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Inclusive Growth
TH E CO RDIL L ER A CORRIDO R TEA T RADE TRE A TY
The Williamson Tea Farm, Kenya
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This instrument is dedicated to my grandfather Dennis Masa’ao Molintas—and his only son Dennis Sabaoan
Molintas. Lolo Tatang was an agriculturist who introduced langka, cocoa, star apple, avocado and the
Zamboanga coffee variety to the locality. A school master up until the war changed the course of his life, where the
fame of leadership acts of courage in the final battles in the capture of Japanese General Tomoyuki Yamashita
made him Statesman. For daddy, whose genius in mathematics and interest in science barely appreciated,
executed fall from grace in a sudden tragic voyage to the ancestry.
I thank my family for such lifelong inspiration and thoughtful of my intellectual pursuits: Tchaika, Yanni and
Tamiya; Sheryl, Risa, Sari, Isis and Glana; Rohan, Danilo and Bob; JP, Jesus and Lennin; Zeal, Gabriela,
Gillian and Riva; Levy, Ryu, Zander and Ragnar.
I am humbled to learn the acumen by the finest mentors and absolutely fortunate to receive the confidence and
closeness of trusted good friends.
Dominique Trual Molintas
A spoonful of rooibos at the Malaya Tea Room in Almeda, California /The National Geographic
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Unless specified otherwise, the thoughts and abstractions in this manuscript are
expressions of the author and do not represent institutions or organisations, insofar the
limitation by methodology/2023.
One Perfect Cup /The National Geographic
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The Advocator,
George Chua Cham
PRC Belt and Road Initiative Chairman,
Federation of Filipino Chinese
Chambers of Commerce and Industry, Inc
De partment of Agriculture
H.E. Marcos Junior, Ferdinand
Glen Panganiban, Cameron Odsey, Abigail Amestoso and Ellen Camut
OSEC with BPI, HVCD, F2C2 & CAR RO
De partment of Environment and Natural Resources
National Mapping and Resource Information Authority
Photogrammetry Division, Mapping and Geodesy Branch
Peter Tiangco & Janette Javier
With Ruel Belen, Nicandro Parayno, Jessie Racimo
De partment of Science and Technology
Philippine Atmospheric, Geophysical
& Astronomical Services Administration
Climate and Agrometeorological Data Section
Climate Data Reference A-052022-069 Approved 11/06/ 2022
Vicente Malano, Rosalina De Guzman, Christian Mark Ison
Be n guet State University
Romeo Gomez Jr, Constantino Sudaypan, Leonard Apilis
Christine Abellon and Andres Basalong
The Trust Clu tch,
My Little Women
Miguel Ben Gomez
Erlinda Bestre
Danilo Haduca
Macaria Tolentino
Anthony Byrne
Margarita Hora
Romeo Densen
Nancy Molitas
Marlene Lee
Satilyn Dulay
Anna Tinnies
Milton Nunez
Pedro Perez
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Content
CONTENT ........................................................................................................................... 4
ABSTRACT .......................................................................................................................... 6
CHAPTER 1/ INTRODUCTION ................................................................................................. 7
THE BARIO FARMER ....................................................................................................................................................9
Chart 1/ Vegetable Production .......................................................................................................................9
Chart 2/ Population growth rate ................................................................................................................. 10
Chart 3/ DTI Indicators Of Economic Competiveness .............................................................................. 10
Chart 4/ DTI Indicators Of Equity ................................................................................................................ 11
Chart 5/ Location Map of Barrios ................................................................................................................ 11
CHAPTER 2/ M ETHODOLO GY ............................................................................................... 12
Chart 6/ The Impact Path way Conceptual Framework ........................................................................... 14
Chart 7/ The Impact Path way Logical Framework of ve rifiable indicators.......................................... 15
CHAPTER 3/ GCC EQUATION ................................................................................................ 16
CHAPTER 4/ ELEMENT YIELD Y0 ............................................................................................ 17
YIELD-HECTARES Y................................................................................................................................................... 18
Chart 8/ Sto chastic abstraction yi eld-hectares ........................................................................................ 18
Chart 9/ Sto chastic abstraction Yield cross–check ................................................................................... 18
Chart 10/ Cross-sectional data se t of histo rical yield .............................................................................. 19
YIELD SUBSTRATE Y1
................................................................................................................................................. 20
Chart 11/ Stochastic abs traction Y1 Yi eld-subst rate ................................................................................ 21
Chart 12/ Stochastic abs traction yield -weed-slump ................................................................................ 21
Chart 13/ Cross-sectional data set............................................................................................................. 22
