivers alters greatly depending on seasons. The flow increases rapidly, but the drainage systems are not drained promptly in the rainy season. They are both main causes of flood and waterlogging in some parts of the study area. Contrarily, in the dry season, the significant reducing of the water level is the main cause of drought and shortage of water supply for crops. With these limitations, there must be measures to improve the hydrological systems as well as protect water surface resources for sustainable use. The soil resources in the area are diversified. According to FAO-UNESCO classification, there are 6 main soil groups with 12 soil units and 18 sub-units in the agricultural lands. The largest area belongs to Fluvisols group with 9358.29ha, covering 67.11% of the investigated area. The smallest area is Leptosols group with 219.33ha, accounting for 1.57%. The rest belongs to Arenosols, Salic Fluvisols, Gleysols, and Acrisols groups with a total area of 4366.19ha, equivalent to 31% of the investigated area. The diversity of soil types and soil texture is the potential for cultivating different crops and generating biological diversity in agriculture. The analyzed results of physical and

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Discussion of the Results chemical characteristics of soil groups also demonstrate that although some soil types contain certain limitations, but most types of soil in the study area can be used for agricultural production. The socio-economic and the infrastructure status in study area for cultivated production are up to average level. The rural transportation system which is quite good condition for the imports and exports products outside the districts and Thanh Hoa Province. However, the irrigation system, markets, agricultural tools, and machines are simple and do not meet the requirement of large production scale. The farmer‘s intellectual standard and the technological knowledge of labor force in the district are still limited. Most of them are manual workers, and follow traditional local habit and experience. The local people have low incomes and some have a living under the standard conditions, but they do not have specific strategies for further development. The agricultural production is still spontaneous and conducted in small household scale due to a lack of investment capital, techniques, irrigation system, good seeds, and stable markets for agricultural products. The annual agricultural crops in the study area are diverse. The main land use patterns are: rice - rice, rice – crops, rice – vegetables, short-term industrial crops and perennial fruits. Nevertheless, very few types of use or crops bring high and stable economic benefits because of limit information of land use policy and land potential productivity. Most of the agricultural practices are spontaneous and follow in traditional ways of farming. The agricultural commodities follow the unstable demands of the market every year. Regarding socio- economic and environmental effects, annual crops and vegetables are the most potential patterns in the coastal sandy land area in the future. Therefore, land suitability evaluation is needed as it is concerned as a smart method for selection which kinds of crops should plant for highest benefits and protect land resources from degradation. As a major agricultural district of Thanh Hoa Province, current land use status and land use structure for agricultural production in 2012 on the district were still insufficient. The available land for agricultural production was about 11,228.63ha, covering about 49.29% of the total natural area, in which only 47.56% of the total agricultural land was used for growing annual crops (Table 3.7). The rest was used for growing perennial plants. In addition, the cultivated land of each household was small, narrow, and scattering in plots, so it was highly difficult for concentration of commodity production. As a result, local farmers have to plant different crops on the same land unit in a year. This is a big challenge of land use in other coastal districts of the province. Thus, it is needed to have a wise and appropriate investment together with applying the scientific and technological advances not only for improving the land characteristics, but also for improving the quality of agricultural production.

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Question 2: What are the promising land use types in the existing land, climates, social - economic and infrastructural conditions in the study area? There are three major principle rules for a sustainable type of land use in Vietnam which are economic sustainability, social sustainability, and environment sustainability (Thu and Khang, 1998). Based on the investigation, eight main annual crops were selected to be promising in existing condition of Quang Xuong District. The chosen crops include paddy rice, sweet potato, groundnut, maize, potato, sesame, soybean, and green pepper. of which paddy rice is one of the most popular food types consumed by local people. Due to the tropical monsoon climate, the diversity of crops can be grown in different reasons. With relatively flat terrain and diversified soil types, the study area is suitable for development of annual crops. These annual crops supply not only sufficient food, but also medium economic return to farmers. In addition, low economic input and appropriate rotation crops might help growers improve their annual income. Most of the agricultural lands are irrigated from hydrological systems. This is a good condition for selecting and growing the crops as well as improving yields and quality of agricultural products. Moreover, the strategies for agricultural development of the district and the province together with food habit, traditional custom and social structure are seen as driving forces for local farmers in this case study. According to the current agricultural parameters such as cropping systems, main agricultural markets and facilities, labor forces, economic situation, and expert opinions in the field of agriculture, the mentioned annual crops satisfy socio-economic and environmental sustainability development.

Question 3: Which roles do land information systems play in land evaluation? Land information system (LIS) is determined as an important key in land suitability assessment process. It influences directly or indirectly land evaluation results. In Chapter 5, the soils database and LIS are created for conducting the land assessment. After investigating soils, the real conditions of the case study and considering the inheritable information sources, this current study has figured out major factors which are used for establishing land unit map, including drainage capacity, soil depth, relative topography, irrigation condition, soil texture, percentage of organic matter content, soil pH, cation exchange capacity, sum of exchangeable basic cation, soil unit group, and base saturation of soil as spatial data. Besides, many attribute information are collected for land evaluation process. GIS technique is applied to create different thematic maps and overlaid them together to generate land mapping unit with 42 land units in the study area at the scale of 1:25,000 and in UTM projection, Zone 48 North, Datum of WGS 84.

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Discussion of the Results

Application GIS allows users to access, store, edit, overlay and analyze to create a new map that meets the requirements of the study from LIS database. LIS is necessary and helpfulness at the district level because the district authorities implement most of the policies of the government and agricultural projects. In addition, LIS can help to establish new information, saving time searching for rudimentary pattern from the initiation. With many advantages and significant benefits, LIS is useful for making land use consultancy, land suitability evaluation for cultivated crops, and sustainable land use planning. Existing land information system and database of each land unit in GIS software can be updated and be used for further study of land suitability evaluation and land management. This also provides more useful information for handling of agricultural land with appropriate technology and crops. However, the most difficult challenge for building LIS database at the district level is that the patterns of spatial and attributes data is not consistent. The data are collected at different points of time and the particularized data are incoherent and diffused. The data has not been standardized, synchronized and connected with other sources of land information. Besides, each governmental department and public service office has different ways of storing, managing information and data. They may also apply different software systems for establishing and managing map figures.

Question 4: How is physical land suitability assessment used for agricultural development? In this study, physical land suitability assessment includes land characteristics and climate conditions. The perspective of ecological and sustainable development has been carried out for the land suitability evaluation of different land utilization types. Regarding land potential productivity, the high potential land is only 733.77ha (5.26%). Most of cultivated land is classified as the moderate potential with 11585.97ha (83.10%). 1402.74ha (10.06%) of the investigated area presents at low potential category, and the smallest area with 219.33ha (1.57%) belongs to very low potential level. Among all major limited factors for current land potential is chemical soil and physical soil properties such as the percentage of organic matter content, soil pH, soil texture and relative topography. In this case, changing farming methods and increased investment inputs can improve the soil condition. However, relative topography is strongly difficult to correct unless advanced technology and improving irrigation and drainage systems is applied. The results of land suitability for selected annual crops by applying three different methods bring different classes of suitability for each crop. For instance, the high suitability level (S1) does not exist in any land unit by applying the simple limitation, parametric, and AHP methods to evaluate land suitability for groundnut crop. The moderate suitability level

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(S2) is 9120.87ha (13.78%), 880.02ha (6.31%), and 1119.87ha (8.03%) for the simple limitation, parametric, and AHP methods respectively. The marginal suitability (S3) by using those method is 5280.93ha (37.88%), 4386.33ha (31.46%), and 7239.96ha (51.93%) respectively. The total unsuitability level (N1 + N2) for growing groundnut crop is 6740.01ha (48.35%), 8675.46ha (62.23%), and 5581.98ha (40.04%). Similarly, the different results of land suitability also occurred with other annual crops. The consequences of land suitability for selected annual crops lead to a conclusion that all the land units are maintaining certain degree of limitation which may affect the land quality. The dominant limitations are very difficult to transform or correct such as relative topography, soil texture, soil depth, and irrigation condition. Some limitations can influence land suitability rating but it can be improved. Those limitations are known as ordinary limitations such as the percentage of organic matter content (OM), soil pH, cation exchange capacity (CEC). Based on the results of land potential productivity and land suitability level for each land utilization type, land users and land planners cannot only make right decisions on what kind of land or where area and kinds of crops should be maintained for agricultural development, but also recommend appropriate techniques, investment inputs for eliminating the limitations in order to improve land quality and achieve effective results from cultivation practices.

Question 5: What are the criteria for land evaluation and how does it classify in different suitability ratings? Three different methods are applied to assess land suitability for selected crops in the study area, including the simple limitation, parametric (square root), and AHP methods. For the simple limitation and parametric methods, the factors of relative topography, soil chemical property (including OM, ECE, pH, EC, and BS), and soil physical property (soil texture, soil depth, irrigated condition, and drainage capacity) are considered for land suitability evaluation. In the parametric method, the rate for each factor from 0 to 100 is determined after being evaluated in comparison with the crop requirements (MARD, 2005; Sys et al., 1993) in the reference table (Table 5.20). The equation (2) in Chapter 4 is presented for calculating the final score of each land unit and it is also given a rate from 0 to 100. In the simple limitation method, the biggest limitation factor is defined by the final score for a land unit. In AHP method combining with multi-criteria evaluation approach for selected annual crops, three main parameters, four sub-criteria and nine sub-unit criteria are considered for land suitability assessment process. Three main parameters are soil property, relative topography and infrastructure system. Among three main parameters, soil property is the most important one with the highest weight value (0.610) and much higher than the weight values of two other parameters. The second highest weight value is

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Discussion of the Results relative topography (0.237) and the lowest weight value is infrastructure system (0.123). These results indicate that soil property is more influenced on the suitability level of certain crop than relative topography and infrastructure system. The soil property has two sub-criteria, namely chemical property and physical property. After a comparison of both of them, the weight values of chemical and physical property are 0.581 and 0.419 respectively. This means the chemical property is more important than physical property in land suitability assessment process. The parameter of infrastructure system also has two sub-criteria, including rural transportation system and irrigation-drainage system. The results of weight values are 0.332 for rural transportation system and 0.668 for irrigation-drainage system. This implies that the second sub-criterion is more affected on land suitability level than the first one. The sub-criterion of chemical property has five sub-unit criteria, including OM, CEC, pH, EC, and BS. Among them, OM is the most important criterion with the highest weight value (0.394). The second most decisive one is pH criterion with the weight value of 0.274. CEC is also interested and has a big impact on crop production with the weight value of 0.170. The least important criteria are determined as BS and EC with the weight values of 0.069 and 0.092, respectively. The sub-unit criterion of physical property has four sub-unit criteria, including soil texture, soil depth, irrigation condition, and drainage capacity. After calculation of comparison matrix, soil texture is determined as the most important factor with the highest weight value (0.369), the second most significant one is irrigation condition with the weight value of 0.339. The least important sub-unit criteria belong to drainage capacity and soil depth with the lowest values of 0.156 and 0.135, respectively. The CR index at all levels is less than 0.10, so the weight values are reliable and acceptable. One example of application of abovementioned parameters, sub-criteria and sub- unit criteria is land suitability evaluation for maize crop. The results show that there is no high level of suitability (S1) for maize crop in the case study by applying AHP method. Nearly a half of the agricultural land is classified as marginal suitability with 6702.39ha, accounting for 48.07%. The moderately suitable level (S2) is about 2186.19ha, covering 15.68% of the investigated area and non-suitability level (N) has 5053.23ha, in which

4002.48ha, equivalent to 28.71% belongs to the current unsuitability level (N1);

1050.75ha, accounting for 7.54% falls under the permanent unsuitability (N2) for maize production. All the results of Multi-criteria land suitability evaluation for all selected crops have been presented in Chapter 5.

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Question 6: Who are the main beneficiaries in the present study? The development of annual crops attracts the attention of many stakeholders, especially the local people because it not only brings about economic benefits to improve their livelihoods, but also helps to protect the land resource and preserve the local specialties. This current study provides and analyzes key data of agricultural land resources, so the local famers, rural planers, environmentalists, and governmental officers are the first crux to make use of the result output. The findings of land-cover change, land potential productivity, and the land suitability are useful for land-users to make the strategies for sustainable land use in long-term as well as to develop feasibly agricultural projects associating with local people‘s benefits. They could be the issues of interest for further extension to the rural planners and agricultural officers. The identification of the land characteristics and quality of the each land parcel is taken into account as a big concern of planters. The identified limitations, which affect land suitability level, are very important for local growers to decide what area and crop should be used for cultivated production. This information also plays an essential role in improving the land suitability and obtaining high economic efficiency of significant investment input. In other words, suitability assessment results will benefit the purpose of agricultural activities directly. Therefore, current research involves dimension from all the sectors of society and environment and its results can contribute to greater extent land management and land use planning. However, during the suitability evaluation process, the research is dealt with several current difficulties and shortcomings of this region. The most difficult problems are the scattering of the cultivated plots and using the water supply during the flowering and fruiting of development process. In addition, local practice, traditional farming methods, unskilled labor force, low capital source, and low understanding of technology and science in effective annual crop plantation are prevalent obstacles to agricultural production. The other problems like unprofessional function of the agriculture service center and post- harvest processing are also affected by the productivity and quality of agricultural products. Although these types of the problems are not spatial attributes, but they are needed to be addressed timely. Therefore, in order to expand land of annual agricultural crop, land-users and land planners should first be based on the advantages and disadvantages of each land unit as well as the different suitability levels in each land unit for particular kind of selected crops. As a result, appropriate decisions and strategies should be made to improve the efficiency of land use for all stakeholders from the local farmers to authorities makers. This will satisfy the three aspects of economic efficiency, social acceptability, and environmental sustainability.

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Discussion of the Results

6.5. Research limitations and consequences Different applied methods in this study brought different results of land suitability for a specific annual crop. Therefore, the choice of which method should be used is a difficult task, because it will affect the accuracy and objectivity of suitability evaluation consequences for reclamation of agricultural land and influences the decision making related to agricultural production. The most popular and most currently used method to evaluate land suitability in Vietnam is the simple limitation method. This method has been widely used, since it is simple, easily applied, scalable, and it does not require high expertise. It is simple and easy to use and it only takes one lowest characteristic into account to define a suitability level. Thus, the results of land suitability for a specific crop may be low precision. Regarding the parametric method, an ultimate land index to determine the suitability level for a cultivated crop is calculated from multiplication of chemical and physical properties. The result of different suitability levels in comparison between land units is because of multiplication of the land suitability ratings by each other used in calculating of the land index. Moreover, the lowest rating suitability of a certain characteristic will control the final land suitability index. Recently, some Vietnamese soil scientists have applied the AHP approach in combination with GIS for land evaluation such as Tri and Tri, (2004), Chuong (2008a), Loi (2008), Giang (2012). The finding from these studies indicated that AHP approach can be used to determine the weights associated with suitability map layers, and classify a whole area in detail about the suitability levels for particular land use. However, the problem of mutuality between criteria and alternatives, inconsistencies in judgment are the most limitations of the model. Additionally, the results of land evaluation in this study show that the weighted summation in this method has a limitation of the compensatory problem. This implies that a land unit with a low score on one characteristic may have a high score on other characteristics. For instance, land unit with depression of relative topography will have the low score for maize crop growth while it scores highly on the organic matter criterion. Consequently, the resulting score after the weighted summation may be exaggerated. In fact, very little study on land evaluation has been applied the parametric method, and there is no research of comparison between the simple limitation, parametric (square root), and AHP methods for land suitability assessment in Vietnam before this study. Therefore, in order to determine which method is more reliable, the observed yield is recommended to estimate for a certain kind of crops associated with each land unit. After analysis of the correlation between the three methods of land evaluation and observed yield

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Discussion of the Results of a certain crop, which method has closer correlation with the observed yields, it will be a better model for the classification of the land suitability (Sharififar, 2012). Regarding data collected from this study, selecting the right data is one of the most difficult stages in land evaluation process. It is not easy to know in advance which kinds of data are the best essential with the study area after consultation the local experts and the previous literature according to FAO guidelines for land evaluation (FAO, 1976, 1985, and 1993). In Chapter 4, both types of spatial and attribute data were collected based on the natural conditions of the study area and the available data which could be used for further analysis. Those data were collected from different sources. However, none of those official sources has a professional LIS database and the land use managers may apply different software systems for storing and managing the spatial data. Especially, at the district level, most of the historical spatial data is absent and inadequate. For example, if they had the status map of land use in 1989 and 2013, then the determination of land cover/land use change could be much easier by comparing the change between two periods of time. Fortunately, analysis of land use change has been handled by using satellite images of Landsat5 for 1989 and Landsat8 for 2013. Moreover, the spatial data is not standardized and synchronized with other sources. This problem reduced the accurate delineation of boundary. For example, the topography map of the district had been out of date and used Hanoi-72 as the main coordinates system, but the VN-2000 coordinates system was used for the current map of land use in 2012, and some types of maps were registered by UTM projection, Zone 48 North, Datum of WGS 84 such as the administrative and hydrographic maps. Besides, most of the land characteristics were collected and analyzed from conventional soil survey. They were still managed by using traditional backward methods. Therefore, the attribute data is often inconsistent with spatial data and building map. Some important soil characteristics such as calcium carbonate (CaCo3), salinity, alkalinity, and stones at the surface were not referred in both spatial and attribute data. Thus, it was a big limitation for selecting the parameters for land evaluation. In addition, this study concentrated on the physical land suitability assessment, several socio-economic factors such as proximity to market, agricultural input availability, cultivation pattern, labour force, and investment capacity were not included in the design as the parameters for land suitability evaluation model. Those limitations not only affect the accuracy of research results but also bring many difficulties for the development of a land information system due to standardizing data and time consumption.

