Assessing impacts of socioeconomic factors on farmland …preservation of Japan, especially in the suburbs of TMA in the future. • Underused UPA needs to be well controlled, which

Taikichiro Mori Memorial Research Grants 2019

Research Projects:

Assessing impacts of socio-economic factors on farmland decrease in suburbs of

metropolitan area

Research Project Leader: Ruiyi ZHANG, M2, EG Program

Affiliation: Graduate School of Media and Governance

E-mail: [email protected]

Abstract of the research

Summary:

Farming based food supply is a challenge in many metropolitan areas all over the

world as they are facing severe farmland decrease because of farmland

abandonment and farmland conversion to built-up area in the suburban area.

Previous researches showed the farmland decrease has correlation to local socio-

economic factors. And as a specific policy package which shapes the local socio-

economic context, the urban zoning is also threatening existing farmlands as its

effects would worsen the farmland preservation situation.

This research examined the extent of zoning change by using GIS data, analyzed the

impacts on the farmlands inventory change in Tokyo Metropolitan Area (TMA).

The research reveals that, in different net migration and population context, the

correlation between farmland decrease and zoning varies. Farmland decreases in a

pace of mean 25% in recent 5 years, and UPA (Urbanization Promotion Area)/UCA

(Urbanization Control Area) zoning system has unignorable correlation with

farmland decrease at ward-city-town-village municipality level as Multiple R up to

0.87 is observed for times in regression analysis between them. And the land price

didn’t show effect on farmland decrease in the centrifugal area of TMA where the

spatial distance to core of TMA shows positive correlation to farmland decrease.

Keywords:

Socio-economic context, Zoning, Farmland abandonment, Farmland conversion

i

Table of Contents

List of Tables ............................................................................................................................ ii

List of Figures .......................................................................................................................... iii

Chapter 1 - Introduction ....................................................................................................... 1

1.1 Background .................................................................................................................... 1

1.2 Research Questons and Objectives ............................................................................... 2

Chapter 2 – Approach and Results ..................................................................................... 3

2.1 Approach ........................................................................................................................ 3

2.2 Data ................................................................................................................................. 4

2.3 Results............................................................................................................................. 4

2.4 Conclutions ..................................................................................................................... 7

Chapter 3 – Research Outputs ............................................................................................. 9

3.1 Peer-reviewed publications. ......................................................................................... 9

3.2 Business trip to Tianjin ................................................................................................. 9

Chapter 4 – Future Work .................................................................................................... 10

Acknowledgement ............................................................................................................... 10

Appendix ............................................................................................................................... 11

ii

List of Tables

Table 2.1 Datasets ......................................................................................................................... 4

Table 2.2 Group comparison based on Clustering ........................................................................ 6

Table 2.3 Regression analysis in each group ................................................................................. 6

Table 5.1 Group A abandonment 2010 ....................................................................................... 11

Table 5.2 Group A abandonment 2015 ....................................................................................... 11

Table 5.3 Group A conversion 2010 ............................................................................................ 12

Table 5.4 Group A conversion 2015 ............................................................................................ 12

Table 5.5 Group C abandonment 2010 ....................................................................................... 13


Table 5.7 Group B conversion 2010 ............................................................................................ 14

Table 5.8 Group B conversion 2015 ............................................................................................ 15


Table 5.10 Group C abandonment 2015 ..................................................................................... 16

Table 5.11 Group C conversion 2010........................................................................................... 16

Table 5.12 Group C conversion 2015........................................................................................... 17

Table 5.13 Group D abandonment 2010 ..................................................................................... 17

Table 5.14 Group D abandonment 2015 ..................................................................................... 18

Table 5.15 Group D conversion 2010 .......................................................................................... 18

Table 5.16 Group D conversion 2015 .......................................................................................... 19

iii

List of Figures

Figure 1.1 Farmland classification under “The Agricultural Promotion Area Act” and “The City

Planning Act” ................................................................................................................................. 1

Figure 2.1 Research concept ......................................................................................................... 3

Figure 2.2 Research framework ..................................................................................................... 3

Figure 2.3 Group comparison based on Clustering ....................................................................... 5

Figure 2.4 Population decrease and zoning .................................................................................. 7

1

Chapter 1 - Introduction

1.1 Background

Farmland decrease is a common phenomenon in the process of modernization and

urbanization, especially area in and around a big continuous urbanized area like

Tokyo, Shanghai or Jakarta metropolitan areas. Farmland decrease is summation of

converted farmland, abandoned farmland and erosion farmland. About the farmland

decrease around us, the first we can know is its conversion to built-up area, we need

houses for dwelling, shopping center, roads and pipes for living and factory for jobs.