YIELD WEED-SLUMP Y2 ............................................................................................................................................ 23
Chart 14/ Cross-sectional data se t of tea weeds ...................................................................................... 24
YIELD ENVELOPE Y3.................................................................................................................................................. 25
Chart 15/ Cross-sectional climat e data set ............................................................................................... 34
Chart 16/ Stochastic abs traction yield -envelope Y3 temperature-max ................................................ 35
Chart 17/ Stochastic abs traction Yield-enve lope Y3 minimum temperature....................................... 35
Chart 18/ Stochastic abs traction yield-envelope Y3 relative humidity ................................................. 35
Chart 19/ Stochastic abs traction yield -envelope Y3 rainfall ................................................................... 35
Chart 20/ Summary of forecast Yield-envelope Y3 in monthly breakdown ....................................... 36
BY RULE OF THUMB CROSS-CHECK Y0 ...................................................................................................................... 37
CHAPTER 5/ ELEMENT Ζ LAND U TILIZATION ........................................................................... 39
Chart 21 Orthoimage of elevation contours of Barrio Gadang , Kapangan ......................................... 40
Chart 22/ Orthoimage of elevation contours of Barrio Pongayan, Kapangan .................................... 41
Chart 23/ Orthoimage of elevation contours of Barrio Sagubo, Kapangan ........................................ 42
Chart 24/ Orthoimage of elevation contours of Barrio Karao, Bokod .................................................. 43
Chart 25/ Orthoimage of elevation contours of Barrio Nawal, Bokod ................................................. 44
Chart 26/ Orthoimage of elevation contours of Barrio Pito, Bokod...................................................... 45
EMPLOYMENT IN AGRICULTURE ................................................................................................................................ 46
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Chart 27/ Cross-sectional data set on Labour in Agriculture ............................................................... 47
Chart 28/ Cross-sectional data set on worker per hectares ................................................................. 48
Chart 29/ Stochastic Abs traction, pe rcentage of Labour in Agriculture .............................................. 49
Chart 30/ Stochastic Abs traction, number of fa rmer per hectares ....................................................... 49
Chart 31/ Forecast Labour Group by Barrio .............................................................................................. 49
LAND UTILISATION FORECAST 2030 -2075 .............................................................................................................. 51
CHAPTER 6/ ELEMENT Χ COST OF FARM PRODUCE ................................................................. 52
Chart 32/ Cross-sectional data set 25 years auction price .................................................................... 52
Chart 33/ Stochastic abs traction of cost of farm produce χ................................................................. 53
Chart 34/ Int ersection with mean cost of farm p roduce χ.................................................................... 53
Chart 35/ T rending world tea retail p rices per kilogra m USD ................................................................ 54
CHAPTER 7/ ANTICIPATED WORLD ACCESSION ....................................................................... 55
Chart 36/ Illust ration of trade blo cks ......................................................................................................... 56
Chart 37/ Anticipated accession of the Philippines, reference point .................................................. 57
SMALLHOLDER FARMERS’ POTENTIAL EARNINGS........................................................................................................ 58
Chart 38/ Cross-sectional data set Profit to Revenue Ratio ................................................................. 59
Chart 39/ Smallholde r farmer earnings at Mean profit to revenue ra tio 69 percent ...................... 60
Chart 40/ Forecast Smallholder farmer earnings PRR 69% .................................................................. 60
Chart 41/ Forecast Smallholder farmer earnings ................................................................................... 61
INVESTMENT HURDLE RATE ...................................................................................................................................... 62
Chart 42/ Stochastic abs traction of investment cost of SWIP/ S FR ...................................................... 63
Chart 43/ Stochastic abs traction of United Nations Com Trade Tea Export Value ............................. 63
Chart 44/ Cross-sectional Data s et PRR of Global Tea Exporters .......................................................... 64
Chart 45/ Stochastic abs traction on PRR of Tea Exporters/Blenders .................................................. 65
Chart 46/Adaptation of tea watering requirement to Yield-envelope ................................................. 66
Chart 47/ Hurdle Rate across Six Barrios for SW IP .................................................................................. 67