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Conclusion and Recommendation

Chapter 7. Conclusion and Recommendation

7.1 Conclusions Quang Xuong District is located in a poor coastal sandy area of Thanh Hoa Province, where most of the local people depend on agricultural products for daily life. However, agricultural land area is constantly decreasing over years because of the expansion of non- agricultural activities such as the expansion of inhabitant area, the development of rural infrastructures or using agricultural land for industrialization so as to ensure food supplies for the locals as well as matching the strategy of agricultural development of the district and the province. It is strongly necessary to understand land capacity to offer appropriate cultivation activities. The land suitability evaluation is one of the most important steps in land use planning, especially in the development of sustainable agriculture. It also plays an essential role in identifying land limitations and supporting further land management measures. Chapter 4 described the methodologies used for analyses, and determination of the land cover change, land potential productivity, and land suitability evaluation and the results in Chapter 5 answer the given hypotheses in Chapter 1 as follow: Remote sensing not only has the capability of classifying, extracting information from satellite images, but it also has the ability of monitoring land cover change in different time. For this study, Landsat images were taken from USGS earth explorer website. The images were related to 1989 TM, and 2013 OLI & TIRS respectively. The land cover categories are developed by supervised classification method (Maximum likelihood). Six classes have been identified as built-up area, agricultural land, forest land, water surface area, salty land, and unused land for the last 24 years from 1989 to 2013. During that time, built- up area and forest land increased significantly, from 52.69ha to 448.45ha for forest and from 5173.66ha to 9185.44ha for built-up area; while, cultivated land decreased from 14362.12ha to 11200.30ha and unused land decreased from 1010.25ha to 91.0ha. Change detection shows the environmental and socio-economic impacts and environmental change can be a result of land use change. Understanding the trends of land use changes will help to develop an appropriate model evolution of land use patterns and land use planning in the future at the district level. Land management and land use planning in Vietnam are still facing many obstacles, because the data of environment, socio-economy and land characteristics are not standardized and collected by various professional offices from the provincial level to the community level. These data are usually analyzed by using outdated methods and stored in different forms. Thus, the achieved findings are not consistent and reliable. On the

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Conclusion and Recommendation contrary, the management of land resources, especially land evaluation for agricultural development at a district level requires adequate, consistent and continuous data to analyze the current status, to anticipate changing trends of land use and land resources deterioration. Therefore, GIS techniques can provide powerful tools for storing, analyzing and updating different data to evaluate land potential and land suitability for agricultural development within the study area. Each land unit is distinguished from each other unit by one or more different characteristics. In this study, the suitability levels for agricultural use are mostly depended on soil qualities. In order to assess land potential productivity, the Linear Combination Method was applied through GIS modeling to establish the potential productivity map for agriculture. The weigh values and scores of soil properties were obtained by collecting experts‘ opinions and analyzing local conditions. The consequences show that there were about 5.26%, 83.10%, 10.06%, and 1.57% of the cultivated land figured out as high potential, medium potential, low potential, and very low potential productivity respectively. These findings are useful for choosing the potential productivity site for annual agricultural crop production and selecting the most appropriate boundaries for each kind of crops in the future. The purposes of agricultural production are to develop the economy of the district, to increase income, and to meet the demand for food of the local people. Land quality is one most important factor which directly affects crop growth and crop yields. Each land unit is assigned for a specific level of suitability based on its qualities and each crop has its own requirements for development. The final level of suitability (high, moderate, marginal suitability level or unsuitability level) for a certain crop is classified depending on how the land qualities meet the crop requirements. Therefore, developing database of land resources, soil properties, climate conditions is necessary for land-users to choose suitable land area for cultivated activities according to the classification of land suitability levels. It also provides information to improve the current restrictive conditions and enhance production effects for sustainable development. The results of land suitability evaluation for the selected annual crops in this study illustrate that three different methods of the simple limitation, parametric, and AHP produce different results of suitability level for each crop. It is significant to provide more alternatives for land-users to consider which land evaluation method should to be applied and which method is best suited with their purposes. For instance, if the land-users intend to use qualitative and simple models for land evaluation, then the simple limitation one should to be used. On the contrary, if they stand for more complex method, then the parametric or AHP approaches should be implemented. According to Manna et al. (2009). It is difficult to figure out the most feasible

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Conclusion and Recommendation method for land evaluation as there have been a limited number of studies on this topic. He also suggests that more complicated methods have better predictive ability than more simplified approaches. In this study, the AHP method is considered as the most complex method, and found to be as a useful approach to define the weights. It can endow with visualization of the results of the normalization procedure. In comparing with the simple limitation and parametric methods, the AHP is superior and more effective because it can handle inconsistent experts‘ opinions for determining the weights. Therefore, combination of AHP method and GIS demonstrates a powerful combination to implement for the assessment of agricultural land potential and the evaluation of land suitability for a specific crop. The difference consequences of land suitability can be explained by the different ways in calculating the final suitability level for each land unit associated with a specific crop. In the simple limitation method, the final level of suitability is classified as the lowest level among suitable classes. In the parametric method, the final level of suitability is depended on the results of land suitability index. It is the square root of multiplication of different land suitability ratings with the lowest rating suitability (Rmin) of land characteristics. On the contrary, in AHP method, the final suitability level is depended on the score of land properties and the weight value of each parameter in which experts should be consulted to understand the weight value of each parameter. In short, the suitability level is based on a variety of factors. Table 7.1 presents the advantages and disadvantages of simple limitation, parametric, and AHP method. The results of land suitability evaluation are an important value and proficiency of the land in the land use planning for future sustainability of land resources in general and agricultural land resources in particular.

7.2 Recommendations 7.2.1 Recommendations for further researches In order to recommend the best results of land suitability evaluation for land-users, land planners, and local people, it is necessary to determine crop yields in different land units for finding out the correlation between the crop yields and the suitability results of land evaluation by using three different methods. The closer correlation between crop yields and the results of land suitability, the better results should be recommended. It is also important to make a scientific plan for agricultural production because agriculture is one of the most important components to promote the economic development of the district. It is urgently necessary to create the land information system (LIS), including soil types, land characteristics, soil properties, topography, current land use status, climate conditions, vegetation cover, and land unit map for district level in Vietnam. It is because this administrative level applies most of the policies, strategies of agricultural development

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Conclusion and Recommendation from the government and semi-detailed land use planning, but shortage of information of land resources. GIS techniques are highly recommended to use for creating a database system like LIS because GIS allow land users to access, edit, update, overlay, and analyze both spatial and attribute data to create a new map which meets the requirement of the study problem. Other advanced technologies such as remote sensing and Global Positioning System (GPS) are also encouraged to apply to land management because they will help on bringing real time change in land use and management strategy. Using different methods of simple limitation, parametric, and AHP in land suitability assessment can provide land-users with more alternatives of the appropriate results to decide which crop, and where is the best suitable area for agricultural production. It is very significant to helping the crop development planning in the study area more scientific and effective. Those methods can be expanded and repeated in different areas of the province as well as applied to determine land evaluation for other agricultural crops. The main factors used for land suitability evaluation can be changed to be suitable for a particular area. It is necessary to have a scientific instruction of the field survey to collect the locals and experts‘ opinions to choose adequate and accurate evaluation criteria for the land evaluation process. Thus, further studies should be conducted to complete all official data with close links among different information sources of natural environment and socio- economic conditions.

7.2.2 Recommendations for soil improvement and crop development Agricultural land in the study area is constantly decreasing over years because of the expansion of inhabitant area, industrialization, and rural infrastructure development. Thus, the district and the province need to establish new policies to reduce the transfer from cultivated areas to non-agricultural areas. This is strongly necessary to regard food security for local people and the future generations as well as to ensure the development of sustainable agriculture. Soil fertility of the agricultural land is being seriously degraded, especially the percent of organic matter content and the process of soil acidification. Thus, land users should have a reasonable investment of technology, land reclamation and rational fertilization regimes to provide sufficient nutrients for the soil and improve the soil texture for better absorbability. These conditions have a vital part in making the crops develop well and bring desired agricultural productivity.

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Conclusion and Recommendation

Table 7.1: The advantage and disadvantage of three applied methods

Method Advantage Disadvantage - Simple, easy to understand - Not allow for interactions between - Can be applied at different details land characteristics depending on objectives and the - Low precision because the most Simple scale of the evaluation limiting characteristics defines land limitation - Well suited to the place where data suitability class of conventional soil survey already - The results may be incorrect and exists unreliable if the data is out of date - Simple and easy to apply. - Misleading sense of accuracy, arbitrary - Allowing any important factor to choice of factors Parametric control the rating. - Assuming synergistic interactions in (Root Square) - Decreasing irrational influence on factors, therefore, more factors lead to total land index and more balanced lower average ratings mathematically - More suitable for small spatial scale - Difficulty for finding the best areas alternative - Having the ability to incorporate - Interdependence between criteria and different types of data alternatives AHP - Comparing two parameters at the - Time consuming same time - The results are depended on experts‘ - Can deal with inconsistent experts‘ experience and knowledge opinions

In order to improve the efficiency of agricultural land use, and ensure stable growth and sustainable development for agricultural crops, it is necessary to continuously invest to repair sea dikes, river dikes, and plan protection forest to protect cultivated lands from the salinization and the invasion of sand. The irrigation-drainage system is also needed to upgrade the supply of initiative water for crops during the dry season and drainage timely during the rainy season. The agricultural land managers and the extension officers should give the proper and seasonal calendar crops for growing to avoid the negative effects of cold Northeastern monsoon and the hot, dry Southwestern during the period of flowing and fruiting of crops. Besides, the socio-economic limitations such as rural transportation system, storage and post-harvest processing, labor force, markets, land use policies, and crop seeds should be cared for expanding scale and area for commodity annual crop production.

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Acknowledgements

Acknowledgements

During PhD research periods, I had received a lot of invaluable and honest assistances, advice, comments, and encouragements from many different people. I would like to express my equally thank to all those who contribute to this study. First of all, I would like to express a deep sense of gratitude to my principal supervisor, Prof. Dr. rer. nat. Reinhard Zolitz for providing me a great opportunity to pursue my research as a Ph.D candidate at the Institute of Geography and Geology, Greifswald University and for his valuable suggestion, precious comments and useful ideas from the beginning to the end of this doctoral research, without his supports the thesis would not have been completed. I would like to express my sincere gratitude to Ass. Prof. Dr. Dao Chau Thu, my second advisor, for her supports and guides during the time of the data collection in Vietnam and for her encouragement in my research. I strongly appreciate all her contributions to make my current study productive and stimulating. I gratefully acknowledge Mr. J. Hartleib, M.Sc. N. Q. Huong, and Dr. Tobias Matusch for their personal supports and sharing some of their enormous GIS experience and computer skills with me. My special thanks also go to Dr. Bernd Bobertz for his kind encouragement and delicious coffee during different phases of the study. My appreciation is extended to M.Sc. N. D Trung, M.Sc P. T. Noi from the Faculty of Land Management, Hanoi University of Agriculture; M.Sc. H. T. Anh from the Division of Resources and Environment, M.Sc N. D. Bach from the Agricultural Extension Center, Quang Xuong District; M.Sc. D. N. Duc from the Informatics Technology Central, Department of Resources and Environment, Thanh Hoa Province, and all local farmers and experts for their collaboration, willingness to share information, and assistance of data collection. My sincerely thanks also go to Dr. B. P. Hung for his useful comments on this dissertation and for editing and improving my English. I would like to express my wholeheartedly acknowledge to the People‘s Committee of Thanh Hoa Province for giving full financial support for my stay in Germany as well as in Vietnam during my fieldwork. I am also indebted to the International Office of Greifswald University for funding three months to complete this research. Finally, I expressed my deepest gratitude to my parents, my wife, and my children for giving all the moral support love, understanding, and sacrifice without their supportive contributions my study could be impossible.

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Appendixes

Appendix 3.1: Climate data in the study area recorded from 1993 to 2012

T- Relative Tmin Tmax T_mean Evaporation Precipitation Month Everage Humility (0C) (0C) (0C) (mm) (mm) (0C) (mm) Jan 14 18.6 16.3 17.165 64.54 18.38 84.4 Feb 13.5 21.3 17.4 18.245 45.365 15.45 88.2 Mar 16.8 21.8 19.3 20.015 46.51 44.15 89.4 Apr 22.4 25.4 23.9 23.87 51.2 66.52 85.7 May 25.7 28.4 27.05 27.115 89.755 170.4 85.5 Jun 28.1 30.6 29.35 29.38 126.115 166.32 80 Jul 28.4 30.3 29.35 29.18 113.67 196.15 81.9 Aug 27.4 28.6 28 28.155 79.515 295 86.8 Sep 25.3 27.9 26.6 26.905 80.15 349.77 86.2 Oct 23.8 26.1 24.95 25.115 98.335 224.18 83.1 Nov 21 24.6 22.8 22.21 102.3 80.85 78.6 Dec 16.2 20.6 18.4 18.79 89.54 31.81 60.5 Aver.Sum 21.88 25.35 23.615 23.84 82.25 138.25 82.525

Source: Climate station of Thanh Hoa City

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Appendix 3.2: Soil classification information in Quang Xuong district

Area Vietnamese name FAO-UNESCO name Symbol (%) (ha) 1. Đất cát 1. Arenosols AR 3127.86 22.44

1.1. Đất cồn cát trắng vàng 1.1. Luvic Arenosols ARL 560.52 4.02

1.1.1. Đất cồn cát trắng, vàng trung tính 1.1.1 Eutri luvic Arenosols ARLe 560.52 4.02 ít chua

1.2. Đất cát trung tính ít chua 1.2. Eutric Arenosols ARe 1759.14 12.62

1.2.1. Đất cát trung tính ít chua điển hình 1.2.1. Hapli eutric Arenosols AReh 1759.14 12.62

1.3. Đất cát có tầng đốm gỉ 1.3. Cambic Arenosols ARb 808.20 5.80

1.3.1. Đất cát trắng có tầng đốm gỉ trung 1.3.1. Endogleyi eutri cambic ARbeg2 750.06 5.38 tính ít chua glây sâu Arenosols

1.3.2. Đất cát trắng có tầng đốm gỉ chua 1.3.2. Dystri cambic ARbd 58.14 0.42 Arenosols

2. Đất mặn 2. Salic Fluvisols FLs 265.23 1.90

2.1. Đất mặn ít 2.1. Hyposalic Fluvisols FLs 234.81 1.68

2.1.1. Đất mặn ít glây nông 2.1.1. Epigleyi hyposalic FLswg1 234.81 1.68 Fluvisols

2.2 Đất mặn nhiều 2.2. Hypersalic Fluvisols FLs 30.42 0.22

2.2.1. Đất mặn nhiều glây nông 2.2.1. Epigleyi hypersalic FLshrg1 30.42 0.22 Fluvisols 3. Đất phù sa 3. Fluvisols FL 9358.29 67.11

3.1. Đất phù sa trung tính ít chua 3.1. Eutric Fluvisols FLe 520.56 3.73

3.1.1. Đất phù sa trung tính ít chua điển 3.1.1. Hapli eutric Fluvisols FLeh 520.56 3.73 hình

3.2. Đất phù sa có tầng đốm gỉ 3.2. Cambic Fluvisols FLb 3176.10 22.78

3.2.1. Đất phù sa có tầng đốm gỉ trung 3.2.1. Hapli eutri cambic FLbeh 565.47 4.06 tính ít chua điển hình Fluvisols

2.2.2. Đất phù sa có tầng đốm gỉ chua 3.2.2. Hapli dystri cambic FLbdh 608.31 4.36 điển hình Fluvisols 3.2.3. Đất phù sa có tầng đốm gỉ chua 3.2.3. Dystri cambic Fluvisols FLbd 864.18 6.20

3.2.4. Đất phù sa đốm gỉ glây nông chua 3.2.4. Dystri epigleyi cambic FLbgd 1138.14 8.16 Fluvisols

3.3. Đất phù sa chua 3.3. Dystric Fluvisols FLd 1274.76 9.14

3.3.1. Đất phù sa chua điển hình 3.3.1. Hapli dystric Fluvisols FLdh 1274.76 9.14

3.4. Đất phù sa glêy 3.4. Gleyic Fluvisols FLg 4386.87 31.46 3.4.1. Đất phù sa glây chua 3.4.1. Dystri Gleyic Fluvisols Flgd 1294.38 9.28 3.4.2. Đất phù sa trung tính ít chua 3.4.2. Eutri Gleyic Fluvisols FLge 717.03 5.14

3.4.3. Đất phù sa glây trung tính ít chua 3.4.3. Hapli eutri epigleyic FLge h 2375.46 17.04 điển hình Fluvisols 4. Đất glây 4. Gleysols GL 453.6 3.25 4.1. Đất glây chua 4.1. Dystric Gleysols GLd 453.6 3.25

4.1.1. Đất glây chua điển hình 4.1.1. Hapli dystric Gleysols GLdh 453.6 3.25

206

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5. Đất xám 5. Acrisols AC 517.5 3.17 5.1. Đất xám điển hình 5.1. Haplic acrisols ACh 517.5 3.17 5.1.1. Đất xám điển hình có tầng bạc 5.1.1. Albi haplic acrisols ACh al 517.5 3.17 trắng 6. Đất xói mòn mạnh trơ sỏi đá 6. Leptosols LP 219.33 1.57

6.1. Đất xói mòn mạnh trơ sỏi đá chua 6.1.Dystric leptosols LPd 219.33 1.57

6.1.1. Đất xói mòn mạnh trơ sỏi đá chua 6.1.1. Hapli dystric leptosols LPdh 219.33 1.57 điển hình Sum 13,941.81 100.00

Source: Department of Natural Resources and Environment Management of Thanh Hoa province

207

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Appendix 3.3: The average investment cost per hectare in 2012

Crops Seeds N P K Lime Pesticides Other costs Labour Tax Total cost

Rice 1,405.82 1,308.55 1,318.75 1,079.11 460.00 915.00 1,525.70 5,500.00 600.00 14,112.93 Maize 1,143.07 1,234.48 1,238.25 1,007.57 365.00 870.00 1,439.09 5,000.00 600.00 12,897.45 Sweet potato 811.58 728.42 984.23 769.25 250.00 583.14 1,067.46 4,750.00 600.00 10,544.08 Potato 1,046.67 1,012.96 970.42 1,031.39 270.00 600.00 1,113.00 5,000.00 600.00 11,644.44 Groundnut 2,570.45 657.00 1,163.28 1,212.36 755.00 767.46 1,350.43 5,500.00 750.00 14,725.98 Sesame 1,223.17 579.09 1,155.33 973.52 365.28 345.46 729.07 4,500.00 600.00 10,470.90 Soybean 1,133.78 736.56 1,231.24 1,216.04 357.35 907.96 1,224.28 5,000.00 600 12,407.21 Green pepper 6,405.20 1,432.23 1,477.12 1,111.76 440.00 400.13 2,418.10 8,000.00 800.00 22,484.53 Sedge 3,100.18 1,350.57 966.93 999.71 180.00 867.54 1,668.70 6,500.00 800.00 16,433.63 Jute 3,223.45 1,360.86 1,067.44 1,057.83 230.00 1,056.70 1,749.93 6,500.00 800.00 17,046.21 Tobacco 7,460.75 2,093.84 2,559.27 1,503.65 470.00 1,284.41 2,689.33 8,000.00 800.00 26,861.25 Vegetables 3,060.83 1,450.68 965.41 1,095.92 265.00 1,238.40 1,456.29 6,500.00 800.00 16,832.53 Other crops 4,126.30 1,250.39 1,324.45 1,127.81 280.00 450.55 2,447.16 8,000.00 800.00 19,806.66 N = Nitrogen; P = phosphorus; K = potassium 1 USD = 20.940 VND (26/6/2012) Source: Famer interviews and yearly report from the Division of Agricultural Economics of Quang Xuong

209

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Appendix 3.4: Sweet potato, sesame, groundnut, soybean, potato, and green pepper cultivation in the study area.