Besides farmland changes to forest, grassland and some other natural land use. It

also lost because of erosion caused by some disaster like flood. And in some cases,

even though it is not change to other land use, it is abandoned by farmers for some

reasons.

Figure 1.1 Farmland classification under “The Agricultural Promotion Area Act” and “The City Planning Act”

On the other hand, urban zoning system, as a specific policy package which shapes

the local socio-economic context, could have negative effect on farmland

preservation. UPA and UCA within City Planning Area is established 50 years ago to

control urban sprawl and to protect natural and agricultural area. This regulation

had positive effect during the rapid development of TMA from 1960s. However, it

could not respond to quick changes of suburbanization and recentralization period

in local history, so that part of farmland is decreasing because of this inflexibility of

urban zoning system.

2

Notes for Fig 1.1:

• Urbanization Promotion Area (UPA): including both built-up area and area

where building is encouraged in future 10 years;

• Urbanization Control Area (UCA): including both strictly conserved area and

area where building is strictly limited.

• Productive Green Land (PGL): farmland in UPA, protected with conditional

preferential treatment including lower tax rate and grace period for tax

payment;

• Agricultural Promotion Area (APA): including small rural community and

plenty of farmland out of UPA, where farming is encouraged in future

10years;

• Agricultural Land Zone (ALZ): strictly preserved farmland in the APA, so

farmland in APA is divided to ALZ and the other farmland.

1.2 Research Questons and Objectives

In Tokyo Metropolitan Area, farmland converted to built-up area can be more than

that to abandoned cultivated land. On the other hand, population of some

municipalities in TMA, especially in suburb and peri-urban area, began decreasing

for years and some others are facing their peaks, while, according to the government

reports the trend of farmland decrease speaks no difference. So, what is the specialty

of the suburbs of TMA? In urban zoning’s perspective, there is the frontier of extent

of UPA/UCA and APA/ALZ in the suburbs. The research question is: to what extent

the urban zoning has effect on farmland decrease in the suburbs of TMA.

The conflict between urban management and agricultural promotion act cannot be

ignored in this new era of new urban life style. The possibility of urban planning

regulation affecting the two patterns, abandonment and conversion, of farmland

decrease and how it does is what motivates me to research on this topic. It is to find

new ways to protect suburban farmland from zoning perspective. This way has

positive effects; if not avert. Therefore, the objective of this research is: to analysis

the UPA/UCA zoning impacts on farmland decrease and how the effects could be

minimized.

In this research, process is divided into 3 parts: (1) farmland decrease analysis, (2)

comparation between two patterns of farmland decrease and (3) GIS spatial data

analysis based on zoning. And the research hypothesis is: current UPA/UCA zoning

has negative effect on farmland preservation in the suburbs of TMA.

3

Chapter 2 – Approach and Results

2.1 Approach

Figure 2.1 Research concept

In Fig 2.1, the first question is: how to connect zoning with farmland decrease as the

research methodology? Because urban zoning doesn’t exactly represent the real

situation of urbanization which could explain the landscape of agricultural land use.

In our research, I applied Densely Inhabited District (DID) to spatially connect

zoning and farmland decrease. For example: Densely Inhabited District (DID) is an

important index to explain the level of urbanization, which means in a specific area

basically every urban unit has a population density above 4000 people/km2.

Figure 2.2 Research framework

4

The second question is: Does heterogeneity of research objects exist? We assume

yes. The relation between zoning and farmland decrease may be different in

different objects.