MULTIPLIER EFFECT OF FOREGN TRADE..................................................................................................................... 69
Chart 48/ Illust ration of Mul tiplier Effect .................................................................................................. 69
OPPORTUNITY LOSS ................................................................................................................................................. 70
Chart49 Illust ration of Opportunity Loss .................................................................................................... 70
GOVERNMENT COUNTERPART .................................................................................................................................. 72
CHAPTER 8/ EQUITY & INCLUSION........................................................................................ 73
EARNINGS AND EDUCATION...................................................................................................................................... 74
Chart 50/ Cross-sectional Data s et of NO Education, decrease in worker output .............................. 74
Chart 51/ Stochastic abs traction of NO Education-output drop per worker ....................................... 75
Chart 52/ Stochastic abs traction of NO Education-Ave return/ year ................................................... 75
Chart 53/ The Mincerian equation .............................................................................................................. 76
INFRASTRUCTURE INVESTMENTS SOCIAL RATE OF RETURN ......................................................................................... 77
Chart 54/ Cross sectional Data set of social equity returns by Infrastructure Inves tment ............... 77
Chart 55/ Stochastic abs traction Social equity returns by Infrastructure Investment....................... 78
Chart 56/ Lost Labour and Mobility Curve ................................................................................................. 79
Chart 57/ The Water & Sanitation Ben efits Curve ................................................................................... 79
CHAPTER 10/ CONCLUSION ................................................................................................. 80
REFERENCES ..................................................................................................................... 91
SPECIAL CHAP TER/ SOCIAL ACCEPTANCE ............................................................................... 97
THE ZERO-SUM RUBRIC ............................................................................................................................................ 99
Chart 58/ Survey Results............................................................................................................................. 101
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Abstract
This manuscript posits the GCC equation to outline the parameters of the Cordillera
Corridor, Tea Trade Treaty. A tea corridor is the strategy for resource use of rugged
mountain terrain in the Cordillera, Philippines; and the GCC equation su bstantiates the
rudiments for cross-border competence. GCC would mean the “Gateway to Cross-border
Competence” defined in a mathematical construct. The equation has specific relevance in
the conjectures of tea cultivation, for the empowerment of Accession.
GCC EQUATION = χ Y0
The equation is formulated by meta-analysis of cross-sectional case studies synthesized by
stochastic abstraction to obtain the tangible limit of three rudiments: yield constant
ratio— Y0; land utilization— and purchase value constant ratio—χ.
Yield constant ratio Y0; is derived from yield-hectares Y, yield-substrate Y1, yield-weed-
slump Y2 and yield-weed-envelope Y3. Land utilization is a dependent variable defined by
the labour in agriculture divided by the mean persons per hectares. in all instances is less than
the total area to com prise the elevations ideal for tea growth, as d etermined by NAMRIA. Lastly, Χ
is the purchase-value constant ratio determined through stochastic abstraction of 25
years tea auctions published by the London Tea Brokers of tea farmers in Africa and
India.
The strategy significantly underscores trade as a function of equity. With reason of, the
research is a manifestation of the theory of change by illustration of the theory of
competition. Inclusion and facilitation are the mechanisms of development, construed
though farm support infrastructure, investing in health and wellness. Facilitation
introduces the earmarking of Tea Tariffs for compulsory university schooling of the
generation next. Trade escalates Government liquidity and institutionalizes stable farmer
earnings. Given so, good life for these smallholder farmers is achievable.
A special section captures the smallholder farmer opinion and sentiment in written and
conversational exchange of ideas. Despite the uncalled hyperbole of a Cordillera Corridor
intentions for manipulation, the section exposed the farmers' gung-ho to participate in tea
cultivation for trade, that can only be curtailed by dearth of expertise and capital.
Inclusive Growth. The GCC equation for smallho lde r farmer e quity: Gateway to Cross -border Competenc e. Ytt Quaes itum Res earch 202 3
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Chapter 1/ Introduction
This study appraises the plausibility of a trade treaty bet ween an international
organization and Local Government Units; entailing the cultivation and export of tea.
Described as the Tea Corridor, the treaty is envisioned as a long-term strategic approach
for structural transformation through resource use transition. The land resource in the
Cordillera embraces ancestral lands of rugged mountain ranges, with plenty of it idle. The
water runs dry half of the year and yet no systematic amass of the resource is done during
the wet months.