211

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Appendix 5.1: Pairwise matrix comparison and calculating of weight criteria at level 2 and level 3

(a): Soil property comparison

Chemical Physical Criteria Weight property property Chemical property 1 1.384 0.581 Physical property 0.723 1 0.419

CR = 0.000; = ; CI = 0.000; RI = 0.000

(b): Rural infrastructure system comparison

Rural road Irrigation and Criteria Weight systems drainage Rural road system 1 0.497 0.332 Irrigation-drainage system 2.013 1 0.668

CR = 0.000; = ; CI = 0.000; RI = 0.000

(c): Chemical property comparison

Sub-chemical OM CEC pH EC BS Weight property OM 1 2.769 1.251 4.441 5.144 0.394 CEC 0.361 1 0.692 1.861 2.611 0.170 pH 0.799 1.445 1 3.105 3.663 0.274 EC 0.225 0.537 0.322 1 1.488 0.092 BS 0.194 0.383 0.273 0.672 1 0.069

CR = 0.002; = ; CI = 0.002; RI = 1.12

(d): Physical property comparison

Soil Irrigation Soil Drainage Sub-physical property Weight texture condition depth capability Soil texture 1 1 3.11 2.276 0.369 Irrigation condition 1 1 2.632 1.914 0.339 Soil depth 0.322 0.380 1 1.03 0.135 Drainage capacity 0.439 0.522 0.971 1 0.156

CR = 0.006; = CI = 0.007; RI = 0.9

213

Appendixes

Appendix 5.2: The score each criterion is based on the crops requirements

Rice Variable Category Score Variable Category Score Silty clay, Clay loam, > 50% 4 4 Silty clay loam 35% - 50% 3 Silty loam, Loam 3 Bas saturation Soil texture Loamy sand, 2 < 35% 2 Sandy loam Coarse sand 1 > 1.5 4 Actively irrigated 4 0.8 - 1.5 3 somewhat irrigated 3 OM (%) Irrigation Poorly irrigated 2 < 0.8 2 None_irrigated 1 > 10 4 > 75 4 CEC (meq/100g soil) 5 - 10 3 50 - 75 3 Soil depth (cm) < 5 2 20 -50 2 5.5 - 7.0 4 < 20 1 5.0 - 5.5 3 Good 3 Drainage pH(H2O) 4.5 - 5.0 2 Moderate 4 Flat 4 < 4.5 1 Low flat 4 > 4.0 4 Upper flat 3 Relative topography 2.8 - 4.0 3 High 1 Exchange Cation 1.6 - 2.8 2 Depressed 2 < 1.6 1 < 500m 4 < 250m 4 500m - 1000m 3 Irrigation and 250m - 500m 3 Rural road systems 1000m - 1500m 2 drainage 500m - 1000m 2 > 1500m 1 > 1000m 1 Sweet potato Silty clay, Clay loam, Loam, > 35% 4 Silty clay loam, 4 Base saturation Soil texture Sandy loam, Silty loam 20% - 35% 3 Loamy sand 3 < 20% 2 Coarse sand 1 > 2.0 4 Actively irrigated 4 1.0 - 2.0 3 somewhat irrigated 3 OM (%) Irrigation Poorly irrigated 2 < 1.0 2 None_irrigated 1 > 10 4 > 75 4 CEC (meq/100g soil) 5 - 10 3 50 - 75 3 Soil depth (cm) < 5 2 20 -50 2 5.2 - 7.2 4 < 20 1 4.8 - 5.2 3 Good 4 Drainage pH(H2O) 4.5 - 4.8 2 Moderate 4 Flat 4 < 4.5 1 Low flat 2

> 3.5 4 Upper flat 4 Exchange Cation Relative opography 2.0 - 3.5 3 High 3

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< 2.0 2 Depressed 1 < 500m 4 < 250m 4 500m - 1000m 3 Irrigation and 250m - 500m 3 Rural road systems 1000m - 1500m 2 drainage 500m - 1000m 2 > 1500m 1 > 1000m 1 Groundnut Silty clay, Clay loam, Loam, > 35% 4 Silty clay loam, 4 Base saturation Soil texture Sandy loam, Silty loam Loamy sand 3 < 35% 3 Coarse sand 1 > 1.2 4 Actively irrigated 4 0.8 - 1.2 3 somewhat irrigated 3 OM (%) Irrigation Poorly irrigated 2 < 0.8 2 None_irrigated 1 > 15 4 > 75 4 CEC (meq/100g soil) 10 - 15 3 < 10 2 Soil depth (cm) 50 - 75 3 6.0 - 6.8 4 20 -50 2 5.6 - 6.0 3 < 20 1 pH(H2O) 5.4 - 5.6 2 Good 4 Drainage Moderate 3 < 5.4 1 Flat 4 > 2.8 4 Low flat 2 1.6 - 2.8 3 Relative topography Upper flat 4 Exchange Cation High 3 < 1.6 2 Depressed 1 < 500m 4 < 250m 4 500m - 1000m 3 Irrigation and 250m - 500m 3 Rural road systems 1000m - 1500m 2 drainage 500m - 1000m 2 > 1500m 1 > 1000m 1 Maize Silty clay, Clay loam, Loam, > 50% 4 4 Silty clay loam, Base saturation Soil texture Silty loam Loamy sand, 35% - 50% 3 3 Sandy loam, < 35% 2 Coarse sand 1 > 1.2 4 Actively irrigated 4 0.8 - 1.2 3 somewhat irrigated 3 OM (%) Irrigation Poorly irrigated 2 < 0.8 2 None_irrigated 1 > 10 4 > 75 4 CEC (meq/100g soil) 5 - 10 3 < 5 2 Soil depth (cm) 50 - 75 3 5.8 - 7.0 4 20 -50 2 5.5 - 5.8 3 < 20 1 pH(H2O) 5.2 - 5.5 2 Good 4 Drainage < 5.2 1 Moderate 4

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Flat 4 > 5.0 4 Low flat 2 3.5 - 5.0 3 Relative topography Upper flat 4 Exchange Cation 2.0 - 3.5 2 High 3 < 2.0 1 Depressed 1 < 500m 4 < 250m 4 500m - 1000m 3 Irrigation and 250m - 500m 3 Rural road systems 1000m - 1500m 2 drainage 500m - 1000m 2 > 1500m 1 > 1000m 1 Potato Clay loam, Loam, Silty clay loam, > 35% 4 4 Sandy loam, Base saturation Soil texture Silty loam Loamy sand 3 < 35% 3 Silty clay 2 Coarse sand 1 > 1.2 4 Actively irrigated 4 0.8 - 1.2 3 somewhat irrigated 3 OM (%) Irrigation Poorly irrigated 2 < 0.8 2 None_irrigated 1 > 15 4 > 60 4 CEC (meq/100g soil) 10 - 15 3 Soil depth 40 - 60 3 < 10 2 (cm) 20 - 40 2 5.6 - 7.0 4 < 20 1 5.2 - 5.6 3 Good 4 Drainage pH(H2O) 4.8 - 5.2 2 Moderate 4 Flat 4 < 4.8 1 Low flat 2 >3.5 4 Relative topography Upper flat 4 Exchange Cation 2.0 - 3.5 3 High 3 < 2.0 2 Depressed 1 < 500m 4 < 250m 4 500m - 1000m 3 Irrigation and 250m - 500m 3 Rural road systems 1000m - 1500m 2 drainage 500m - 1000m 2 > 1500m 1 > 1000m 1 Sesame Silty clay, Clay loam, Loam, > 50% 4 Silty clay loam, 4 Base saturation Soil texture Sandy loam, Silty loam 35% - 50% 3 Loamy sand, 3 < 35% 2 Coarse sand 1 > 1.2 4 Actively irrigated 4 0.8 - 1.2 3 somewhat irrigated 3 OM (%) Irrigation Poorly irrigated 2 < 0.8 2 None_irrigated 1 > 10 4 > 75 4 CEC (meq/100g soil) 5 - 10 3 50 - 75 3 Soil depth (cm) < 5 2 30 -50 2 pH(H2O) 5.8 - 7.0 4 < 30 1

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5.5 - 5.8 3 Good 4 Drainage 5.2 - 5.5 2 Moderate 4 Flat 4 < 5.2 1 Low flat 2 > 4.0 4 Upper flat 4 Relative topography 2.8 - 4.0 3 High 3 Exchange Cation 1.6 - 2.8 2 Depressed 1 < 1.6 1 < 500m 4 < 250m 4 500m - 1000m 3 Irrigation and 250m - 500m 3 Rural road systems 1000m - 1500m 2 drainage 500m - 1000m 2 > 1500m 1 > 1000m 1 Soybean Silty clay, Clay loam, Loam, > 35% 4 4 Silty clay loam, Soil texture Silty loam Base saturation Loamy sand, Sandy 20% - 35% 3 3 loam Coarse sand 1 < 20% 2 Actively irrigated 4 > 1.2 4 somewhat irrigated 3 Irrigation OM (%) 0.8 - 1.2 3 Poorly irrigated 2 < 0.8 2 None_irrigated 1 > 15 4 > 60 4 CEC (meq/100g soil) 10 - 15 3 40 - 60 3 Soil depth (cm) < 10 2 20 - 40 2 5.6 - 7.0 4 < 20 1 5.4 - 5.6 3 Good 4 Drainage pH(H2O) 5.2 - 5.4 2 Moderate 4 Flat 4 < 5.2 1 Low flat 2 >3.5 4 Relative topography Upper flat 4 Exchange Cation 2.0 - 3.5 3 High 3 < 2.0 2 Depressed 1 < 500m 4 < 250m 4 500m - 1000m 3 Irrigation and 250m - 500m 3 Rural road systems 1000m - 1500m 2 drainage 500m - 1000m 2 > 1500m 1 > 1000m 1 Green pepper Silty clay, Clay loam, Loam, > 35% 4 Silty clay loam, 4 Soil texture Silty loam, Sandy Base saturation loam 20% - 35% 3 Loamy sand, 3 Coarse sand 1 < 20% 2 Actively irrigated 4 > 0.8 4 somewhat irrigated 3 Irrigation OM (%) Poorly irrigated 2 < 0.8 3 None_irrigated 1 CEC (meq/100g soil) > 15 4 Soil depth (cm) > 75 4

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10 - 15 3 50 - 75 3 < 10 2 30 -50 2 6.0 - 6.8 4 < 30 1 5.5 - 6.0 3 Good 4 Drainage pH(H2O) 5.2 - 5.5 2 Moderate 4 Flat 4 < 5.2 1 Low flat 2 >3.5 4 Relative topography Upper flat 4 Exchange Cation 2.0 - 3.5 3 High 3 < 2.0 2 Depressed 1 < 500m 4 < 250m 4 500m - 1000m 3 Irrigation and 250m - 500m 3 Rural road systems 1000m - 1500m 2 drainage 500m - 1000m 2 > 1500m 1 > 1000m 1

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Appendix 5.3: Land suitability for paddy rice using the simple limitation method

Land Area Suitability I S D S R S TX S Dr S OM S BS S pH S EC S CEC S unit (ha) class 1 4 N1 3 S3 5 N1 6 N2 1 S2 3 S3 2 S2 4 S2 3 S1 3 S2 406.487 N2t 2 3 S3 2 S2 3 S2 4 S3 1 S2 3 S3 1 S1 3 S1 3 S1 3 S2 158.594 S3ito 3 3 S3 2 S2 3 S2 4 S3 1 S2 3 S3 1 S1 3 S1 1 S1 1 S1 788.626 S3ito 4 3 S3 2 S2 3 S2 3 S2 2 S1 3 S2 1 S1 4 S2 3 S2 3 S2 482.547 S3i 5 3 S3 2 S2 3 S2 5 S3 1 S2 2 S2 1 S1 4 S2 3 S1 3 S2 494.381 S3it 6 3 S3 1 S1 3 S2 4 S3 1 S2 3 S3 2 S2 4 S2 3 N1 3 S2 58.116 N1s 7 2 S2 2 S2 1 S1 5 S3 1 S2 3 S3 1 S1 2 S1 3 S1 3 S2 516.483 S3to 8 1 S1 1 S1 2 S1 3 S2 2 S1 3 S3 1 S1 3 S1 3 S1 3 S2 248.169 S3o 9 1 S1 2 S2 2 S1 3 S2 2 S1 1 S1 1 S1 3 S1 1 S1 1 S1 162.155 S2dt 10 2 S2 3 S3 3 S2 4 S3 1 S2 3 S3 2 S2 4 S2 3 S2 3 S2 70.778 S3dto 11 2 S2 2 S2 2 S1 3 S2 2 S1 1 S1 1 S1 3 S1 1 S1 1 S1 30.875 S2idt 12 2 S2 2 S2 3 S2 1 S2 2 S1 2 S1 1 S1 1 S1 1 S1 1 S1 166.309 S2idrt 13 2 S2 2 S1 3 S2 7 S1 2 S1 1 S1 1 S1 2 S1 2 S1 2 S1 290.246 S2ir 14 2 S2 1 S1 3 S2 3 S2 2 S1 1 S1 1 S1 2 S1 2 S1 2 S1 63.959 S2irt 15 1 S1 2 S2 1 S1 1 S2 2 S1 2 S2 2 S2 3 S1 2 S1 2 S1 295.125 S2to 16 2 S2 3 S3 3 S2 1 S2 2 S1 3 S2 1 S1 3 S1 3 S1 3 S2 527.703 S3d 17 2 S2 2 S2 3 S2 3 S2 2 S1 2 S2 1 S1 2 S1 3 S1 3 S2 469.194 S2idrtoc 18 2 S2 2 S2 1 S1 1 S2 2 S1 3 S2 2 S1 4 S2 3 S2 2 S1 132.653 S2idtohs 19 2 S2 2 S2 3 S2 3 S2 2 S1 1 S1 2 S2 4 S3 3 S1 3 S2 441.833 S3h 20 2 S2 1 S1 3 S1 2 S1 2 S1 2 S2 1 S1 3 S1 2 S1 2 S1 123.202 S2i 21 2 S2 2 S2 3 S2 1 S2 2 S1 2 S1 2 S2 4 S3 2 S1 1 S1 335.606 S3h 22 2 S2 2 S2 1 S1 1 S2 2 S1 2 S1 2 S2 4 S3 2 S1 1 S1 140.146 S3h 23 2 S2 2 S2 3 S2 2 S1 2 S1 2 S2 3 S3 4 S3 2 S1 2 S1 516.69 S3h 24 2 S2 2 S1 3 S2 3 S2 2 S1 2 S2 2 S2 3 S1 2 S1 2 S1 243.113 S2irtob 25 1 S1 2 S1 2 S1 2 S1 2 S1 2 S1 2 S2 4 S2 2 S1 2 S1 374.304 S2bh 26 1 S1 1 S1 1 S1 7 S2 2 S1 1 S1 1 S1 1 S1 1 S1 2 S1 298.2 S2d

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27 1 S1 3 S2 2 S1 3 S2 2 S1 1 S1 1 S1 1 S1 1 S1 2 S1 100.142 S2dt 28 1 S1 2 S1 4 S3 2 S1 2 S1 2 S1 2 S2 4 S2 2 S1 2 S1 358.003 S3r 29 1 S1 1 S1 4 S3 1 S2 2 S1 2 S1 1 S1 4 S2 2 S1 2 S1 950.035 S3r 30 1 S1 2 S2 2 S1 1 S2 2 S1 1 S1 1 S1 3 S1 1 S1 1 S1 96.693 S2dt 31 1 S1 2 S2 2 S1 7 S1 2 S1 1 S1 1 S1 2 S1 1 S1 1 S1 158.774 S2d 32 1 S1 2 S1 1 S1 1 S2 2 S1 1 S1 1 S1 4 S2 2 S1 2 S1 433.352 S2th 33 1 S1 2 S2 4 S3 8 S1 2 S1 1 S1 1 S1 4 S2 1 S1 1 S1 455.541 S3r 34 3 S3 2 S2 3 S3 3 S2 2 S1 3 S3 1 S1 4 S3 3 S2 3 S2 463.499 S3iroh 35 1 S1 3 S3 4 S3 1 S2 2 S1 3 S3 1 S1 3 S1 3 S1 3 S2 62.574 S3ro 36 4 N1 4 S3 6 N2 5 S3 1 S2 3 S3 2 S2 4 S3 3 S3 3 S2 218.378 N2l 37 1 S1 2 S1 2 S1 1 S2 2 S1 1 S1 2 S2 4 S2 1 S1 2 S1 260.352 S2tmh 38 1 S1 2 S2 4 S3 3 S2 2 S1 1 S1 2 S2 4 S2 1 S1 2 S1 405.503 S3r 39 1 S1 2 S2 4 S3 1 S2 2 S1 1 S1 2 S2 2 S1 1 S1 2 S1 629.006 S3r 40 2 S2 2 S2 3 S2 1 S2 2 S1 1 S1 1 S1 2 S1 1 S1 2 S1 457.277 S2idrt 41 2 S2 2 S2 3 S2 3 S2 2 S1 1 S1 2 S2 3 S1 1 S1 2 S1 404.387 S2idrtm 42 1 S1 2 S2 2 S1 1 S2 2 S1 2 S2 1 S1 2 S1 2 S1 2 S1 723.159 S2dto