I applied some other urban facts to minimize the impact of heterogeneity on the

results. For example: Net migration represents to Urbanization needs in the future,

which leads to Possible farmland decrease. And Population density represents to

Current urbanization level, which shows degree of farmland inventory to some

extent.

So, the research framework contains 3 object classes and 4 research levels. 3 object

classes are: zoning, urbanization facts and farmland decrease facts respectively.

And 4 research levels are: facts, variables, results and analysis. (Fig 2.2)

2.2 Data

Table 2.1 is the datasets I used. Table 2.1 Datasets

2.3 Results

5

As current net migration is assumed to show the need for urbanization which

contributes to farmland decrease way forward and population density is assumed

to show current urbanization level which corresponds to farmland inventory, this

research does clustering to all samples to void disequilibrium in development of

those municipalities. The mean population density of all 346 municipalities is 3870

people/km2 and the benchmark of DID is 4000 people/km2, so I set the parameters

of population density as above or below 4000 people/km2. And set parameters of

net migration rate as above or below 0. Then 346 samples can be divided into A, B,

C, D, 4 groups.

The goal is to compare with pattern of farmland decrease by using degrees of

urbanization or urban development in the range of TMA:

• Net migration --> Urbanization needs in the future (increase or decrease)

• Population density --> Urbanization level (low or high)

The sample grouping is as shown figure below. Then analysis group by group. (Fig

2.3)

Figure 2.3 Group comparison based on Clustering

As the statistic by groups finds (Table 2.2):

1. In group A, C and D, mean farmland decrease rate is higher than group mean

farmland decrease, which means the municipalities with more farmland

inventory have lower farmland decrease rate.

2. Farmland conversion rate increases as getting close to core of TMA, farmland

abandonment rate increases as getting far away from core of TMA.

6

3. And the farmland decrease rate (abandonment + conversion) remains

relatively constant in 4 groups as about 25%, which indicated that farmland

decrease trends is identical despite of the distance to the core.

Table 2.2 Group comparison based on Clustering

G

r

o

u

p

Am

oun

t

Urbani

zation

level

Urbani

zation

needs

Mean farmland

conversion

rate/Group

farmland

conversion rate

Mean farmland

abandonment

rate/Group

farmland

abandonment rate

Farmlan

d in total

(km2)

A 103 High High 16%/12% 14%/12% 176.8

B 11 High Low 12%/13% 15%/16% 21.6

C 109 Low High 8%/8% 23%/16% 2280.1

D 123 Low Low 7%/6% 25%/19% 2381.1

As the regression analysis in each group finds (Table 2.3, details in appendix):

1. Amount of ALZ in UCA shows a strong positive correlation with both

farmland abandonment and conversion with inner 3 group.

2. Group B shows correlation between official land price and farmland

conversion, but the number of samples is limited.

3. Only outer ring group C and D show a correlation between distance to core

of TMA and farmland decrease.

4. The assumption of possible negative effect of DID in UCA and underused UPA

on farmland abandonment is confirmed in Group A and C, where the net

migration is above 0, in other words, there is urbanization needs in the future.

But that on farmland conversion, it just confirmed in Group A.

Table 2.3 Regression analysis in each group

7

The current zoning of City Planning Area is established 50 years ago to control urban

sprawl and to protect natural and agricultural area. This regulation had positive

effect during the rapid development of TMA from 1960s. However, it might not

respond to quick changes of suburbanization, recentralization and even

depopulation period in local history.

The DID prediction shows the needs of urbanization to land will get lower and the

press of farmland decrease would be lower in the future. Land use policy making

and implementation of new-town and suburban residential area could be crucial for

the farmland preservation in the suburbs of TMA. Around 6200ha DID of 2020 will

no longer be DID in the year of 2050. And as shown in figure 5.30, it can be calculated

out that 4100ha of those DID is now inside the UPA of 2018. So, it indicates that Well

decided zoning will adaptive to population change context then contribute to

farmland preservation. (Fig 2.4)

Figure 2.4 Population decrease and zoning

2.4 Conclutions

The main objective of this research is to analysis the UPA/UCA zoning impacts on

farmland decrease and how the effects could be minimized. So many factors from

socio-economic sector lies behind farmland abandonment and farmland conversion

to built-up area. This research just concentrated on the possible impacts from

perspective of urban planning.