The prospect to transition resource utilisation, optimising land productivity and the
allocation water, dangles in the balance of a fifty-year partnership between the People’s
Republic of China, Belt & Road Initiative/ PRC-BRI1; and the towns of Kapangan a nd
Bokod to pilot the programme. The international organisation is responsible for first seed
provision and farm support infrastructure investment for water holdings and bridgeway;
by far positioning as the exclusive exporter of all produce of these tea gardens, bought at
set industry price2.
Trade between the Chinese merchants and the smallholder farmers is foreseen to raise
competitiveness and deepen sustainability 3. This recounts for structural tr ansformation.
Lack of opportunity and failure to compete by world measure, are structural defects
tackled through job creation or livelihood; inoculating capacity development of the local
folk.
Trade integration provides the ease of Access to the world market for the smallholder
farmer to move up the global value chain4. In the context of competition; the Tea Trade
Corridor transforms an atomistic concentration amongst smallholder farmers into a
monopoly5 by way of community based farming 6, alongside the practice of international
conventions for cross border trade7. In doing so, a single producer supplies the entire
industry output, in order to command price without concern8. More importantly, the Tea
Trade Corridor can enable innovative farm techniques9, with direct growth opportunity;
earnings and raised GDP10. If it is to note, the tradition of small-scale farmin g has narrow
1 Organisation for Economic Co-operation and Development (2018) China's Belt and Road Initiative i n the global trade,
investment and finance landscape, Paris: OECD Bushiness and Finance Outlook.
2 Saul McLeod (2014) Carl Rogers Theory, Cambridge, United Kingdom
3 Ricardo Hausmann, César Hidalgo, Sebastian Bustos, Michele Coscia, Alexander Simoes and Muhammed Yildirim. (2013). The
atlas of economic complexity: Mapping paths to prosperity. Cambridge: Harvard University.
4 Organisation for Economic Cooperation and Development (2021) Inclusive Growth, Paris: Organisation for Economic
Cooperation and Development.
5 Israel Kirzner. (1985). Discovery and the Capitalist Process. Chicago: University of Chicago Press.
6 Guy Michaels, Ferdinand Rauch, and Stephen Redding (2012) Urbanization and structural transformation, The Quarterly
Journal of Economics, 127(2)535-586.
10.1Justin Yifu Lin. (2016). The quest for prosperity: How developing economies can take off. Princeton: Princeton University
Press.
7 Joe Bain. (2022). Monopoly and competition. Chicago: Encyclopedia Britannica.
8 Susan Olzak and Joane Nagel . (1986). Competitive ethnic relations . Orlando: Academic Press
9 Creative Commons Attribution. (2022). Principles of Economics. Houston, Texas: Rice University.
10 The Editors of Encyclopaedia (2022, March 17), Britannica. Retrieved from https://www.britannica.com/topic/protectionism
Inclusive Growth. The GCC equation for smallho lde r farmer e quity: Gateway to Cross -border Competenc e. Ytt Quaes itum Res earch 202 3
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economic significance in global markets and often times prevent a country to be
agriculturally self-sufficient11.
Inclusive growth is a concept that vindicates to have all at stake in growth 12. A corridor
affects the space economy of nations in a system of multiple components, including
infrastructure, services, logistics and regulations 13. Corridors provide opportunities to
strengthen trade flows connecting to international markets 14. This facility creates a
structure of modern efficiencies set forth in a treaty and eventually involving various
nations over time.
• Tea Corridor is defined as an assertion to balance resources and opportunities to
disadvantaged groups with historical inequitable conditions in the sitios of Nawal,
Karao and Pito of barrio Bokod and the sitios of Gadang, Pongayan and Sagubo of
barrio Kapangan.
• Tea Corridor is defined as the facilitation for inclusive growth through trade and
capacity development by unifying a competitive formation of the smallholder farmer
and inculcating international conventions in farm practices.
• Tea Corridor is defined as the enactment of a Gateway for Cross -border
Competence, in the accession of international market determined in a treaty
between PRC-BRI and the LGU.
• Tea Corridor is defined as the North Farm Quadrangle, a single ultimate source of
power and prestige of the modern day tribe; raising taxation efficiency and LGU
capacity to provide welfare for its constituents as well as sufficient funding to
discipline them.