Soil limitation factors: i: irrigated conditions; d: soi l depth; r: relative topography; t: soil texture; w: Drainage; o: organic matter; h: pHH2O; s: sum of exchangeable basic cations; c: CEC; b: base saturation. S: suitability level

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Appendix 5.4: Land suitability for sweet potato using the simple limitation method

Land Area Suitability I S D S R S TX S Dr S OM S BS S pH S EC S CEC S unit (ha) class 1 4 N1 3 S3 5 S2 6 N2 1 S1 3 S3 2 S1 4 S2 3 S1 3 S2 406.487 N2t 2 3 S3 2 S2 3 S1 4 S2 1 S1 3 S3 1 S1 3 S1 3 S1 3 S2 158.594 S3io 3 3 S3 2 S2 3 S1 4 S2 1 S1 3 S3 1 S1 3 S1 1 S1 1 S1 788.626 S3io 4 3 S2 2 S2 3 S1 3 S1 2 S1 3 S3 1 S1 4 S1 3 S1 3 S2 482.547 S3o 5 3 S3 2 S2 3 S1 5 S1 1 S1 2 S2 1 S1 4 S1 3 S1 3 S2 494.381 S3i 6 3 S3 1 S1 3 S1 4 S2 1 S1 3 S3 2 S1 4 S1 3 S3 3 S2 58.116 S3ios 7 2 S2 2 S2 1 S1 5 S1 1 S1 3 S3 1 S1 2 S1 3 S1 3 S2 516.483 S3o 8 1 S1 1 S1 2 S3 3 S1 2 S1 3 S3 1 S1 3 S1 3 S1 3 S2 248.169 S3ro 9 1 S1 2 S2 2 S3 3 S1 2 S1 1 S1 1 S1 3 S1 1 S1 1 S1 162.155 S3r 10 2 S2 3 S3 3 S1 4 S2 1 S1 3 S3 2 S1 4 S2 3 S2 3 S2 70.778 S3do 11 2 S2 2 S2 2 S3 3 S1 2 S1 1 S1 1 S1 3 S1 1 S1 1 S1 30.875 S3r 12 2 S2 2 S2 3 S1 1 S1 2 S1 2 S2 1 S1 1 S1 1 S1 1 S1 166.309 S2ido 13 2 S2 2 S1 3 S1 7 S1 2 S1 1 S1 1 S1 2 S1 2 S1 2 S1 290.246 S2i 14 2 S2 1 S1 3 S1 3 S1 2 S1 1 S1 1 S1 2 S1 2 S1 2 S1 63.959 S2i 15 1 S1 2 S2 1 S1 1 S1 2 S1 2 S2 2 S1 3 S1 2 S1 2 S1 295.125 S2do 16 2 S2 3 S3 3 S1 1 S1 2 S1 3 S3 1 S1 3 S1 3 S1 3 S2 527.703 S3do 17 2 S2 2 S2 3 S1 3 S1 2 S1 2 S2 1 S1 2 S1 3 S1 3 S2 469.194 S2idoc 18 2 S2 2 S2 1 S1 1 S1 2 S1 3 S3 2 S1 4 S2 3 S1 2 S1 132.653 S3o 19 2 S2 2 S2 3 S1 3 S1 2 S1 1 S1 2 S1 4 S3 3 S1 3 S2 441.833 S3h 20 2 S2 1 S1 3 S1 2 S1 2 S1 2 S2 1 S1 3 S1 2 S1 2 S1 123.202 S2io 21 2 S2 2 S2 3 S1 1 S1 2 S1 2 S2 2 S1 4 S3 2 S1 1 S1 335.606 S3h 22 2 S2 2 S2 1 S1 1 S1 2 S1 2 S2 2 S1 4 S2 2 S1 1 S1 140.146 S2ido 23 2 S2 2 S2 3 S1 2 S1 2 S1 2 S2 3 S2 4 S2 2 S1 2 S1 516.69 S2idomh 24 2 S2 2 S1 3 S1 3 S1 2 S1 2 S2 2 S1 3 S1 2 S1 2 S1 243.113 S2io 25 1 S1 2 S1 2 S3 2 S1 2 S1 2 S2 2 S1 4 S1 2 S1 2 S1 374.304 S3r

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26 1 S1 1 S1 1 S1 7 S1 2 S1 1 S1 1 S1 1 S1 1 S1 2 S1 298.2 S1 27 1 S1 3 S2 2 S3 3 S1 2 S1 1 S1 1 S1 1 S1 1 S1 2 S1 100.142 S3r 28 1 S1 2 S1 4 N1 2 S1 2 S1 2 S2 2 S1 4 S1 2 S1 2 S1 358.003 N1r 29 1 S1 1 S1 4 N1 1 S1 2 S1 2 S2 1 S1 4 S1 2 S1 2 S1 950.035 N1r 30 1 S1 2 S2 2 S3 1 S1 2 S1 1 S1 1 S1 3 S1 1 S1 1 S1 96.693 S3r 31 1 S1 2 S2 2 S3 7 S1 2 S1 1 S1 1 S1 2 S1 1 S1 1 S1 158.774 S3r 32 1 S1 2 S1 1 S1 1 S1 2 S1 1 S1 1 S1 4 S1 2 S1 2 S1 433.352 S1 33 1 S1 2 S2 4 N1 8 S1 2 S1 1 S1 1 S1 4 S2 1 S1 1 S1 455.541 N1r 34 3 S3 2 S2 3 S1 3 S1 2 S1 3 S3 1 S1 4 S3 3 S2 3 S2 463.499 S3ioh 35 1 S1 3 S3 4 N1 1 S1 2 S1 3 S3 1 S1 3 S1 3 S1 3 S2 62.574 N1r 36 4 N1 4 S3 6 N2 5 S1 1 S1 3 S3 2 S1 4 S3 3 S2 3 S2 218.378 N2l 37 1 S1 2 S1 2 S3 1 S1 2 S1 1 S1 2 S1 4 S1 1 S1 2 S1 260.352 S3r 38 1 S1 2 S2 4 N1 3 S1 2 S1 1 S1 2 S1 4 S1 1 S1 2 S1 405.503 N1r 39 1 S1 2 S2 4 N1 1 S1 2 S1 1 S1 2 S1 2 S1 1 S1 2 S1 629.006 N1r 40 2 S2 2 S2 3 S1 1 S1 2 S1 1 S1 1 S1 2 S1 1 S1 2 S1 457.277 S2id 41 2 S2 2 S2 3 S1 3 S1 2 S1 1 S1 2 S1 3 S1 1 S1 2 S1 404.387 S2id 42 1 S1 2 S2 2 S3 1 S1 2 S1 2 S2 1 S1 2 S1 2 S1 2 S1 723.159 S3r

Soil limitation factors: i: irrigated conditions; d: soil depth; r: relative topography; t: soil texture; w: Drainage; o: organic matter; h: pHH2O; s: sum of exchangeable basic cations; c: CEC; b: base saturation. S: suitability level

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Appendix 5.5: Land suitability for groundnut using the simple limitation method

Land Area Suitability I S D S R S TX S Dr S OM S BS S pH S EC S CEC S unit (ha) class 1 4 N1 3 S3 5 S2 6 N2 1 S1 3 S3 2 S1 4 N1 3 S1 3 S3 406.487 N2t 2 3 S3 2 S2 3 S1 4 S2 1 S1 3 S3 1 S1 3 S2 3 S1 3 S3 158.594 S3ioc 3 3 S3 2 S2 3 S1 4 S2 1 S1 3 S3 1 S1 3 S2 1 S1 1 S1 788.626 S3io 4 3 S2 2 S2 3 S1 3 S1 2 S2 3 S2 1 S1 4 S3 3 S1 3 S3 482.547 S3hc 5 3 S3 2 S2 3 S1 5 S1 1 S1 2 S2 1 S1 4 N1 3 S1 3 S3 494.381 N1h 6 3 S3 1 S1 3 S1 4 S2 1 S1 3 S3 2 S1 4 N1 3 S3 3 S3 58.116 N1h 7 2 S2 2 S2 1 S1 5 S1 1 S1 3 S3 1 S1 2 S1 3 S1 3 S3 516.483 S3oc 8 1 S1 1 S1 2 S3 3 S1 2 S2 3 S3 1 S1 3 S2 3 S1 3 S3 248.169 S3roc 9 1 S1 2 S2 2 S3 3 S1 2 S2 1 S1 1 S1 3 S2 1 S1 1 S1 162.155 S3r 10 2 S2 3 S3 3 S1 4 S2 1 S1 3 S3 2 S1 4 N1 3 S1 3 S3 70.778 N1h 11 2 S2 2 S2 2 S3 3 S1 2 S2 1 S1 1 S1 3 S2 1 S1 1 S1 30.875 S3r 12 2 S2 2 S2 3 S1 1 S1 2 S2 2 S1 1 S1 1 S1 1 S1 1 S1 166.309 S2idw 13 2 S2 2 S1 3 S1 7 S1 2 S2 1 S1 1 S1 2 S1 2 S1 2 S2 290.246 S2iwc 14 2 S2 1 S1 3 S1 3 S1 2 S2 1 S1 1 S1 2 S1 2 S1 2 S2 63.959 S2iwc 15 1 S1 2 S2 1 S1 1 S1 2 S2 2 S2 2 S1 3 S2 2 S1 2 S2 295.125 S2dwhc 16 2 S2 3 S3 3 S1 1 S1 2 S2 3 S2 1 S1 3 S3 3 S1 3 S3 527.703 S3dhc 17 2 S2 2 S2 3 S1 3 S1 2 S2 2 S1 1 S1 2 S1 3 S1 3 S3 469.194 S3c 18 2 S2 2 S2 1 S1 1 S1 2 S2 3 S2 2 S1 4 N1 3 S1 2 S2 132.653 N1h 19 2 S2 2 S2 3 S1 3 S1 2 S2 1 S1 2 S1 4 N1 3 S1 3 S3 441.833 N1h 20 2 S2 1 S1 3 S1 2 S1 2 S2 2 S1 1 S1 3 S2 2 S1 2 S2 123.202 S3c 21 2 S2 2 S2 3 S1 1 S1 2 S2 2 S1 2 S1 4 N1 2 S1 1 S1 335.606 N1h 22 2 S2 2 S2 1 S1 1 S1 2 S2 2 S1 2 S1 4 N1 2 S1 1 S1 140.146 N1h 23 2 S2 2 S2 3 S1 2 S1 2 S2 2 S1 3 S2 4 N1 2 S1 2 S2 516.69 N1h 24 2 S2 2 S1 3 S1 3 S1 2 S2 2 S1 2 S1 3 S2 2 S1 2 S2 243.113 S2iwhc 25 1 S1 2 S1 2 S3 2 S1 2 S2 2 S1 2 S1 4 N1 2 S1 2 S2 374.304 N1h

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26 1 S1 1 S1 1 S1 7 S1 2 S2 1 S1 1 S1 1 S1 1 S1 2 S2 298.2 S2Drc 27 1 S1 3 S2 2 S3 3 S1 2 S2 1 S1 1 S1 1 S1 1 S1 2 S2 100.142 S3r 28 1 S1 2 S1 4 N1 2 S1 2 S2 2 S1 2 S1 4 S3 2 S1 2 S2 358.003 N1r 29 1 S1 1 S1 4 N1 1 S1 2 S2 2 S1 1 S1 4 N1 2 S1 2 S2 950.035 N1rh 30 1 S1 2 S2 2 S3 1 S1 2 S2 1 S1 1 S1 3 S2 1 S1 1 S1 96.693 S3r 31 1 S1 2 S2 2 S3 7 S1 2 S2 1 S1 1 S1 2 S1 1 S1 1 S1 158.774 S3r 32 1 S1 2 S1 1 S1 1 S1 2 S2 1 S1 1 S1 4 S3 2 S1 2 S2 433.352 S3h 33 1 S1 2 S2 4 N1 8 S1 2 S2 1 S1 1 S1 4 N1 1 S1 1 S1 455.541 N1h 34 3 S3 2 S2 3 S1 3 S1 2 S2 3 S3 1 S1 4 N1 3 S1 3 S3 463.499 N1h 35 1 S1 3 S3 4 N1 1 S1 2 S2 3 S3 1 S1 3 S2 3 S1 3 S3 62.574 N1r 36 4 N1 4 S3 6 N2 5 S1 1 S1 3 S3 2 S1 4 N1 3 S3 3 S3 218.378 N2l 37 1 S1 2 S1 2 S3 1 S1 2 S2 1 S1 2 S1 4 N1 1 S1 2 S2 260.352 N1h 38 1 S1 2 S2 4 N1 3 S1 2 S2 1 S1 2 S1 4 S3 1 S1 2 S2 405.503 N1r 39 1 S1 2 S2 4 N1 1 S1 2 S2 1 S1 2 S1 2 S1 1 S1 2 S2 629.006 N1r 40 2 S2 2 S2 3 S1 1 S1 2 S2 1 S1 1 S1 2 S1 1 S1 2 S2 457.277 S2idwc 41 2 S2 2 S2 3 S1 3 S1 2 S2 1 S1 2 S1 3 S3 1 S1 2 S2 404.387 S3h 42 1 S1 2 S2 2 S3 1 S1 2 S2 2 S1 1 S1 2 S1 2 S1 2 S2 723.159 S3r

Soil limitation factors: i: irrigated conditions; d: soil depth; r: relative topography; t: soil texture; w: Drainage; o: organic matter; h: pHH2O; s: sum of exchangeable basic cations; c: CEC; b: base saturation. S: suitability level

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Appendix 5.6: Land suitability for maize using the simple limitation method

Land Area Suitability I S D S R S TX S Dr S OM S BS S pH S EC S CEC S unit (ha) class 1 4 N1 3 S3 5 S2 6 N2 1 S1 3 S3 2 S2 4 N1 3 S2 3 S2 406.487 N2t 2 3 S3 2 S2 3 S1 4 S2 1 S1 3 S3 1 S1 3 S2 3 S2 3 S2 158.594 S3io 3 3 S3 2 S2 3 S1 4 S2 1 S1 3 S3 1 S1 3 S2 1 S1 1 S1 788.626 S3io 4 3 S2 2 S2 3 S1 3 S1 2 S1 3 S2 1 S1 4 S3 3 S3 3 S2 482.547 S3hs 5 3 S3 2 S2 3 S1 5 S2 1 S1 2 S2 1 S1 4 S3 3 S2 3 S2 494.381 S3ih 6 3 S3 1 S1 3 S1 4 S2 1 S1 3 S3 2 S2 4 S3 3 N1 3 S2 58.116 N1s 7 2 S2 2 S2 1 S1 5 S2 1 S1 3 S3 1 S1 2 S1 3 S1 3 S2 516.483 S3o 8 1 S1 1 S1 2 S3 3 S1 2 S1 3 S3 1 S1 3 S1 3 S2 3 S2 248.169 S3ro 9 1 S1 2 S2 2 S3 3 S1 2 S1 1 S1 1 S1 3 S2 1 S1 1 S1 162.155 S3r 10 2 S2 3 S3 3 S1 4 S2 1 S1 3 S3 2 S2 4 N1 3 S3 3 S2 70.778 N1h 11 2 S2 2 S2 2 S3 3 S1 2 S1 1 S1 1 S1 3 S2 1 S1 1 S1 30.875 S3r 12 2 S2 2 S2 3 S1 1 S1 2 S1 2 S1 1 S1 1 S1 1 S1 1 S1 166.309 S2id 13 2 S2 2 S1 3 S1 7 S1 2 S1 1 S1 1 S1 2 S1 2 S1 2 S1 290.246 S2i 14 2 S2 1 S1 3 S1 3 S1 2 S1 1 S1 1 S1 2 S1 2 S1 2 S1 63.959 S2i 15 1 S1 2 S2 1 S1 1 S1 2 S1 2 S2 2 S2 3 S2 2 S1 2 S1 295.125 S2domh 16 2 S2 3 S3 3 S1 1 S1 2 S1 3 S2 1 S1 3 S2 3 S1 3 S2 527.703 S3d 17 2 S2 2 S2 3 S1 3 S1 2 S1 2 S1 1 S1 2 S1 3 S1 3 S2 469.194 S2idc 18 2 S2 2 S2 1 S1 1 S1 2 S1 3 S2 2 S1 4 N1 3 S2 2 S1 132.653 N1h 19 2 S2 2 S2 3 S1 3 S1 2 S1 1 S1 2 S2 4 N1 3 S1 3 S2 441.833 N1h 20 2 S2 1 S1 3 S1 2 S1 2 S1 2 S1 1 S1 3 S1 2 S1 2 S1 123.202 S2i 21 2 S2 2 S2 3 S1 1 S1 2 S1 2 S1 2 S2 4 N1 2 S1 1 S1 335.606 N1h 22 2 S2 2 S2 1 S1 1 S1 2 S1 2 S1 2 S2 4 N1 2 S1 1 S1 140.146 N1h 23 2 S2 2 S2 3 S1 2 S1 2 S1 2 S1 3 S3 4 N1 2 S1 2 S1 516.69 N1h 24 2 S2 2 S1 3 S1 3 S1 2 S1 2 S1 2 S2 3 S2 2 S1 2 S1 243.113 S2imh 25 1 S1 2 S1 2 S3 2 S1 2 S1 2 S1 2 S2 4 S3 2 S1 2 S1 374.304 S3rh