• Farmland decreases in TMA in a pace of mean 25% for 2010-2015 period,

and identical in both area where near to the core and area where far away to

the core.

8

• UPA/UCA zoning system has unignorable correlation with farmland decrease

at municipality level (ward-city-town-village) as Multiple R up to 0.87 is

usually observed in regression analysis between zoning and farmland

decrease.

• Farmland abandonment could be prevented to some extent by zoning

optimization.

Recommendations: It advocates that an adaptive UPA/UCA zoning policy making

strategy for new population change scenario could be better for farmland

preservation of Japan, especially in the suburbs of TMA in the future.

• Underused UPA needs to be well controlled, which means to meet the balance

between urbanization needs and small UPA as possible.

• The meaning of DID in the UCA needs to be minimized as it is not positive to

farmland preservation no matter the area turns to UPA or depopulates to

non-inhabited area.

• The establishment of UCA needs to be keep away from ALZ as possible,

otherwise regulation of UCA needs to be reviewed and strengthened.

• The impact of distance to core of TMA only shows out of 30km radius area,

and it is possible that the distance to DID or station shows impact in the core

area of TMA.

• More factors are needed to understand the difference between those groups,

and the impacts in the future.

Limitations:

• The farmland abandonment statistic data from the Census of Agriculture and

Forestry is only data of farmland above 500m2 per farmer household, and

the same as the total amount used in this research. Farmland below 500m2

per farmer household is not calculated in this research.

• Socio-economic factors filtering in abandonment sector and urbanization

sector are not all available in 346 samples, there are missing values of some

small municipalities. So, the correlation of land price of each municipality

might not indicate the exact situation.

• And according to my study, the way forward research steps including GWR

analysis need perfect data frame without any missing value, Tentatively,

some Multiple Imputation method was used to make them useful, but that

would raise some other limitations.

9

Chapter 3 – Research Outputs

3.1 Peer-reviewed publications.

1. UNDERSTANDING THE BARRIERS RESTRAINING EFFECTIVE OPERATION

OF FLOOD EARLY WARNING SYSTEMS by Vibhas Sukhwani, Bismark Adu

Gyamfi, Ruiyi Zhang, Anwaar Mohammed AlHinai and Rajib Shaw – is

accepted and published by International Journal of Disaster Risk

Management • (IJDRM) • Vol. 1, No. 2

2. Impacts of Population Decrease on Farmland Decrease: a study in Suburbs of

TMA – originated from this research is planned to submit to 日本都市計画論

文集

3.2 Business trip to Tianjin

I had a business trip to Tianjin in October, 2019 for 3 things, which related to my

urban research:

1. Consult former professor and senior in Tianjin University about impacts of

urban planning policy on farmland decrease in China.

2. Participate to Annual National Urban Planning Professional Certification Test

of China in Tianjin. For strengthening my professional foundation as

researcher of urban planning.

3. Receive an official document of an inter-university workshop in the summer

of 2019 in which our lab in Keio University took a leading role.

10

Chapter 4 – Future Work

Population of TMA is believed to be decreasing and DID will shrink in future decades.

Nonetheless, some suburbs of TMA are continuously attracting population due to

excess concentration of population towards TMA. If these trends continue, the study

advocates that, a more conservative and adaptive UPA/UCA zoning system is needed

to contribute to farmland preservation in the suburbs of TMA by the year of 2050.

As future research topic I am considering investigating the impact of population

decrease on farmland decrease in the suburbs of metropolitan area, because this

topic is new for rapid developing countries like China. Besides, I am thinking to

continue research on the urbanization comparison between big cities in Japan and

China.

Acknowledgement

I want to convey my sincere gratitude to Mori Fund Steering Committee for selecting

me as one of Mori Grants 2019 recipients and providing this unevaluable

opportunity to conduct my research.