• A Tea Trade Corridor is consistent with the Philippine Development Plan, Pillar
Two: Inequality-reducing transformation, ‘pagbabago’
;
and consistent
with the
pursuit of Philippines Development Plan, Pillar Three: Increasing Growth Potential
‘patuloy na pag-un lad’.
11 Soma Dutta. (2016). Top 25 Agricultural Producing Countries in the World. New York: Yahoo Finance.
12 Elena Ianchovichina and Susanna Lundstrom. (2013). Inclusive growth analytics: Framework and application. New York City:
The World Bank Group.
13 Stephan Klasen. (2010). Measuring and monitoring Incl usive Growth: Multiple definitions, open questions, and some
constructive proposals. Manila: Asian Development Bank
14 Charles Kunaka and Robin Carruthers (2014) Trade and Transport Corridor Management Toolkit. New York: World Bank
Group.
Inclusive Growth. The GCC equation for smallho lde r farmer e quity: Gateway to Cross -border Competenc e. Ytt Quaes itum Res earch 202 3
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THE BARIO FARMER
Benguet farmers concentrate in vegetable production. The province sits throne for nearly
83 percent of vegetable production of regional production, with about 280 crop cover and
for production volume of 292535 metric tons 15 . Vegetable trade utilizes the ancient
Mountain Trail as the vegetable corridor across the province. Trade movements, mostly
cabbage, carrots and potatoes are sent forward to the La Trinidad Trading Post for 41% of
production; and the Baguio Hangar Market for 37% of production. About 17% of
production is traded directly to large wholesalers16.
In terms of trade; the deficit for the Philippines for 2019 for vegetables alone, amounts to
123.7614 billion pesos. Chart 1 presents the shares of the vegetable production in the
Cordillera by province in metric tonnes. Vegetable export comprises okra and asparagus;
chili and squash; which are mostly cultivated outside the Cordillera region. Importation
includes potatoes, mushroom, broccoli, celery and lettuce; or locally produced crop17. As
yet, the deficit in trade for vegetables recognizes poor quality and low yield that affect the
capacity to enter the international market, or even sustain the local demand. Crop yield
declined altogether with the hectares used in vegetable production. Between 2008 and
2017 potato yield is less 0.32%; cabbage production is less 0.54% and carrot harvest is less
1.06%. Land resource utilisation for vegetable production has dropped from 8596 hectares
to 7912 hectares18.
CHA RT 1/ VEGETABLE PRODUCTION19
15 Philippine Statistics Authority (2021) 2021 Preliminary Cordillera Vegetables Situationer, Reference No. SR 2022 17 Manila:
Republic of the Philippines.
16 Maria Eden Piadozo (2013) Efficiency of Benguet vegetable price linkages, Los Banos: University of the Philippines.
17 Japan International Cooperation Agency (2017) Final Report: Survey on issue analysis of food value chain in the Philippines.
London: Price Waterhouse Coopers.
18 Japan International Cooperation Agency (2017) Final Report: Survey on issue analysis of food value chain in the Philippines.
London: Price Waterhouse Coopers.
19 Philippine Statistics Authority (2021) 2021 Preliminary Cordillera Vegetables Situationer, Reference No. SR 2022-17, Manila:
Republic of the Philippines.
Abra
5,767.23
Apa yao
13878.34
Benguet
292535.11
Ifugao
5209.97
Kalinga
1174.56
Mountain
Prov ince
32465.76
Metric Tonnes
351030.97
Inclusive Growth. The GCC equation for smallho lde r farmer e quity: Gateway to Cross -border Competenc e. Ytt Quaes itum Res earch 202 3
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Vegetable farming has churned a consistent income to most municipalities, still some are
behind. Vegetable trade had also been quickly surpassed by other forms of resource
utilisation with higher returns; such as energy generation for Bakun and Bokod; or rare
earth mining for Itogon, Mankayan and Tuba. Crucially and specifically for a handful of
deprived barrios that make a living out of farming; the need for a larger effort to
introduce alternative high value crops suitable on idle rough elevated terrain, urgently
and good enough for export––not pure subsistence.
The target areas to be introduced to tea cultivation belong to two fourth class
municipalities of the Province of Benguet. Bokod has a population of 13,756 persons and
the population of Kapangan is 19,297 persons. The barrios growth level and percentage
composition to the municipal population is shown in Chart 2. Nawal, Gadang and
Pongayan are on downward trends.