225

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26 1 S1 1 S1 1 S1 7 S1 2 S1 1 S1 1 S1 1 S1 1 S1 2 S1 298.2 S1 27 1 S1 3 S2 2 S3 3 S1 2 S1 1 S1 1 S1 1 S1 1 S1 2 S1 100.142 S3r 28 1 S1 2 S1 4 N1 2 S1 2 S1 2 S1 2 S2 4 S3 2 S1 2 S1 358.003 N1r 29 1 S1 1 S1 4 N1 1 S1 2 S1 2 S1 1 S1 4 S3 2 S1 2 S1 950.035 N1r 30 1 S1 2 S2 2 S3 1 S1 2 S1 1 S1 1 S1 3 S1 1 S1 1 S1 96.693 S3r 31 1 S1 2 S2 2 S3 7 S1 2 S1 1 S1 1 S1 2 S1 1 S1 1 S1 158.774 S3r 32 1 S1 2 S1 1 S1 1 S1 2 S1 1 S1 1 S1 4 S3 2 S1 2 S1 433.352 S3h 33 1 S1 2 S2 4 N1 8 S1 2 S1 1 S1 1 S1 4 N1 1 S1 1 S1 455.541 N1rh 34 3 S3 2 S2 3 S1 3 S1 2 S1 3 S3 1 S1 4 N1 3 S3 3 S2 463.499 N1h 35 1 S1 3 S3 4 N1 1 S1 2 S1 3 S3 1 S1 3 S2 3 S2 3 S2 62.574 N1r 36 4 N1 4 S3 6 N2 5 S1 1 S1 3 S3 2 S2 4 N1 3 N1 3 S2 218.378 N2l 37 1 S1 2 S1 2 S3 1 S1 2 S1 1 S1 2 S2 4 S3 1 S1 2 S1 260.352 S3rh 38 1 S1 2 S2 4 N1 3 S1 2 S1 1 S1 2 S2 4 S3 1 S1 2 S1 405.503 N1r 39 1 S1 2 S2 4 N1 1 S1 2 S1 1 S1 2 S2 2 S1 1 S1 2 S1 629.006 N1r 40 2 S2 2 S2 3 S1 1 S1 2 S1 1 S1 1 S1 2 S1 1 S1 2 S1 457.277 S2id 41 2 S2 2 S2 3 S1 3 S1 2 S1 1 S1 2 S2 3 S2 1 S1 2 S1 404.387 S2idmh 42 1 S1 2 S2 2 S3 1 S1 2 S1 2 S1 1 S1 2 S1 2 S1 2 S1 723.159 S3r

Soil limitation factors: i: irrigated conditions; d: soil depth; r: relative topography; t : soil texture; w: Drainage; o: organic matter; h: pHH2O; s : sum of exchangeable basic cations; c: CEC; b: base saturation. S: suitability level

226

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Appendix 5.7: Land suitability for potato using the simple limitation method

Land Area Suitability I S D S R S TX S Dr S OM S BS S pH S EC S CEC S unit (ha) class 1 4 N1 3 S2 5 S2 6 N2 1 S1 3 S3 2 S1 4 S3 3 S1 3 S2 406.487 N2t 2 3 S3 2 S1 3 S1 4 S2 1 S1 3 S3 1 S1 3 S1 3 S1 3 S2 158.594 S3io 3 3 S3 2 S1 3 S1 4 S2 1 S1 3 S3 1 S1 3 S1 1 S1 1 S1 788.626 S3io 4 3 S3 2 S2 3 S1 3 S1 2 S1 3 S2 1 S1 4 S2 3 S1 3 S2 482.547 S3i 5 3 S3 2 S1 3 S1 5 S1 1 S1 2 S2 1 S1 4 S2 3 S1 3 S2 494.381 S3i 6 3 S3 1 S1 3 S1 4 S2 1 S1 3 S3 2 S1 4 S2 3 S3 3 S2 58.116 S3is 7 2 S2 2 S1 1 S1 5 S1 1 S1 3 S3 1 S1 2 S1 3 S1 3 S2 516.483 S3o 8 1 S1 1 S1 2 S3 3 S1 2 S1 3 S3 1 S1 3 S1 3 S1 3 S2 248.169 S3ro 9 1 S1 2 S1 2 S3 3 S1 2 S1 1 S1 1 S1 3 S1 1 S1 1 S1 162.155 S3r 10 2 S2 3 S2 3 S1 4 S2 1 S1 3 S3 2 S1 4 S3 3 S2 3 S2 70.778 S3oh 11 2 S2 2 S1 2 S3 3 S1 2 S1 1 S1 1 S1 3 S1 1 S1 1 S1 30.875 S3r 12 2 S2 2 S1 3 S1 1 S1 2 S1 2 S1 1 S1 1 S1 1 S1 1 S1 166.309 S2i 13 2 S2 2 S1 3 S1 7 S1 2 S1 1 S1 1 S1 2 S1 2 S1 2 S1 290.246 S2i 14 2 S2 1 S1 3 S1 3 S1 2 S1 1 S1 1 S1 2 S1 2 S1 2 S1 63.959 S2i 15 1 S1 2 S1 1 S1 1 S1 2 S1 2 S2 2 S1 3 S1 2 S1 2 S1 295.125 S2o 16 2 S2 3 S3 3 S1 1 S1 2 S1 3 S2 1 S1 3 S2 3 S1 3 S2 527.703 S3d 17 2 S2 2 S1 3 S1 3 S1 2 S1 2 S1 1 S1 2 S1 3 S1 3 S2 469.194 S2ic 18 2 S2 2 S1 1 S1 1 S1 2 S1 3 S2 2 S1 4 S3 3 S1 2 S1 132.653 S3h 19 2 S2 2 S1 3 S1 3 S1 2 S1 1 S1 2 S1 4 N1 3 S1 3 S2 441.833 N1h 20 2 S2 1 S1 3 S1 2 S1 2 S1 2 S1 1 S1 3 S1 2 S1 2 S1 123.202 S2i 21 2 S2 2 S2 3 S1 1 S1 2 S1 2 S1 2 S1 4 N1 2 S1 1 S1 335.606 N1h 22 2 S2 2 S1 1 S1 1 S1 2 S1 2 S1 2 S1 4 S3 2 S1 1 S1 140.146 S3h 23 2 S2 2 S1 3 S1 2 S1 2 S1 2 S1 3 S2 4 S3 2 S1 2 S1 516.69 S3h 24 2 S2 2 S1 3 S1 3 S1 2 S1 2 S1 2 S1 3 S1 2 S1 2 S1 243.113 S2i 25 1 S1 2 S1 2 S3 2 S1 2 S1 2 S1 2 S1 4 S3 2 S1 2 S1 374.304 S3rh

227

Appendixes

26 1 S1 1 S1 1 S1 7 S1 2 S1 1 S1 1 S1 1 S1 1 S1 2 S1 298.2 S1 27 1 S1 3 S2 2 S3 3 S1 2 S1 1 S1 1 S1 1 S1 1 S1 2 S1 100.142 S3r 28 1 S1 2 S1 4 N1 2 S1 2 S1 2 S1 2 S1 4 S2 2 S1 2 S1 358.003 N1r 29 1 S1 1 S1 4 N1 1 S1 2 S1 2 S1 1 S1 4 S2 2 S1 2 S1 950.035 N1r 30 1 S1 2 S1 2 S3 1 S1 2 S1 1 S1 1 S1 3 S1 1 S1 1 S1 96.693 S3r 31 1 S1 2 S1 2 S3 7 S1 2 S1 1 S1 1 S1 2 S1 1 S1 1 S1 158.774 S3r 32 1 S1 2 S1 1 S1 1 S1 2 S1 1 S1 1 S1 4 S2 2 S1 2 S1 433.352 S2h 33 1 S1 2 S1 4 N1 8 S1 2 S1 1 S1 1 S1 4 S3 1 S1 1 S1 455.541 N1r 34 3 S3 2 S1 3 S1 3 S1 2 S1 3 S3 1 S1 4 N1 3 S2 3 S2 463.499 N1h 35 1 S1 3 S2 4 N1 1 S1 2 S1 3 S3 1 S1 3 S1 3 S1 3 S2 62.574 N1r 36 4 N1 4 S3 6 N2 5 S1 1 S1 3 S3 2 S1 4 N1 3 S2 3 S2 218.378 N2l 37 1 S1 2 S1 2 S3 1 S1 2 S1 1 S1 2 S1 4 S2 1 S1 2 S1 260.352 S3r 38 1 S1 2 S1 4 N1 3 S1 2 S1 1 S1 2 S1 4 S2 1 S1 2 S1 405.503 N1r 39 1 S1 2 S1 4 N1 1 S1 2 S1 1 S1 2 S1 2 S1 1 S1 2 S1 629.006 N1r 40 2 S2 2 S1 3 S1 1 S1 2 S1 1 S1 1 S1 2 S1 1 S1 2 S1 457.277 S2i 41 2 S2 2 S1 3 S1 3 S1 2 S1 1 S1 2 S1 3 S2 1 S1 2 S1 404.387 S2ih 42 1 S1 2 S1 2 S3 1 S1 2 S1 2 S1 1 S1 2 S1 2 S1 2 S1 723.159 S3r

Soil limitation factors: i: irrigated conditions; d: soil depth; r: relative topography; t: soil texture; w: Drainage; o: organic matter; h: pHH2O; s: sum of exchangeable basic cations; c: CEC; b: base saturation. S: suitability level

228

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Appendix 5.8: Land suitability for sesame using the simple limitation method

Land Area Suitability I S D S R S TX S Dr S OM S BS S pH S EC S CEC S unit (ha) class 1 4 N1 3 S3 5 S2 6 N2 1 S1 3 S3 2 S2 4 N1 3 S1 3 S2 406.487 N2t 2 3 S3 2 S2 3 S1 4 S2 1 S1 3 S3 1 S1 3 S2 3 S1 3 S2 158.594 S3io 3 3 S3 2 S2 3 S1 4 S2 1 S1 3 S3 1 S1 3 S2 1 S1 1 S1 788.626 S3io 4 3 S2 2 S2 3 S1 3 S1 2 S1 3 S2 1 S1 4 S3 3 S2 3 S2 482.547 S3h 5 3 S3 2 S2 3 S1 5 S2 1 S1 2 S2 1 S1 4 S3 3 S1 3 S2 494.381 S3ih 6 3 S3 1 S1 3 S1 4 S2 1 S1 3 S3 2 S2 4 S3 3 N1 3 S2 58.116 N1s 7 2 S2 2 S2 1 S1 5 S1 1 S1 3 S3 1 S1 2 S1 3 S1 3 S2 516.483 S3o 8 1 S1 1 S1 2 S3 3 S1 2 S1 3 S3 1 S1 3 S1 3 S1 3 S2 248.169 S3ro 9 1 S1 2 S2 2 S3 3 S1 2 S1 1 S1 1 S1 3 S2 1 S1 1 S1 162.155 S3r 10 2 S2 3 S3 3 S1 4 S2 1 S1 3 S3 2 S2 4 N1 3 S2 3 S2 70.778 N1h 11 2 S2 2 S2 2 S3 3 S1 2 S1 1 S1 1 S1 3 S2 1 S1 1 S1 30.875 S3r 12 2 S2 2 S2 3 S1 1 S1 2 S1 2 S1 1 S1 1 S1 1 S1 1 S1 166.309 S2id 13 2 S2 2 S1 3 S1 7 S1 2 S1 1 S1 1 S1 2 S1 2 S1 2 S1 290.246 S2i 14 2 S2 1 S1 3 S1 3 S1 2 S1 1 S1 1 S1 2 S1 2 S1 2 S1 63.959 S2i 15 1 S1 2 S2 1 S1 1 S1 2 S1 2 S2 2 S2 3 S2 2 S1 2 S1 295.125 S2domh 16 2 S2 3 S3 3 S1 1 S1 2 S1 3 S2 1 S1 3 S2 3 S1 3 S2 527.703 S3d 17 2 S2 2 S2 3 S1 3 S1 2 S1 2 S1 1 S1 2 S1 3 S1 3 S2 469.194 S2idc 18 2 S2 2 S2 1 S1 1 S1 2 S1 3 S2 2 S1 4 N1 3 S2 2 S1 132.653 N1h 19 2 S2 2 S2 3 S1 3 S1 2 S1 1 S1 2 S2 4 N1 3 S1 3 S2 441.833 N1h 20 2 S2 1 S1 3 S1 2 S1 2 S1 2 S1 1 S1 3 S1 2 S1 2 S1 123.202 S2i 21 2 S2 2 S2 3 S1 1 S1 2 S1 2 S1 2 S2 4 N1 2 S1 1 S1 335.606 N1h 22 2 S2 2 S2 1 S1 1 S1 2 S1 2 S1 2 S2 4 N1 2 S1 1 S1 140.146 N1h 23 2 S2 2 S2 3 S1 2 S1 2 S1 2 S1 3 S3 4 N1 2 S1 2 S1 516.69 N1h 24 2 S2 2 S1 3 S1 3 S1 2 S1 2 S1 2 S1 3 S2 2 S1 2 S1 243.113 S2ih 25 1 S1 2 S1 2 S3 2 S1 2 S1 2 S1 2 S2 4 S3 2 S1 2 S1 374.304 S3rh

229

Appendixes

26 1 S1 1 S1 1 S1 7 S1 2 S1 1 S1 1 S1 1 S1 1 S1 2 S1 298.2 S1 27 1 S1 3 S2 2 S3 3 S1 2 S1 1 S1 1 S1 1 S1 1 S1 2 S1 100.142 S3r 28 1 S1 2 S1 4 N1 2 S1 2 S1 2 S1 2 S2 4 S3 2 S1 2 S1 358.003 N1r 29 1 S1 1 S1 4 N1 1 S1 2 S1 2 S1 1 S1 4 S3 2 S1 2 S1 950.035 N1r 30 1 S1 2 S2 2 S3 1 S1 2 S1 1 S1 1 S1 3 S1 1 S1 1 S1 96.693 S3r 31 1 S1 2 S2 2 S3 7 S1 2 S1 1 S1 1 S1 2 S1 1 S1 1 S1 158.774 S3r 32 1 S1 2 S1 1 S1 1 S1 2 S1 1 S1 1 S1 4 S3 2 S1 2 S1 433.352 S3h 33 1 S1 2 S2 4 N1 8 S1 2 S1 1 S1 1 S1 4 N1 1 S1 1 S1 455.541 N1rh 34 3 S3 2 S2 3 S1 3 S1 2 S1 3 S3 1 S1 4 N1 3 S2 3 S2 463.499 N1h 35 1 S1 3 S3 4 N1 1 S1 2 S1 3 S3 1 S1 3 S2 3 S1 3 S2 62.574 N1r 36 4 N1 4 N2 6 N2 5 S1 1 S1 3 S3 2 S2 4 N1 3 S3 3 S2 218.378 N2dr 37 1 S1 2 S1 2 S3 1 S1 2 S1 1 S1 2 S2 4 S2 1 S1 2 S1 260.352 S3r 38 1 S1 2 S2 4 N1 3 S1 2 S1 1 S1 2 S2 4 S2 1 S1 2 S1 405.503 N1r 39 1 S1 2 S2 4 N1 1 S1 2 S1 1 S1 2 S2 2 S1 1 S1 2 S1 629.006 N1r 40 2 S2 2 S2 3 S1 1 S1 2 S1 1 S1 1 S1 2 S1 1 S1 2 S1 457.277 S2id 41 2 S2 2 S2 3 S1 3 S1 2 S1 1 S1 2 S2 3 S2 1 S1 2 S1 404.387 S2idmh 42 1 S1 2 S2 2 S3 1 S1 2 S1 2 S1 1 S1 2 S1 2 S1 2 S1 723.159 S3r

Soil limitation factors: i: irrigated conditions; d: soil depth; r: relative topography; t: soil texture; w: Drainage; o: organic matter; h: pHH2O; s: sum of exchangeable basic cations; c: CEC; b: base saturation. S: suitability level

230

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Appendix 5.9: Land suitability for soybean using the simple limitation method

Suitabilit Land Area I S D S R S TX S Dr S OM S BS S pH S EC S CEC S y unit (ha) class 1 4 N1 3 S3 5 S2 6 N2 1 S1 3 S3 2 S1 4 N1 3 S1 3 S3 406.487 N2t 2 3 S3 2 S2 3 S1 4 S2 1 S1 3 S3 1 S1 3 S1 3 S1 3 S3 158.594 S3ioc 3 3 S3 2 S2 3 S1 4 S2 1 S1 3 S3 1 S1 3 S1 1 S1 1 S1 788.626 S3io 4 3 S2 2 S2 3 S1 3 S1 2 S1 3 S2 1 S1 4 S2 3 S1 3 S3 482.547 S3c 5 3 S3 2 S2 3 S1 5 S2 1 S1 2 S2 1 S1 4 S3 3 S1 3 S3 494.381 S3ihc 6 3 S3 1 S1 3 S1 4 S2 1 S1 3 S3 2 S1 4 S3 3 S3 3 S3 58.116 S3iohc 7 2 S2 2 S2 1 S1 5 S2 1 S1 3 S3 1 S1 2 S1 3 S1 3 S3 516.483 S3oc 8 1 S1 1 S1 2 S3 3 S1 2 S1 3 S3 1 S1 3 S1 3 S1 3 S3 248.169 S3roc 9 1 S1 2 S2 2 S3 3 S1 2 S1 1 S1 1 S1 3 S1 1 S1 1 S1 162.155 S3r 10 2 S2 3 S3 3 S1 4 S2 1 S1 3 S3 2 S1 4 S3 3 S2 3 S3 70.778 S3dohc 11 2 S2 2 S2 2 S3 3 S1 2 S1 1 S1 1 S1 3 S1 1 S1 1 S1 30.875 S3r 12 2 S2 2 S2 3 S1 1 S1 2 S1 2 S1 1 S1 1 S1 1 S1 1 S1 166.309 S2id 13 2 S2 2 S1 3 S1 7 S1 2 S1 1 S1 1 S1 2 S1 2 S1 2 S2 290.246 S2ic 14 2 S2 1 S1 3 S1 3 S1 2 S1 1 S1 1 S1 2 S1 2 S1 2 S2 63.959 S2ic 15 1 S1 2 S2 1 S1 1 S1 2 S1 2 S2 2 S1 3 S1 2 S1 2 S2 295.125 S2doc 16 2 S2 3 S3 3 S1 1 S1 2 S1 3 S2 1 S1 3 S2 3 S1 3 S3 527.703 S3dc 17 2 S2 2 S2 3 S1 3 S1 2 S1 2 S1 1 S1 2 S1 3 S1 3 S3 469.194 S3c 18 2 S2 2 S2 1 S1 1 S1 2 S1 3 S2 2 S1 4 N1 3 S1 2 S2 132.653 N1h 19 2 S2 2 S2 3 S1 3 S1 2 S1 1 S1 2 S1 4 N1 3 S1 3 S3 441.833 N1h 20 2 S2 1 S1 3 S1 2 S1 2 S1 2 S1 1 S1 3 S1 2 S1 2 S2 123.202 S2ic 21 2 S2 2 S2 3 S1 1 S1 2 S1 2 S1 2 S1 4 N1 2 S1 1 S1 335.606 N1h 22 2 S2 2 S2 1 S1 1 S1 2 S1 2 S1 2 S1 4 N1 2 S1 1 S1 140.146 N1h 23 2 S2 2 S2 3 S1 2 S1 2 S1 2 S1 3 S2 4 N1 2 S1 2 S2 516.69 N1h 24 2 S2 2 S1 3 S1 3 S1 2 S1 2 S1 2 S1 3 S1 2 S1 2 S2 243.113 S2ic 25 1 S1 2 S1 2 S3 2 S1 2 S1 2 S1 2 S1 4 S3 2 S1 2 S2 374.304 S3rh