11

Appendix

Group A:

Farmland Abandonment Amount ~ Amount (UPA-DID) + Amount (UCA∩DID) +

Amount (UCA∩ALZ) has high significance both in year of 2010 and 2015, and all

positive correlation to the response variable.

Table 4.1 Group A abandonment 2010

Regression Statistics

Multiple R 0.867756

R Square 0.753

Adjusted R Square 0.745515

Standard Error 1931.806

Observations 103

ANOVA

df SS MS F Significance F

Regression 3 1.13E+09 3.75E+08 100.6031 6.04E-30

Residual 99 3.69E+08 3731875

Total 102 1.5E+09

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept -206.305 262.4711 -0.78601 0.43374 -727.104 314.4951

UPA-DID 4.041577 0.922372 4.381721 2.93E-05 2.21139 5.871763

UCA∩DID 3.913753 0.964868 4.056259 9.96E-05 1.999246 5.82826

UCA∩ALZ 8.3259 0.627446 13.2695 1.07E-23 7.08091 9.57089

Table 4.2 Group A abandonment 2015


Multiple R 0.863768

R Square 0.746095



Observations 103

ANOVA


Regression 3 1.37E+09 4.57E+08 96.97008 2.36E-29

Residual 99 4.67E+08 4717356

12

Total 102 1.84E+09


Intercept 150.5313 268.6283 0.56037 0.576493 -382.485 683.548

UPA-DID 2.572655 0.95582 2.691569 0.00835 0.676101 4.469209

UCA∩DID 5.855722 1.243904 4.707536 8.16E-06 3.387547 8.323897

UCA∩ALZ 9.05458 0.744893 12.15555 2.39E-21 7.576551 10.53261

Farmland Conversion Amount ~ Amount (UPA-DID) + Amount (UCA∩DID) + Amount

(UCA∩ALZ) has also be tested as high significance, and all positive correlation to

the response variable.

Table 4.3 Group A conversion 2010


Multiple R 0.705713

R Square 0.498031



Observations 103

ANOVA


Regression 3 2.38E+08 79322351 32.74111 8.7E-15

Residual 99 2.4E+08 2422714

Total 102 4.78E+08


Intercept -279.88 211.4798 -1.32344 0.188739 -699.502 139.7419

UPA-DID -3.62776 0.743179 -4.88141 4.04E-06 -5.10239 -2.15313

UCA∩DID -2.57604 0.777419 -3.31357 0.001287 -4.1186 -1.03347

UCA∩ALZ -2.8232 0.50555 -5.5844 2.06E-07 -3.82632 -1.82008

Table 4.4 Group A conversion 2015


Multiple R 0.686357

R Square 0.471086



Observations 103

ANOVA

13


Regression 3 3.69E+08 1.23E+08 29.39205 1.13E-13

Residual 99 4.14E+08 4186848

Total 102 7.84E+08


Intercept -852.657 253.0731 -3.36921 0.001075 -1354.81 -350.505

UPA-DID -1.93016 0.900472 -2.1435 0.034523 -3.71689 -0.14343

UCA∩DID -3.99071 1.171874 -3.4054 0.000956 -6.31596 -1.66545

UCA∩ALZ -4.12045 0.701759 -5.8716 5.78E-08 -5.51289 -2.72801

However, Distance to core and Official land price didn’t show regression significance.

Group B:

Farmland Abandonment Amount ~ Amount (UPA-DID) + Amount (UCA∩ALZ) + Official

land price has high significance both in year of 2010 and 2015. Amount (UCA∩ALZ)

has positive, while Amount (UPA-DID) and Official land price have negative

correlation to farmland abandonment amount.