CHA RT 2/ POPULATION GROWTH RATE20
Percentage
Population
Population
Change
Growth
2015
2015
2010
2010 - 2015
2010 - 2015
Gadang
7.81%
1,513
1,534
(1.37%)
(0.26%)
Pongayan
4.06%
786
945
(16.83%)
(3.45%)
Sagubo
9.93%
1,923
1,697
13.32%
2.41%
Karao
7.19%
989
958
3.24%
0.61%
Nawal
4.22%
581
743
(21.80%)
(4.57%)
Pito
7.94%
1,092
838
30.31%
5.17%
Basing on the Department of Trade and Industry (2022) Cities and municipalities
competitive index; Chart 3 outlines some of the competitiveness indicators. Between 2017
and 200, the local economy contracted in the town of Kapangan and yet scored higher in
terms of productivity and transport competitiveness. The town of Bokod on the other hand
had reactivated its Hydro-Electric Plant in Ambukl ao, which contributes largely to the
local economy.
CHA RT 3/ DTI INDICATORS OF ECONOMIC COMPETIVENESS21
Local Economy
Productivity
Road Network
Transportation
2017
2022
2017
2022
2017
2022
2017
2022
Kapangan
0.206
0.008
0.010
0.087
0.000
0.000
0.055
0.003
Bokod
0.003
0.026
0.033
0.075
0.001
0.001
0.001
0.002
Chart 4 shows the ratings of both towns in the categories of health and education.
Between 2017 and 2022 scores declined for both towns in these categories. In terms of cost
of living, the localities were exposed to a higher cost of living in 2020, even higher than
the cost of living in Baguio City, and in fact scored relatively close to the cost of living in
Makati City.
20 Philippine Statistics Authority (2015) Re port No 3, 2015 Population, land area and population density. Manila: Republic
of the Philippines.
21 Department of Trade and Industry (2022), Cities and municipalities competitive index, DTI Data Portal, Makati .
Inclusive Growth. The GCC equation for smallho lde r farmer e quity: Gateway to Cross -border Competenc e. Ytt Quaes itum Res earch 202 3
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CHA RT 4/ DTI INDICATORS OF EQUITY22
Education
Health
Cost Of Living
2017
2022
2017
2022
2017
2022
Kapangan
0.240
0.038
0.491
0.173
1.810
1.999
Bokod
0.174
0.083
0.439
0.116
1.810
1.999
The location of these selected six barrios is depicted in Chart 5. The municipalities of Atok
and Tublay are between and these barrios are also provincial borders.
CHA RT 5/ LOCA TION MAP OF BARRIOS
22 Departme nt of Trade and Industry (2022), Cities and municipalities competitive index, DTI Data Portal, Makati.
Inclusive Growth. The GCC equation for smallho lde r farmer e quity: Gateway to Cross -border Competenc e. Ytt Quaes itum Res earch 202 3
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Chapter 2/ Methodology
This study exploits the meta-analysis 23 technique for the extrapolation of pertinent data
from a cross-sectional compilation of studies on smallholder tea gardens in Bangladesh,
China, Japan, Hawaii, India, Indonesia, Iran, Indonesia, Sri Lanka and Scotland;––of
verified field research work on tea cultivation, as tangible results obtained for specific
cultivation objectives. These tangible results are assembled in a probability space of
derivatives, to comprise the essential elements in the tea plant growth, such as substrate
composition and weather conditions.
To establish strong predictions using these extrapolated derivatives, the stochastic
abstraction modelling approach is applied to define tangible limits of the GCC Equation24,
the Gateway to Cross -border Competence. Stochastic abstraction is the random between
maximum and minimum limits25. Randomness in this context simply means a random
probability distribution or pattern analysed statistic ally to predict outcomes. The
mathematical formula ensures reliability in simulation of the probable farm yield, land
utilisation or level of accession to in ternational market. Stochastic techniques are broadly
used as mathematical systems in random manner, and interpret as a random element i n
a function26.
The research philosophy applied is positivism. In positivism, the fundamental assumption
is that the nature of reality is objective, tangible, and singul ar 27. The research approach is
deductive, as it examines theory or phenomenon to tests its validity within the specific
circumstance. Deductive approach observes the most logic al path to start the reasoning28
with a theory that directs the study structure in compilations or meta-analysis to validate
the equation.