231

Appendixes

26 1 S1 1 S1 1 S1 7 S1 2 S1 1 S1 1 S1 1 S1 1 S1 2 S2 298.2 S2c 27 1 S1 3 S2 2 S3 3 S1 2 S1 1 S1 1 S1 1 S1 1 S1 2 S2 100.142 S3r 28 1 S1 2 S1 4 N1 2 S1 2 S1 2 S1 2 S1 4 S2 2 S1 2 S2 358.003 N1r 29 1 S1 1 S1 4 N1 1 S1 2 S1 2 S1 1 S1 4 S3 2 S1 2 S2 950.035 N1r 30 1 S1 2 S2 2 S3 1 S1 2 S1 1 S1 1 S1 3 S1 1 S1 1 S1 96.693 S3r 31 1 S1 2 S2 2 S3 7 S1 2 S1 1 S1 1 S1 2 S1 1 S1 1 S1 158.774 S3r 32 1 S1 2 S1 1 S1 1 S1 2 S1 1 S1 1 S1 4 S2 2 S1 2 S2 433.352 S2hc 33 1 S1 2 S2 4 N1 8 S1 2 S1 1 S1 1 S1 4 N1 1 S1 1 S1 455.541 N1rh 34 3 S3 2 S2 3 S1 3 S1 2 S1 3 S3 1 S1 4 N1 3 S2 3 S3 463.499 N1h 35 1 S1 3 S3 4 N1 1 S1 2 S1 3 S3 1 S1 3 S1 3 S1 3 S3 62.574 N1r 36 4 N1 4 S3 6 N2 5 S2 1 S1 3 S3 2 S1 4 N1 3 S2 3 S3 218.378 N2l 37 1 S1 2 S1 2 S3 1 S1 2 S1 1 S1 2 S1 4 S3 1 S1 2 S2 260.352 S3rh 38 1 S1 2 S2 4 N1 3 S1 2 S1 1 S1 2 S1 4 S2 1 S1 2 S2 405.503 N1r 39 1 S1 2 S2 4 N1 1 S1 2 S1 1 S1 2 S1 2 S1 1 S1 2 S2 629.006 N1r 40 2 S2 2 S2 3 S1 1 S1 2 S1 1 S1 1 S1 2 S1 1 S1 2 S2 457.277 S2idc 41 2 S2 2 S2 3 S1 3 S1 2 S1 1 S1 2 S1 3 S2 1 S1 2 S2 404.387 S2idhc 42 1 S1 2 S2 2 S3 1 S1 2 S1 2 S1 1 S1 2 S1 2 S1 2 S2 723.159 S3r

Soil limitation factors: i: irrigated conditions; d: soil depth; r: relative topography; t: soil texture; w: Drainage; o: organic matter; h: pHH2O; s: sum of exchangeable basic cations; c: CEC; b: base saturation. S: suitability level

232

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Appendix 5.10: Land suitability for green pepper using the simple limitation method

Land Area Suitability I S D S R S TX S Dr S OM S BS S pH S EC S CEC S unit (ha) class

1 4 N1 3 S3 5 S2 6 N2 1 S1 3 S2 2 S1 4 N1 3 S1 3 S3 406.487 N2t 2 3 S3 2 S2 3 S1 4 S2 1 S1 3 S2 1 S1 3 S2 3 S1 3 S3 158.594 S3ic 3 3 S3 2 S2 3 S1 4 S2 1 S1 3 S1 1 S1 3 S2 1 S1 1 S1 788.626 S3i 4 3 S3 2 S2 3 S1 3 S1 2 S1 3 S1 1 S1 4 S3 3 S1 3 S3 482.547 S3hc 5 3 S3 2 S2 3 S1 5 S1 1 S1 2 S1 1 S1 4 S3 3 S1 3 S3 494.381 S3ihc 6 3 S3 1 S1 3 S1 4 S2 1 S1 3 S2 2 S1 4 S3 3 S3 3 S3 58.116 S3ihsc 7 2 S2 2 S2 1 S1 5 S1 1 S1 3 S2 1 S1 2 S1 3 S1 3 S3 516.483 S3c 8 1 S1 1 S1 2 S3 3 S1 2 S1 3 S2 1 S1 3 S2 3 S1 3 S3 248.169 S3rc 9 1 S1 2 S2 2 S3 3 S1 2 S1 1 S1 1 S1 3 S2 1 S1 1 S1 162.155 S3r 10 2 S2 3 S3 3 S1 4 S2 1 S1 3 S2 2 S1 4 N1 3 S2 3 S3 70.778 N1h 11 2 S2 2 S2 2 S3 3 S1 2 S1 1 S1 1 S1 3 S2 1 S1 1 S1 30.875 S3r 12 2 S2 2 S2 3 S1 1 S1 2 S1 2 S1 1 S1 1 S1 1 S1 1 S1 166.309 S2id 13 2 S2 2 S1 3 S1 7 S1 2 S1 1 S1 1 S1 2 S1 2 S1 2 S2 290.246 S2ic 14 2 S2 1 S1 3 S1 3 S1 2 S1 1 S1 1 S1 2 S1 2 S1 2 S2 63.959 S2ic 15 1 S1 2 S2 1 S1 1 S1 2 S1 2 S1 2 S1 3 S2 2 S1 2 S2 295.125 S2dhc 16 2 S2 3 S3 3 S1 1 S1 2 S1 3 S1 1 S1 3 S2 3 S1 3 S3 527.703 S3rc 17 2 S2 2 S2 3 S1 3 S1 2 S1 2 S1 1 S1 2 S1 3 S1 3 S3 469.194 S3c 18 2 S2 2 S2 1 S1 1 S1 2 S1 3 S1 2 S1 4 N1 3 S1 2 S2 132.653 N1h 19 2 S2 2 S2 3 S1 3 S1 2 S1 1 S1 2 S1 4 N1 3 S1 3 S3 441.833 N1h 20 2 S2 1 S1 3 S1 2 S1 2 S1 2 S1 1 S1 3 S2 2 S1 2 S2 123.202 S3c 21 2 S2 2 S2 3 S1 1 S1 2 S1 2 S1 2 S1 4 N1 2 S1 1 S1 335.606 N1h 22 2 S2 2 S2 1 S1 1 S1 2 S1 2 S1 2 S1 4 N1 2 S1 1 S1 140.146 N1h 23 2 S2 2 S2 3 S1 2 S1 2 S1 2 S1 3 S2 4 N1 2 S1 2 S2 516.69 N1h 24 2 S2 2 S1 3 S1 3 S1 2 S1 2 S1 2 S1 3 S2 2 S1 2 S2 243.113 S2ihc 25 1 S1 2 S1 2 S3 2 S1 2 S1 2 S1 2 S1 4 S3 2 S1 2 S2 374.304 S3rh

233

Appendixes

26 1 S1 1 S1 1 S1 7 S1 2 S1 1 S1 1 S1 1 S1 1 S1 2 S2 298.2 S2c 27 1 S1 3 S2 2 S3 3 S1 2 S1 1 S1 1 S1 1 S1 1 S1 2 S2 100.142 S3r 28 1 S1 2 S1 4 N1 2 S1 2 S1 2 S1 2 S1 4 S3 2 S1 2 S2 358.003 N1r 29 1 S1 1 S1 4 N1 1 S1 2 S1 2 S1 1 S1 4 S3 2 S1 2 S2 950.035 N1r 30 1 S1 2 S2 2 S3 1 S1 2 S1 1 S1 1 S1 3 S2 1 S1 1 S1 96.693 S3r 31 1 S1 2 S2 2 S3 7 S1 2 S1 1 S1 1 S1 2 S1 1 S1 1 S1 158.774 S3r 32 1 S1 2 S1 1 S1 1 S1 2 S1 1 S1 1 S1 4 S3 2 S1 2 S2 433.352 S3h 33 1 S1 2 S2 4 N1 8 S1 2 S1 1 S1 1 S1 4 N1 1 S1 1 S1 455.541 N1rh 34 3 S3 2 S2 3 S1 3 S1 2 S1 3 S2 1 S1 4 N1 3 S2 3 S3 463.499 N1h 35 1 S1 3 S3 4 N1 1 S1 2 S1 3 S2 1 S1 3 S2 3 S1 3 S3 62.574 N1r 36 4 N1 4 N2 6 N2 5 S1 1 S1 3 S2 2 S1 4 N1 3 S2 3 S3 218.378 N2l 37 1 S1 2 S1 2 S3 1 S1 2 S1 1 S1 2 S1 4 S3 1 S1 2 S2 260.352 S3rh 38 1 S1 2 S2 4 N1 3 S1 2 S1 1 S1 2 S1 4 S3 1 S1 2 S2 405.503 N1r 39 1 S1 2 S2 4 N1 1 S1 2 S1 1 S1 2 S1 2 S1 1 S1 2 S2 629.006 N1r 40 2 S2 2 S2 3 S1 1 S1 2 S1 1 S1 1 S1 2 S1 1 S1 2 S2 457.277 S2idc 41 2 S2 2 S2 3 S1 3 S1 2 S1 1 S1 2 S1 3 S2 1 S1 2 S2 404.387 S2idc 42 1 S1 2 S2 2 S3 1 S1 2 S1 2 S1 1 S1 2 S1 2 S1 2 S2 723.159 S3r

Soil limitation factors: i: irrigated conditions; d: soil depth; r: relative topography; t: soil texture; w: Drainage; o: organic matter; h: pHH2O; s: sum of exchangeable basic cations; c: CEC; b: base saturation. S: suitability level

234

Appendixes

Appendix 5.11: Land suitability for paddy rice using the parametric method (Square root)

Land Rt Area Land Suitability I Rt R Rt Dr Rt OM Rt BS Rt pH Rt CEC Rt unit (D/TX) (ha) index class 1 4 40 5 40 1 85 3 11 2 67 4 60 3 50 40 406.487 1.15024 N2irdt 2 3 60 3 85 1 85 3 34 1 88 3 87 3 73 52 158.594 12.06806 N2odt 3 3 60 3 85 1 85 3 36 1 92 3 89 1 100 54 788.626 15.76097 N1odt 4 3 60 3 85 2 95 3 63 1 94 4 80 3 50 68 482.547 19.75380 N1ioc 5 3 60 3 85 1 85 2 74 1 93 4 77 3 55 53 494.381 18.83885 N1cdt 6 3 60 3 85 1 85 3 15 2 83 4 70 3 56 50 58.116 3.98339 N2ocdt 7 2 85 1 95 1 85 3 31 1 89 2 95 3 79 55 516.483 15.56661 N1odt 8 1 100 2 100 2 95 3 23 1 89 3 91 3 77 69 248.169 14.70535 N1odt 9 1 100 2 100 2 95 1 100 1 100 3 90 1 100 67 162.155 61.95236 S2dt 10 2 85 3 85 1 85 3 12 2 73 4 67 3 52 47 70.778 3.25132 N2ocdt 11 2 85 2 100 2 95 1 100 1 100 3 90 1 96 85 30.875 70.99815 S2idt 12 2 85 3 85 2 95 2 91 1 100 1 98 1 98 84 166.309 65.05891 S2dt 13 2 85 3 85 2 95 1 95 1 94 2 98 2 92 91 290.246 65.37980 S2ir 14 2 85 3 85 2 95 1 100 1 100 2 97 2 95 61 63.959 48.51301 S3dt 15 1 100 1 95 2 95 2 67 2 83 3 89 2 92 93 295.125 50.60202 S2o 16 2 85 3 85 2 95 3 63 1 90 3 85 3 78 87 527.703 37.60615 S3oc 17 2 85 3 85 2 95 2 78 1 90 2 96 3 83 79 469.194 48.63902 S3odt 18 2 85 1 95 2 95 3 64 2 85 4 61 2 86 88 132.653 34.28096 S3oh 19 2 85 3 85 2 95 1 100 2 71 4 48 3 69 79 441.833 24.73940 N1hc 20 2 85 3 85 2 95 2 83 1 86 3 95 2 92 85 123.202 54.96335 S2o 21 2 85 3 85 2 95 2 91 2 78 4 49 1 100 84 335.606 31.34617 S3h 22 2 85 1 95 2 95 2 93 2 78 4 56 1 100 86 140.146 38.73995 S3h 23 2 85 3 85 2 95 2 77 3 59 4 60 2 88 97 516.69 30.69594 S3h 24 2 85 3 85 2 95 2 81 2 82 3 88 2 89 80 243.113 47.80315 S3odt 25 1 100 2 100 2 95 2 90 2 64 4 68 2 90 87 374.304 43.18156 S3bh

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26 1 100 1 95 2 95 1 100 1 100 1 99 2 92 97 298.2 85.64754 S1 27 1 100 2 100 2 95 1 100 1 100 1 99 2 91 74 100.142 68.45920 S2dt 28 1 100 4 60 2 95 2 88 2 83 4 78 2 91 89 358.003 39.72432 S3rh 29 1 100 4 60 2 95 2 88 1 85 4 70 2 92 83 950.035 36.97821 S3rh 30 1 100 2 100 2 95 1 100 1 93 3 92 1 100 84 96.693 75.73149 S1 31 1 100 2 100 2 95 1 100 1 93 2 97 1 95 82 158.774 73.98860 S2dt 32 1 100 1 95 2 95 1 100 1 85 4 78 2 89 83 433.352 58.71671 S2h 33 1 100 4 60 2 95 1 100 1 85 4 61 1 100 100 455.541 42.11021 S3rh 34 3 60 3 85 2 95 3 55 1 95 4 41 3 47 79 463.499 12.57003 N1ohc 35 1 100 4 60 2 95 3 48 1 89 3 88 3 57 81 62.574 21.79189 N1oc 36 4 40 6 40 1 85 3 45 2 64 4 51 3 37 54 218.378 3.84281 N2roc 37 1 100 2 100 2 95 1 100 2 80 4 77 2 89 80 260.352 56.64186 S2hdt 38 1 100 4 60 2 95 1 100 2 70 4 84 2 90 71 405.503 35.84694 S3rbdt 39 1 100 4 60 2 95 1 100 2 64 2 95 2 91 86 629.006 40.33990 S3rb 40 2 85 3 85 2 95 1 100 1 91 2 99 2 85 83 457.277 60.17368 S2dt 41 2 85 3 85 2 95 1 100 2 70 3 85 2 90 74 404.387 43.63403 S3bdt 42 1 100 2 100 2 95 2 84 1 93 2 97 2 87 86 723.159 67.26316 S2o

Soil limitation factors: i: irrigated conditions; d: soil depth; r: relative topography; t: soil texture; w: Drainage; o: organic matter; h: pHH2O; s: sum of exchangeable basic cations; c: CEC; b: base saturation. Rt: rating

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Appendix 5.12: Land suitability for sweet potato using the parametric method (Square root)

Land Rt Area Land Suitability I Rt R Rt Dr Rt OM Rt BS Rt pH Rt CEC Rt unit (D/TX) (ha) index class

1 4 40 5 85 1 100 3 8 2 88 4 73 3 50 40 406.487 1.75564 N2iocdt 2 3 60 3 95 1 100 3 27 1 100 3 90 3 73 52 158.594 11.36028 N2odt 3 3 60 3 95 1 100 3 29 1 100 3 85 1 100 54 788.626 14.83344 N1odt 4 3 60 3 95 2 95 3 53 1 100 4 88 3 50 68 482.547 20.72063 N1oc 5 3 60 3 95 1 100 2 65 1 100 4 87 3 55 53 494.381 22.31572 N1cdt 6 3 60 3 95 1 100 3 12 2 94 4 86 3 56 50 58.116 4.31033 N2ocdt 7 2 85 1 100 1 100 3 25 1 100 2 95 3 79 55 516.483 14.80833 N1odt 8 1 100 2 60 2 95 3 19 1 100 3 93 3 77 69 248.169 10.08329 N2o 9 1 100 2 60 2 95 1 92 1 100 3 92 1 100 67 162.155 44.03908 S3rdt 10 2 85 3 95 1 100 3 10 2 90 4 81 3 52 47 70.778 3.79302 N2ocdt 11 2 85 2 60 2 95 1 92 1 100 3 92 1 96 85 30.875 44.80799 S3r 12 2 85 3 95 2 95 2 80 1 100 1 99 1 98 84 166.309 63.25478 S2o 13 2 85 3 95 2 95 1 86 1 100 2 97 2 92 91 290.246 67.48267 S2i 14 2 85 3 95 2 95 1 94 1 100 2 97 2 95 61 63.959 49.72497 S3dt 15 1 100 1 100 2 95 2 60 2 94 3 91 2 92 93 295.125 50.03030 S2o 16 2 85 3 95 2 95 3 53 1 100 3 89 3 78 87 527.703 36.07538 S3o 17 2 85 3 95 2 95 2 68 1 100 2 96 3 83 79 469.194 47.25309 S3odt 18 2 85 1 100 2 95 3 55 2 95 4 74 2 86 88 132.653 36.04974 S3oh 19 2 85 3 95 2 95 1 95 2 89 4 53 3 69 79 441.833 31.51402 S3hc 20 2 85 3 95 2 95 2 71 1 100 3 95 2 92 85 123.202 53.59899 S2o 21 2 85 3 95 2 95 2 80 2 92 4 55 1 100 84 335.606 37.87689 S3h 22 2 85 1 100 2 95 2 83 2 92 4 67 1 100 86 140.146 48.78971 S3h 23 2 85 3 95 2 95 2 67 3 75 4 72 2 88 97 516.69 39.84116 S3o 24 2 85 3 95 2 95 2 70 2 94 3 90 2 89 80 243.113 47.58349 S3o 25 1 100 2 60 2 95 2 79 2 86 4 85 2 90 87 374.304 39.32477 S3ro

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26 1 100 1 100 2 95 1 90 1 100 1 100 2 92 97 298.2 82.86747 S1 27 1 100 2 60 2 95 1 87 1 100 1 100 2 91 74 100.142 44.76197 S3rdt 28 1 100 4 40 2 95 2 76 2 94 4 88 2 91 89 358.003 27.81951 S3ro 29 1 100 4 40 2 95 2 76 1 100 4 86 2 92 83 950.035 27.54297 S3ro 30 1 100 2 60 2 95 1 100 1 100 3 93 1 100 84 96.693 51.68853 S2r 31 1 100 2 60 2 95 1 100 1 100 2 97 1 95 82 158.774 50.83556 S2r 32 1 100 1 100 2 95 1 89 1 95 4 88 2 89 83 433.352 65.83139 S2dt 33 1 100 4 40 2 95 1 95 1 100 4 74 1 100 100 455.541 32.68884 S3r 34 3 60 3 95 2 95 3 44 1 100 4 42 3 47 79 463.499 12.49215 N1ohc 35 1 100 4 40 2 95 3 38 1 100 3 91 3 57 81 62.574 15.57809 N1roc 36 4 40 6 40 1 100 3 36 2 86 4 59 3 37 54 218.378 4.58496 N2iroc 37 1 100 2 60 2 95 1 100 2 93 4 87 2 89 80 260.352 44.38683 S3r 38 1 100 4 40 2 95 1 100 2 89 4 88 2 90 71 405.503 27.58091 S3rdt 39 1 100 4 40 2 95 1 89 2 87 2 95 2 91 86 629.006 29.58068 S3r 40 2 85 3 95 2 95 1 87 1 100 2 98 2 85 83 457.277 61.88612 S2dt 41 2 85 3 95 2 95 1 88 2 89 3 89 2 90 74 404.387 51.33545 S2dt 42 1 100 2 60 2 95 2 72 1 100 2 97 2 87 86 723.159 42.27407 S3r

Soil limitation factors: i: irrigated conditions; d: soil depth; r: relative topography; t: soil texture; w: Drainage; o: organic matter; h: pHH2O; s: sum of exchangeable basic cations; c: CEC; b: base saturation. Rt: rating.