Table 4.5 Group C abandonment 2010


Multiple R 0.872705

R Square 0.761614



Observations 11

ANOVA


Regression 3 1.47E+08 49050607 7.454694 0.013894

Residual 7 46058797 6579828

Total 10 1.93E+08


Intercept 11861.98 4091.876 2.89891 0.023022 2186.231 21537.73

UPA-DID -5.43178 3.385425 -1.60446 0.152648 -13.437 2.573481

UCA∩ALZ 19.26374 5.175343 3.722214 0.007434 7.025994 31.50148

Official land price -0.06657 0.021839 -3.04809 0.018633 -0.11821 -0.01493



14

Multiple R 0.86379

R Square 0.746132



Observations 11

ANOVA


Regression 3 1.7E+08 56664983 6.857813 0.017188

Residual 7 57839854 8262836

Total 10 2.28E+08


Intercept 11201.89 4287.503 2.612683 0.034777 1063.551 21340.22

UPA-DID -5.96225 3.307266 -1.80277 0.114419 -13.7827 1.858188

UCA∩ALZ 19.8475 5.338762 3.717623 0.007479 7.223338 32.47167

Official land price -0.06012 0.021709 -2.76928 0.027723 -0.11145 -0.00878

Farmland Conversion Amount ~ Amount (UCA∩DID) + Amount (UCA∩ALZ) + Official

land price has high significance both in year of 2010 and 2015. Amount (UCA∩ALZ)

and Official land price have positive, while Amount (UCA∩DID) has negative

correlation to farmland conversion amount.

Table 4.7 Group B conversion 2010


Multiple R 0.84179

R Square 0.70861



Observations 11

ANOVA


Regression 3 40691228 13563743 5.674265 0.02733

Residual 7 16732776 2390397

Total 10 57424004


Intercept 6065.626 2766.197 2.192767 0.064415 -475.391 12606.64

UCA∩DID 17.02723 7.646777 2.22672 0.061267 -1.05452 35.10899

UCA∩ALZ -15.0407 3.789832 -3.96869 0.005401 -24.0022 -6.07915

Official land price -0.03881 0.018005 -2.15559 0.068049 -0.08139 0.003764

15

Table 4.8 Group B conversion 2015


Multiple R 0.861031

R Square 0.741374



Observations 11

ANOVA


Regression 2 21341478 10670739 11.46638 0.004474

Residual 8 7444889 930611.2

Total 10 28786368


Intercept 663.0034 1280.005 0.517969 0.618496 -2288.69 3614.701

UCA∩ALZ -7.80217 1.661462 -4.69597 0.00155 -11.6335 -3.97084

Official land price -0.00931 0.0066 -1.41009 0.196186 -0.02453 0.005913

Group C:

Farmland Abandonment Amount ~ Amount (UPA-DID) + Amount (UCA∩DID) +

Amount (UCA∩ALZ) + Distance to core has high significance both in year of 2010

and 2015, and all positive correlation to the response variable.



Multiple R 0.677515

R Square 0.459027



Observations 109

ANOVA


Regression 4 4.21E+10 1.05E+10 22.06152 3.32E-13

Residual 104 4.96E+10 4.77E+08

Total 108 9.17E+10

16


Intercept -2627.68 6238.422 -0.42121 0.674472 -14998.7 9743.344

UPA-DID 8.907789 2.844493 3.131591 0.002258 3.267053 14.54853

UCA∩DID 29.64522 12.22871 2.424231 0.017066 5.395226 53.89522

UCA∩ALZ 3.606092 0.885774 4.07112 9.14E-05 1.849569 5.362615

Distance 213.0421 85.87323 2.480891 0.014709 42.75226 383.332



Multiple R 0.724025

R Square 0.524213



Observations 109

ANOVA


Regression 4 5.97E+10 1.49E+10 28.64625 4.75E-16

Residual 104 5.42E+10 5.21E+08

Total 108 1.14E+11


Intercept -2579.86 6460.864 -0.39931 0.690487 -15392 10232.28

UPA-DID 10.157 3.040909 3.34012 0.001164 4.126764 16.18724

UCA∩DID 28.04756 12.79144 2.192682 0.030559 2.681649 53.41346

UCA∩ALZ 4.345738 0.9194 4.726713 7.19E-06 2.522534 6.168942

Distance 203.7587 90.45775 2.252529 0.026389 24.37759 383.1398

Farmland Conversion Amount ~ Amount (UCA∩DID) + Amount (UCA∩ALZ) +

Distance to core has high significance both in year of 2010 and 2015, and all positive

correlation to the response variable.

Table 4.11 Group C conversion 2010


Multiple R 0.573086

R Square 0.328428



Observations 109

ANOVA

17


Regression 2 1.99E+10 9.93E+09 25.91927 6.85E-10

Residual 106 4.06E+10 3.83E+08

Total 108 6.04E+10


Intercept 9475.164 5336.933 1.775395 0.078703 -1105.83 20056.15

UCA∩ALZ -4.30733 0.659108 -6.53509 2.28E-09 -5.61407 -3.00058

Distance -119.344 75.83491 -1.57374 0.118529 -269.694 31.00601

Table 4.12 Group C conversion 2015


Multiple R 0.696557

R Square 0.485192



Observations 109

ANOVA


Regression 3 1.92E+10 6.39E+09 32.98655 4.21E-15

Residual 105 2.03E+10 1.94E+08

Total 108 3.95E+10


Intercept 8145.858 3939.836 2.067563 0.04114 333.8907 15957.83

UCA∩DID -25.9159 7.773382 -3.33393 0.001184 -41.3291 -10.5027

UCA∩ALZ -3.31083 0.459307 -7.20832 9.04E-11 -4.22155 -2.40011

Distance -176.852 54.87416 -3.22286 0.001691 -285.657 -68.0464

Group D:

Farmland Abandonment Amount ~ Amount (UPA-DID) + Distance to core only has very

weak significance both in year of 2010 and 2015.

Table 4.13 Group D abandonment 2010


Multiple R 0.345373

R Square 0.119283



18

Observations 123

ANOVA


Regression 2 1.34E+10 6.7E+09 8.126288 0.00049

Residual 120 9.9E+10 8.25E+08

Total 122 1.12E+11


Intercept 4693.571 8557.225 0.548492 0.584373 -12249.1 21636.28

Distance 290.6458 89.06834 3.263177 0.001435 114.2967 466.9949

UPA-DID 20.49076 6.886532 2.975483 0.003538 6.855906 34.12561

Table 4.14 Group D abandonment 2015


Multiple R 0.368831

R Square 0.136036



Observations 123

ANOVA


Regression 2 1.81E+10 9.03E+09 9.447361 0.000155

Residual 120 1.15E+11 9.55E+08

Total 122 1.33E+11


Intercept 3010.362 9234.089 0.326005 0.744988 -15272.5 21293.22

Distance 325.586 95.98904 3.391908 0.000941 135.5344 515.6376

UPA-DID 23.26796 6.918002 3.363393 0.001034 9.570799 36.96512

Farmland Conversion Amount ~ Amount (UPA-DID) + Amount (UCA∩ALZ) + Distance

to core has high significance in year of 2010 but not in year of 2015. Amount (UCA

∩ALZ), Distance to core have positive, while Amount (UPA-DID) has negative

correlation to farmland conversion amount.

Table 4.15 Group D conversion 2010


Multiple R 0.669029

R Square 0.4476

19



Observations 123

ANOVA


Regression 3 1.04E+10 3.47E+09 32.1412 2.73E-15

Residual 119 1.28E+10 1.08E+08

Total 122 2.32E+10


Intercept 4307.652 3140.225 1.371765 0.172717 -1910.31 10525.61

UCA∩ALZ -5.88249 0.711477 -8.268 2.22E-13 -7.29128 -4.47369

Distance -71.0584 32.49805 -2.18654 0.030733 -135.408 -6.70904

UPA-DID 6.461522 3.443303 1.876548 0.06303 -0.35656 13.27961

Table 4.16 Group D conversion 2015


Multiple R 0.197106

R Square 0.038851



Observations 123

ANOVA


Regression 3 6.62E+08 2.21E+08 1.603369 0.192253

Residual 119 1.64E+10 1.38E+08

Total 122 1.7E+10


Intercept -6173.78 3540.817 -1.7436 0.083811 -13184.9 837.3923

UCA∩ALZ -0.78618 0.760239 -1.03412 0.303175 -2.29153 0.719167

Distance -56.7871 36.59566 -1.55175 0.123379 -129.25 15.67592

UPA-DID -1.56169 3.564775 -0.43809 0.662115 -8.6203 5.496918

Documents

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