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Appendix 5.13: Land suitability for groundnut using the parametric method (Square root)

Land Rt Area Land Suitability I Rt R Rt Dr Rt OM Rt BS Rt pH Rt CEC Rt unit (D/TX) (ha) index class 1 4 40 5 85 1 100 3 11 2 88 4 37 3 35 40 406.487 1.36943 N2iohcdt 2 3 60 3 95 1 100 3 34 1 100 3 61 3 51 52 158.594 10.32448 N2ocdt 3 3 60 3 95 1 100 3 36 1 100 3 66 1 87 54 788.626 15.13451 N1odt 4 3 60 3 95 2 85 3 66 1 100 4 40 3 35 68 482.547 10.32218 N2hc 5 3 60 3 95 1 100 2 84 1 100 4 39 3 39 53 494.381 12.26908 N2hcdt 6 3 60 3 95 1 100 3 15 2 94 4 39 3 39 50 58.116 3.02790 N2ohcdt 7 2 85 1 100 1 100 3 31 1 100 2 95 3 56 55 516.483 15.45996 N1ocdt 8 1 100 2 60 2 85 3 23 1 100 3 74 3 54 69 248.169 8.62482 N2oc 9 1 100 2 60 2 85 1 100 1 100 3 71 1 94 67 162.155 36.99059 S3rhdt 10 2 85 3 95 1 100 3 12 2 90 4 38 3 37 47 70.778 2.62976 N2ohcdt 11 2 85 2 60 2 85 1 100 1 100 3 69 1 85 85 30.875 36.00921 S3rh 12 2 85 3 95 2 85 2 93 1 100 1 98 1 86 84 166.309 61.61178 S2idt 13 2 85 3 95 2 85 1 95 1 100 2 90 2 77 91 290.246 56.26978 S2c 14 2 85 3 95 2 85 1 100 1 100 2 89 2 85 61 63.959 43.95568 S3dt 15 1 100 1 100 2 85 2 73 2 94 3 64 2 78 93 295.125 41.62973 S3h 16 2 85 3 95 2 85 3 65 1 100 3 52 3 55 87 527.703 24.02601 S3hc 17 2 85 3 95 2 85 2 86 1 100 2 86 3 59 79 469.194 37.36326 S3c 18 2 85 1 100 2 85 3 67 2 95 4 37 2 62 88 132.653 18.53348 N1h 19 2 85 3 95 2 85 1 100 2 89 4 35 3 49 79 441.833 17.01984 N1h 20 2 85 3 95 2 85 2 88 1 100 3 84 2 77 85 123.202 50.56638 S2c 21 2 85 3 95 2 85 2 92 2 92 4 35 1 89 84 335.606 23.06594 N1h 22 2 85 1 100 2 85 2 94 2 92 4 36 1 90 86 140.146 25.03512 S3h 23 2 85 3 95 2 85 2 86 3 84 4 37 2 68 97 516.69 21.15980 N1h 24 2 85 3 95 2 85 2 87 2 94 3 62 2 71 80 243.113 35.00823 S3hc 25 1 100 2 60 2 85 2 92 2 86 4 38 2 73 87 374.304 19.23680 N1h

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26 1 100 1 100 2 85 1 100 1 100 1 96 2 76 97 298.2 67.61523 S2c 27 1 100 2 60 2 85 1 100 1 100 1 96 2 76 74 100.142 40.64611 S3rcdt 28 1 100 4 40 2 85 2 91 2 94 4 44 2 76 89 358.003 18.60725 N1rhc 29 1 100 4 40 2 85 2 90 1 100 4 39 2 78 83 950.035 17.35848 N1rhc 30 1 100 2 60 2 85 1 100 1 100 3 74 1 100 84 96.693 43.61303 S3rh 31 1 100 2 60 2 85 1 100 1 100 2 89 1 85 82 158.774 43.56845 S3r 32 1 100 1 100 2 85 1 100 1 95 4 44 2 70 83 433.352 30.13783 S3h 33 1 100 4 40 2 85 1 100 1 100 4 37 1 90 100 455.541 20.46739 N1rh 34 3 60 3 95 2 85 3 55 1 100 4 34 3 33 79 463.499 8.82867 N2hc 35 1 100 4 40 2 85 3 48 1 100 3 64 3 40 81 62.574 11.63463 N2roc 36 4 40 6 40 1 100 3 45 2 86 4 35 3 26 54 218.378 2.81268 N2irhc 37 1 100 2 60 2 85 1 100 2 93 4 40 2 71 80 260.352 20.76160 N1rh 38 1 100 4 40 2 85 1 100 2 89 4 47 2 71 71 405.503 16.93447 N1rh 39 1 100 4 40 2 85 1 100 2 87 2 85 2 75 86 629.006 25.46938 S3r 40 2 85 3 95 2 85 1 100 1 100 2 92 2 60 83 457.277 43.43751 S3c 41 2 85 3 95 2 85 1 100 2 89 3 51 2 73 74 404.387 29.29700 S3h 42 1 100 2 60 2 85 2 89 1 100 2 90 2 64 86 723.159 36.72960 S3rc

Soil limitation factors: i: irrigated conditions; d: soil depth; r: relative topography; t: soil texture; w: Drainage; o: organic matter; h: pHH2O; s: sum of exchangeable basic cations; c: CEC; b: base saturation. Rt: rating.

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Appendix 5.14: Land suitability for maize using the parametric method (Square root)

Land Rt Area Land Suitability I Rt R Rt Dr Rt OM Rt BS Rt pH Rt CEC Rt unit (D/TX) (ha) index class

1 4 40 5 85 1 100 3 11 2 67 4 38 3 50 40 406.487 1.44736 N2iohdt 2 3 60 3 95 1 100 3 34 1 88 3 70 3 73 52 158.594 12.41280 N2odt 3 3 60 3 95 1 100 3 36 1 92 3 76 1 100 54 788.626 16.70081 N1odt 4 3 60 3 95 2 95 3 66 1 94 4 47 3 50 68 482.547 15.88446 N1hc 5 3 60 3 95 1 100 2 84 1 93 4 49 3 55 53 494.381 17.65363 N1hcdt 6 3 60 3 95 1 100 3 15 2 83 4 43 3 56 50 58.116 3.57998 N2ohcdt 7 2 85 1 100 1 100 3 31 1 89 2 91 3 79 55 516.483 16.95437 N1odt 8 1 100 2 60 2 95 3 23 1 89 3 86 3 77 69 248.169 11.07336 N2ro 9 1 100 2 60 2 95 1 100 1 100 3 83 1 100 67 162.155 43.61034 S3rdt 10 2 85 3 95 1 100 3 12 2 73 4 40 3 52 47 70.778 2.88068 N2ohcdt 11 2 85 2 60 2 95 1 100 1 100 3 80 1 96 85 30.875 43.56248 S3r 12 2 85 3 95 2 95 2 93 1 100 1 99 1 98 84 166.309 69.88508 S2dt 13 2 85 3 95 2 95 1 95 1 94 2 86 2 92 91 290.246 64.74886 S2i 14 2 85 3 95 2 95 1 100 1 100 2 95 2 95 61 63.959 50.75590 S2dt 15 1 100 1 100 2 95 2 73 2 83 3 75 2 92 93 295.125 51.92658 S2o 16 2 85 3 95 2 95 3 65 1 90 3 62 3 78 87 527.703 34.21445 S3h 17 2 85 3 95 2 95 2 86 1 90 2 92 3 83 79 469.194 53.19412 S2cdt 18 2 85 1 100 2 95 3 67 2 85 4 39 2 86 88 132.653 23.00770 N1h 19 2 85 3 95 2 95 1 100 2 71 4 36 3 69 79 441.833 19.61566 N1h 20 2 85 3 95 2 95 2 88 1 86 3 75 2 92 85 123.202 50.53453 S2h 21 2 85 3 95 2 95 2 92 2 78 4 36 1 100 84 335.606 24.48028 N1h 22 2 85 1 100 2 95 2 94 2 78 4 38 1 100 86 140.146 27.11536 S3h 23 2 85 3 95 2 95 2 86 3 59 4 39 2 88 97 516.69 22.48020 N1h 24 2 85 3 95 2 95 2 87 2 82 3 71 2 89 80 243.113 44.31982 S3h

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25 1 100 2 60 2 95 2 92 2 64 4 40 2 90 87 374.304 20.50511 N1h 26 1 100 1 100 2 95 1 100 1 100 1 99 2 92 97 298.2 87.87252 S1 27 1 100 2 60 2 95 1 100 1 100 1 100 2 91 74 100.142 47.98987 S3rdt 28 1 100 4 40 2 95 2 91 2 83 4 56 2 91 89 358.003 22.81872 N1rh 29 1 100 4 40 2 95 2 90 1 85 4 43 2 92 83 950.035 19.53978 N1rh 30 1 100 2 60 2 95 1 100 1 93 3 63 1 100 84 96.693 41.02650 S3rh 31 1 100 2 60 2 95 1 100 1 93 2 95 1 95 82 158.774 48.51601 S3r 32 1 100 1 100 2 95 1 100 1 85 4 56 2 89 83 433.352 43.25072 S3h 33 1 100 4 40 2 95 1 100 1 85 4 39 1 100 100 455.541 22.16490 N1rh 34 3 60 3 95 2 95 3 55 1 95 4 35 3 47 79 463.499 11.34417 N2hc 35 1 100 4 40 2 95 3 48 1 89 3 74 3 57 81 62.574 14.89475 N1ro 36 4 40 6 40 1 100 3 45 2 64 4 37 3 37 54 218.378 3.55022 N2rhc 37 1 100 2 60 2 95 1 100 2 80 4 49 2 89 80 260.352 27.92020 S3h 38 1 100 4 40 2 95 1 100 2 70 4 58 2 90 71 405.503 19.85799 N1rh 39 1 100 4 40 2 95 1 100 2 64 2 90 2 91 86 629.006 26.17598 S3r 40 2 85 3 95 2 95 1 100 1 91 2 97 2 85 83 457.277 62.96906 S2dt 41 2 85 3 95 2 95 1 100 2 70 3 60 2 90 74 404.387 35.88149 S3hdt 42 1 100 2 60 2 95 2 89 1 93 2 96 2 87 86 723.159 45.09143 S3r

Soil limitation factors: i: irrigated conditions; d: soil depth; r: relative topography; t: soil texture; w: Drainage; o: organic matter; h: pHH2O; s: sum of exchangeable basic cations; c: CEC; b: base saturation. Rt: rating.

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Appendix 5.15: Land suitability for potato using the parametric method (Square root)

Land Rt Area Land Suitability I Rt R Rt Dr Rt OM Rt BS Rt pH Rt CEC Rt unit (D/TX) (ha) index class

1 4 40 5 85 1 100 3 11 2 88 4 50 3 50 40 406.487 1.90271 N2iodt 2 3 60 3 95 1 100 3 34 1 98 3 86 3 73 52 158.594 14.51917 N1odt 3 3 60 3 95 1 100 3 36 1 98 3 87 1 100 54 788.626 18.44206 N1odt 4 3 60 3 95 2 95 3 67 1 96 4 73 3 50 68 482.547 20.79017 N1ic 5 3 60 3 95 1 100 2 84 1 97 4 68 3 55 53 494.381 22.08893 N1cdt 6 3 60 3 95 1 100 3 15 2 94 4 63 3 56 50 58.116 4.61150 N2odt 7 2 85 1 100 1 100 3 31 1 99 2 95 3 79 55 516.483 18.27029 N1odt 8 1 100 2 60 2 95 3 23 1 99 3 91 3 77 69 248.169 12.01361 N2o 9 1 100 2 60 2 95 1 100 1 92 3 89 1 100 67 162.155 43.31510 S3rdt 10 2 85 3 95 1 100 3 12 2 90 4 57 3 52 47 70.778 3.81823 N2odt 11 2 85 2 60 2 95 1 100 1 95 3 89 1 96 85 30.875 44.78416 S3r 12 2 85 3 95 2 95 2 100 1 93 1 91 1 98 84 166.309 67.00197 S2dt 13 2 85 3 95 2 95 1 100 1 94 2 100 2 92 91 290.246 71.63431 S2i 14 2 85 3 95 2 95 1 100 1 93 2 98 2 95 61 63.959 49.71406 S3dt 15 1 100 1 100 2 95 2 73 2 94 3 87 2 92 93 295.125 59.51735 S2o 16 2 85 3 95 2 95 3 65 1 100 3 80 3 78 87 527.703 41.94675 S3oc 17 2 85 3 95 2 95 2 89 1 100 2 96 3 83 79 469.194 58.26796 S2cdt 18 2 85 1 100 2 95 3 67 2 95 4 52 2 86 88 132.653 32.43130 S3h 19 2 85 3 95 2 95 1 100 2 89 4 39 3 69 79 441.833 23.79199 N1h 20 2 85 3 95 2 95 2 92 1 96 3 95 2 92 85 123.202 65.40896 S2idt 21 2 85 3 95 2 95 2 100 2 92 4 39 1 100 84 335.606 30.02833 S3h 22 2 85 1 100 2 95 2 100 2 92 4 46 1 100 86 140.146 36.76819 S3h 23 2 85 3 95 2 95 2 88 3 84 4 50 2 88 97 516.69 34.78654 S3h 24 2 85 3 95 2 95 2 91 2 94 3 86 2 89 80 243.113 56.69593 S2dt 25 1 100 2 60 2 95 2 100 2 86 4 60 2 90 87 374.304 37.17224 S3rh

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26 1 100 1 100 2 95 1 100 1 93 1 94 2 92 97 298.2 82.57354 S1 27 1 100 2 60 2 95 1 100 1 93 1 93 2 91 74 100.142 44.63058 S3rdt 28 1 100 4 40 2 95 2 100 2 94 4 75 2 91 89 358.003 29.45995 S3r 29 1 100 4 40 2 95 2 100 1 95 4 63 2 92 83 950.035 26.35644 S3r 30 1 100 2 60 2 95 1 100 1 97 3 91 1 100 84 96.693 50.35693 S2r 31 1 100 2 60 2 95 1 100 1 97 2 99 1 95 82 158.774 50.58075 S2r 32 1 100 1 100 2 95 1 100 1 95 4 75 2 89 83 433.352 50.00076 S2h 33 1 100 4 40 2 95 1 100 1 95 4 52 1 100 100 455.541 27.40219 S3rh 34 3 60 3 95 2 95 3 55 1 95 4 38 3 47 79 463.499 12.31653 N2ohc 35 1 100 4 40 2 95 3 48 1 99 3 87 3 57 81 62.574 17.03333 N1roc 36 4 40 6 40 1 100 3 45 2 86 4 40 3 37 54 218.378 4.27902 N2irhc 37 1 100 2 60 2 95 1 100 2 93 4 69 2 89 80 260.352 39.52930 S3rh 38 1 100 4 40 2 95 1 100 2 89 4 77 2 90 71 405.503 25.79958 S3rdt 39 1 100 4 40 2 95 1 100 2 87 2 95 2 91 86 629.006 31.35546 S3r 40 2 85 3 95 2 95 1 100 1 99 2 99 2 85 83 457.277 66.35228 S2dt 41 2 85 3 95 2 95 1 100 2 89 3 79 2 90 74 404.387 51.55783 S2dt 42 1 100 2 60 2 95 2 94 1 97 2 99 2 87 86 723.159 48.06061 S3r

Soil limitation factors: i: irrigated conditions; d: soil depth; r: relative topography; t: soil texture; w: Drainage; o: organic matter; h: pHH2O; s: sum of exchangeable basic cations; c: CEC; b: base saturation. Rt: rating.

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Appendix 5.16: Land suitability for sesame using the parametric method (Square root)

Land Rt Area Land Suitability I Rt R Rt Dr Rt OM Rt BS Rt pH Rt CEC Rt unit (D/TX) (ha) index class

1 4 40 5 85 1 100 3 11 2 67 4 38 3 50 40 406.487 1.44736 N2ordt 2 3 60 3 95 1 100 3 34 1 88 3 70 3 73 52 158.594 12.41280 N2odt 3 3 60 3 95 1 100 3 36 1 92 3 76 1 100 54 788.626 16.70081 N1odt 4 3 60 3 95 2 95 3 66 1 94 4 53 3 50 68 482.547 17.39793 N1hc 5 3 60 3 95 1 100 2 84 1 93 4 49 3 55 53 494.381 17.65363 N1hdt 6 3 60 3 95 1 100 3 15 2 83 4 43 3 56 50 58.116 3.57998 N2o 7 2 85 1 100 1 100 3 31 1 89 2 91 3 79 55 516.483 16.95437 N1o 8 1 100 2 60 2 95 3 23 1 89 3 86 3 77 69 248.169 11.07336 N2o 9 1 100 2 60 2 95 1 100 1 100 3 83 1 100 67 162.155 43.61034 S3rdt 10 2 85 3 95 1 100 3 12 2 73 4 40 3 52 47 70.778 2.88068 N2ocdt 11 2 85 2 60 2 95 1 100 1 100 3 80 1 96 85 30.875 43.56248 S3r 12 2 85 3 95 2 95 2 93 1 100 1 91 1 98 84 166.309 67.00197 S2idt 13 2 85 3 95 2 95 1 95 1 94 2 99 2 92 91 290.246 69.47051 S2i 14 2 85 3 95 2 95 1 100 1 100 2 95 2 95 61 63.959 50.75590 S2dt 15 1 100 1 100 2 95 2 73 2 83 3 72 2 92 93 295.125 50.52777 S2oh 16 2 85 3 95 2 95 3 65 1 90 3 62 3 78 87 527.703 34.21445 S3oc 17 2 85 3 95 2 95 2 86 1 90 2 92 3 83 79 469.194 53.19412 S2cdt 18 2 85 1 100 2 95 3 67 2 85 4 39 2 86 88 132.653 23.00770 N1h 19 2 85 3 95 2 95 1 100 2 70 4 36 3 69 79 441.833 19.47703 N1h 20 2 85 3 95 2 95 2 88 1 86 3 90 2 92 85 123.202 58.93289 S2idt 21 2 85 3 95 2 95 2 92 2 78 4 36 1 100 84 335.606 24.48028 N1h 22 2 85 1 100 2 95 2 94 2 78 4 38 1 100 86 140.146 27.11536 S3h 23 2 85 3 95 2 95 2 86 3 59 4 38 2 88 97 516.69 21.90378 N1h 24 2 85 3 95 2 95 2 87 2 82 3 71 2 89 80 243.113 44.31982 S3hdt 25 1 100 2 60 2 95 2 92 2 64 4 40 2 90 87 374.304 20.50511 N1h

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26 1 100 1 100 2 95 1 100 1 100 1 94 2 92 97 298.2 85.62477 S1 27 1 100 2 60 2 95 1 100 1 100 1 93 2 91 74 100.142 46.27976 S3rdt 28 1 100 4 40 2 95 2 91 2 83 4 56 2 91 89 358.003 22.81872 N1rh 29 1 100 4 40 2 95 2 90 1 85 4 43 2 92 83 950.035 19.53978 N1rh 30 1 100 2 60 2 95 1 100 1 93 3 86 1 100 84 96.693 47.93397 S3r 31 1 100 2 60 2 95 1 100 1 93 2 96 1 95 82 158.774 48.77069 S3r 32 1 100 1 100 2 95 1 100 1 85 4 56 2 89 83 433.352 43.25072 S3h 33 1 100 4 40 2 95 1 100 1 85 4 39 1 100 100 455.541 22.16490 N1rh 34 3 60 3 95 2 95 3 55 1 95 4 35 3 47 79 463.499 11.34417 N2hc 35 1 100 4 40 2 95 3 48 1 89 3 74 3 57 81 62.574 14.89475 N1ro 36 4 40 6 40 1 100 3 45 2 64 4 37 3 37 54 218.378 3.55022 N2rhc 37 1 100 2 60 2 95 1 100 2 80 4 49 2 89 80 260.352 27.92020 S3h 38 1 100 4 40 2 95 1 100 2 70 4 58 2 90 71 405.503 19.85799 N1rh 39 1 100 4 40 2 95 1 100 2 64 2 90 2 91 86 629.006 26.17598 S3r 40 2 85 3 95 2 95 1 100 1 91 2 98 2 85 83 457.277 63.29281 S2dt 41 2 85 3 95 2 95 1 100 2 70 3 61 2 90 74 404.387 36.47951 S3h 42 1 100 2 60 2 95 2 89 1 93 2 97 2 87 86 723.159 45.32567 S3r

Soil limitation factors: i: irrigated conditions; d: soil depth; r: relative topography; t: soil texture; w: Drainage; o: organic matter; h: pHH2O; s: sum of exchangeable basic cations; c: CEC; b: base saturation. Rt: rating.

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Appendix 5.17: Land suitability for soybean using the parametric method (Square root)

Land Rt Area Land Suitability I Rt R Rt Dr Rt OM Rt BS Rt pH Rt CEC Rt unit (D/TX) (ha) index class

1 4 40 5 85 1 100 3 11 2 88 4 38 3 35 40 406.487 1.38781 N2orcdt 2 3 60 3 95 1 100 3 34 1 100 3 86 3 51 52 158.594 12.25892 N2ocdt 3 3 60 3 95 1 100 3 36 1 100 3 87 1 87 54 788.626 17.37625 N1odt 4 3 60 3 95 2 95 3 66 1 100 4 60 3 35 68 482.547 13.36501 N1c 5 3 60 3 95 1 100 2 84 1 100 4 53 3 39 53 494.381 14.30268 N1cdt 6 3 60 3 95 1 100 3 15 2 94 4 45 3 39 50 58.116 3.25249 N2ocdt 7 2 85 1 100 1 100 3 31 1 100 2 95 3 56 55 516.483 15.45996 N1odt 8 1 100 2 60 2 95 3 23 1 100 3 91 3 54 69 248.169 10.11131 N2oc 9 1 100 2 60 2 95 1 100 1 100 3 89 1 94 67 162.155 43.78339 S3rdt 10 2 85 3 95 1 100 3 12 2 90 4 40 3 37 47 70.778 2.69807 N2hco 11 2 85 2 60 2 95 1 100 1 100 3 89 1 85 85 30.875 43.23510 S3r 12 2 85 3 95 2 95 2 93 1 100 1 98 1 86 84 166.309 65.13525 S2dt 13 2 85 3 95 2 95 1 95 1 100 2 98 2 77 91 290.246 62.07537 S2c 14 2 85 3 95 2 95 1 100 1 100 2 97 2 85 61 63.959 48.51301 S3dt 15 1 100 1 100 2 95 2 73 2 94 3 87 2 78 93 295.125 54.80207 S2oc 16 2 85 3 95 2 95 3 65 1 100 3 75 3 55 87 527.703 31.37204 S3oc 17 2 85 3 95 2 95 2 86 1 100 2 96 3 59 79 469.194 41.73337 S3c 18 2 85 1 100 2 95 3 67 2 95 4 39 2 62 88 132.653 20.65248 N1h 19 2 85 3 95 2 95 1 100 2 89 4 36 3 49 79 441.833 18.50726 N1h 20 2 85 3 95 2 95 2 88 1 100 3 95 2 77 85 123.202 56.85078 S2c 21 2 85 3 95 2 95 2 92 2 92 4 36 1 89 84 335.606 25.08176 S3h 22 2 85 1 100 2 95 2 94 2 92 4 38 1 90 86 140.146 27.93722 S3h 23 2 85 3 95 2 95 2 86 3 75 4 38 2 68 97 516.69 21.70885 N1h 24 2 85 3 95 2 95 2 87 2 94 3 86 2 71 80 243.113 46.64544 S3c

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25 1 100 2 60 2 95 2 92 2 86 4 40 2 73 87 374.304 21.40729 N1rh 26 1 100 1 100 2 95 1 100 1 100 1 99 2 76 97 298.2 72.59034 S2c 27 1 100 2 60 2 95 1 100 1 100 1 99 2 76 74 100.142 43.63684 S3rcdt 28 1 100 4 40 2 95 2 91 2 94 4 65 2 76 89 358.003 23.90919 N1rh 29 1 100 4 40 2 95 2 90 1 100 4 44 2 78 83 950.035 19.74039 N1rh 30 1 100 2 60 2 95 1 100 1 100 3 91 1 100 84 96.693 51.12972 S2r 31 1 100 2 60 2 95 1 100 1 100 2 97 1 85 82 158.774 48.08563 S3r 32 1 100 1 100 2 95 1 100 1 95 4 65 2 70 83 433.352 47.06792 S3hc 33 1 100 4 40 2 95 1 100 1 100 4 39 1 90 100 455.541 22.80750 N1rh 34 3 60 3 95 2 95 3 55 1 100 4 35 3 33 79 463.499 9.46983 N2ohc 35 1 100 4 40 2 95 3 48 1 100 3 87 3 40 81 62.574 14.34084 N1roc 36 4 40 6 40 1 100 3 45 2 86 4 37 3 26 54 218.378 2.89192 N2irohc 37 1 100 2 60 2 95 1 100 2 93 4 54 2 71 80 260.352 29.63104 S3rh 38 1 100 4 40 2 95 1 100 2 89 4 69 2 71 71 405.503 21.69200 N1r 39 1 100 4 40 2 95 1 100 2 87 2 95 2 75 86 629.006 28.46578 S3r 40 2 85 3 95 2 95 1 100 1 100 2 99 2 60 83 457.277 47.63662 S3cdt 41 2 85 3 95 2 95 1 100 2 89 3 74 2 73 74 404.387 44.63573 S3hct 42 1 100 2 60 2 95 2 89 1 100 2 97 2 64 86 723.159 40.31189 S3rc

Soil limitation factors: i: irrigated conditions; d: soil depth; r: relative topography; t: soil texture; w: Drainage; o: organic matter; h: pHH2O; s: sum of exchangeable basic cations; c: CEC; b: base saturation. Rt: rating.

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Appendix 5.18: Land suitability for green pepper using the parametric method (Square root)

Land R9 Area Land Suitability I R1 R R2 Dr R3 OM R4 BS R5 pH R6 CEC R8 unit (D/TX) (ha) index class

1 4 40 5 85 1 100 3 15 2 88 4 38 3 35 40 406.487 1.89247 N2rohcdt 2 3 60 3 100 1 100 3 48 1 100 3 66 3 51 52 158.594 15.55520 N1ocdt 3 3 60 3 100 1 100 3 51 1 100 3 70 1 87 54 788.626 22.65434 N1odt 4 3 60 3 100 2 95 3 86 1 100 4 53 3 35 68 482.547 14.71117 N1hc 5 3 60 3 100 1 100 2 90 1 100 4 49 3 39 53 494.381 14.60486 N1hc 6 3 60 3 100 1 100 3 21 2 94 4 43 3 39 50 58.116 4.56678 N2ohc 7 2 85 1 95 1 100 3 44 1 100 2 86 3 56 55 516.483 20.34926 N1ocdt 8 1 100 2 60 2 95 3 33 1 100 3 76 3 54 69 248.169 13.25805 N1oc 9 1 100 2 60 2 95 1 100 1 100 3 74 1 94 67 162.155 39.92365 S3rdt 10 2 85 3 100 1 100 3 17 2 90 4 40 3 37 47 70.778 3.92157 N2ohcdt 11 2 85 2 60 2 95 1 100 1 100 3 72 1 85 85 30.875 38.88729 S3rh 12 2 85 3 100 2 95 2 100 1 100 1 98 1 86 84 166.309 69.29674 S2dt 13 2 85 3 100 2 95 1 100 1 100 2 96 2 77 91 290.246 64.67230 S2c 14 2 85 3 100 2 95 1 100 1 100 2 95 2 85 61 63.959 49.25750 S3dt 15 1 100 1 95 2 95 2 88 2 94 3 69 2 78 93 295.125 50.77696 S2hc 16 2 85 3 100 2 95 3 86 1 100 3 61 3 55 87 527.703 33.38933 S3hc 17 2 85 3 100 2 95 2 92 1 100 2 88 3 59 79 469.194 42.40061 S3cdt 18 2 85 1 100 2 95 3 87 2 95 4 39 2 62 88 132.653 23.53393 N1hc 19 2 85 3 100 2 95 1 100 2 89 4 36 3 49 79 441.833 18.98805 N1hc 20 2 85 3 100 2 95 2 94 1 100 3 84 2 77 85 123.202 56.68590 S2c 21 2 85 3 100 2 95 2 100 2 92 4 36 1 89 84 335.606 26.82887 S3h 22 2 85 1 95 2 95 2 100 2 92 4 38 1 90 86 140.146 28.08543 S3h 23 2 85 3 100 2 95 2 89 3 75 4 38 2 68 97 516.69 22.65796 N1h 24 2 85 3 100 2 95 2 93 2 94 3 67 2 71 80 243.113 42.42537 S3hc 25 1 100 2 60 2 95 2 100 2 86 4 40 2 73 87 374.304 22.31864 N1h

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26 1 100 1 95 2 95 1 100 1 100 1 99 2 76 97 298.2 70.75232 S2c 27 1 100 2 60 2 95 1 100 1 100 1 100 2 76 74 100.142 43.85668 S3rcdt 28 1 100 4 40 2 95 2 100 2 94 4 56 2 76 89 358.003 23.26385 N1rh 29 1 100 4 40 2 95 2 100 1 100 4 43 2 78 83 950.035 20.57038 N1rh 30 1 100 2 60 2 95 1 100 1 100 3 77 1 100 84 96.693 47.03250 S3rh 31 1 100 2 60 2 95 1 100 1 100 2 95 1 85 82 158.774 47.58732 S3r 32 1 100 1 95 2 95 1 100 1 95 4 56 2 70 83 433.352 39.52406 S3hc 33 1 100 4 40 2 95 1 100 1 100 4 39 1 90 100 455.541 22.80750 N1rh 34 3 60 3 100 2 95 3 78 1 100 4 35 3 33 79 463.499 11.57034 N2hc 35 1 100 4 40 2 95 3 72 1 100 3 69 3 40 81 62.574 15.64174 N1rc 36 4 40 6 40 1 100 3 64 2 86 4 37 3 26 54 218.378 3.44882 N2rhc 37 1 100 2 60 2 95 1 100 2 93 4 49 2 71 80 260.352 26.88742 S3h 38 1 100 4 40 2 95 1 100 2 89 4 58 2 71 71 405.503 19.88791 N1r 39 1 100 4 40 2 95 1 100 2 87 2 86 2 75 86 629.006 27.08386 S3r 40 2 85 3 100 2 95 1 100 1 100 2 97 2 60 83 457.277 48.37795 S3c 41 2 85 3 100 2 95 1 100 2 89 3 61 2 73 74 404.387 38.00786 S3h 42 1 100 2 60 2 95 2 95 1 100 2 96 2 64 86 723.159 41.43332 S3rc

Soil limitation factors: i: irrigated conditions; d: soil depth; r: relative topography; t: soil texture; w: Drainage; o: organic matter; h: pHH2O; s: sum of exchangeable basic cations; c: CEC; b: base saturation. Rt: rating.

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Curriculum Vitae

1.1 Personal Profile Full name: Nguyen Huu Hao Sex: Male Date of birth: March 02sd, 1981 Place of birth: Thanh Hoa, Vietnam Marital status: Married and have two children Nationality: Vietnamese Languages: Vietnamese (mother tongue) and English Permanent home address: 26, Nguyen Thai Hoc street, Truong Thi ward, city of Thanh Hoa, Thanh Hoa province, Vietnam. Tel: (+84)373859366 Cell phone: (+84)982588488

Email: [email protected] 1.2 Objective  Improving knowledge, skills and experience in the field of Land Use Management, Land planning, soil science, Agricultural management and rural development, and Environment in order to become an expert in these fields.  Researching the relationship between environmental and social activities in agricultural produce.  Application of learned knowledge for higher education and contributing to the development of sustainable agriculture.  Cooperating with other colleagues and agricultural, environmental experts to create the best learning ground.

1.3 Education and qualification From 1998 to 2002: Studied at Ha noi University of Agriculture to get the bachelor degree of Engineer of Land management. The topic of graduated thesis: ―Application GIS (Geographic Information System) to storage data of land survey for land planning of Tri Qua commune, Thuan Thanh district, Bac Ninh Province, Vietnam‖. Supervisor: Msc. Pham Van Van - Lecturer of Faculty of natural resources and environment, Ha noi University of Agriculture.

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Rank of degree: good From 2006 to 2008 Studied at Ha noi University of Agriculture to get the Master degree of Agriculture. The topic of master thesis: ―Evaluating the real situation of agricultural and forestry land use system for land use planning toward 2015 of Tam Nong district, Phu Tho province, Vietnam‖. Supervisor: Assoc.prof Dao Chau Thu - Vice Director of agriculture and sustainable development center, Ha noi University of Agriculture. From 2012 up to now Doing as PhD candidate at Geography Institute of Greifswald University. 1.4. Work experience Lecturer at Hong Duc University - Inclusive Dates: From 06 October 2003 to Now - Position: Lecturer of Faculty of Agriculture, Forestry and Fishery, Hong Duc University. - Employer: Hong Duc University, Thanh Hoa, Vietnam. - Responsibilities: Teaching subjects which related to Land Use management, Land planning, Natural resources and environment. Assistant for Training and Consulting; Participating in composing details outline and lesson plan; Participating in scheduling time table for faculty. 1.5 Skills Agriculture field - Having good knowledge at Soil evaluation and the field of Agriculture and rural development. - Having good background at state management in the field of Agriculture and rural development. Having a variety of practical understanding to instruct students about technical processes of land management and agriculture. Having many good methods for teaching, and assisting students in understanding easily the ways to establish Land Mapping Unit, Land Use Planning, contributing to protect soil resource and environment. Soft-ware skills Experienced in using Microstation, Arcview, Arcgis, Mapinfo, and Microsoft Office.

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Eigenständigkeitserklärung

Hiermit erkläre ich, dass diese Arbeit bisher von mir weder an der Mathematisch- Naturwissenschaftlichen Fakultät der Ernst-Moritz-Arndt-Universität Greifswald noch einer anderen wissenschaftlichen Einrichtung zum Zwecke der Promotion eingereicht wurde. Ferner erkläre ich, dass ich diese Arbeit selbständig verfasst und keine anderen als die darin angegebenen Hilfsmittel und Hilfen benutzt und keine Textabschnitte eines Dritten ohne Kennzeichnung übernommen habe.

Greifswald,…2017 Unterschrift des Promovenden:

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