A deductive reasoning is explained further as the discerning from a general perspective to
the particular29. The compilation of case studies recognizes verified field research work as
tangible results of specific tea cultivation objectives. These tangible results are collated as
explanations th at establish strong predictions, if not derivatives to v alidate the viability
of tea production in the Cordillera under similar climatic condition. These derivatives are
general, average, and representative of standardisation.
23 Christopher Armitage (2001) Efficacy of the theory of planned behaviour: a Meta-Analytic review. (D. Youngblood, Ed.) British
Journal of Social Psychology, 40, 471-499.
24 Oliver Knill (2009) Stochastic Processes, Selangor: Encyclopedia of Bioinformatics and Computational Biology.
25 Colin Robson (20 02) Real world research: A resource for social scientists and practitioner-researchers (2nd Ed.). Oxford:
Blackwell Publishers Ltd
10.1Mark Saunders, Philip Lewis, Adrian Thornhill, Alex Bristow (2019) Research Methods for Business Students, 5th Edition
New Jersey: Prentice Hall
10.2 Yosef Jabareen (2009) Building a Conceptual Framework: Philosophy, Definitions, and Procedure. International Journal of
Qualitative Methods, 8(4), 49-62.
26 Olav Kallenbe rg (2002) Foundations of Modern Probability, Berlin: Springer Science & Business Media.
11.1 William Hosch (2022 March 6) Stochastic Process Retrieved from Encyclopedia Britannica: https://www.britannica.com/
science /stochastic-process
27 Karen O'Reilly (2009) Sage Research Methods, New York: Sage Publications.
28 Jonathan Wilson (2010) Essentials of business research: A guide to doing your research project. New York: SAGE Publications.
29 Ashok Gulati (2009) Research management: Fundamental and applied research, New Delhi: Global India Publications.
Inclusive Growth. The GCC equation for smallho lde r farmer e quity: Gateway to Cross -border Competenc e. Ytt Quaes itum Res earch 202 3
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The Case-study methodology is apt, as this research entails to elicit tangible, contextual,
in-depth knowledge on specific real-world data on tea cult ivation. The Case-study method
explores the rudimentary characteristics, meanings, and inferences of the slump on tea
yield by impact of weed dominance; among others. This is a complex case study because it
looks into multiple case studies to associate and illuminate different aspects of a research
problem. The approach is excellent for defining, comparing, evaluating and understanding
various components the theoretical construct30.
In this study, the compilation is totally cross-sectional and unique of the other. The work
embeds the cross-sectional technique to compare samples at a single point in time.
Typically thought as a snapshot, where the findings drawn prove apt derivatives to
constitute this mathematical construct. A snapshot of a single moment in time, and do not
consider occurrences before or after the snapshot is taken. The snapshot is construed as a
conclusive event of each earlier work, where inferences, general and average data are
then utilised to extrapolate tangible limits in the current analysis. Across-sectional study
assumed time to have a random effect that produces only variance and not bias31.
The illustration of an Impact Pathway helps envisage the Theory of Change3233 and shown
in Chart 6. Whatever change brought forth by the Tea Corridor is linked to alterations
resulting farm revenues34 and farm sustenance infrastructure 35. These are measurable
change36. No matter how good the research replication pegged; the impact pathway is
focused on a single industry and is location specific in this study.37
The study methodology adheres to standards of mathematical rigour and gives weight to
proven experiments and observations. It construes fundamental physics of complex
structures of competing elements and dependencies between variables. This rese arch is
labelled by Economist Christian Zimmerman, Editor of Econpapers University Library of
Munich, for the application of
Heterodox Approaches
often emphasize non-market aspects
of economic phenomena, such as social identity, cooperative collective action, power
relations, and psychological biases.
30 Shona McCombes (2019) Case Study definition, examples and methods, London: Scribbr Knowledge Base
31 Paul Lavrakas (2008) Cross-sectional data, Ontario: Encyclopedia of Survey Research Methods.
32 Berthold Herrendorf, Richard Rogerson, and Ákos Valentinyi (2014) Growth and structural transformation. In S. D. Philippe
Aghion, Handbook of economic growth, Volume 2 (pp. 855 – 941). Washington: Elsevier Publisher.
33 Frederick Barth. (1963). The Study of Social Change. Plenary Address to the Anthroþologist Association Meeting' 1966
Arlington, Virginia: American Anthropologist Association
34 Law Teacher. (2022). The Historical Development of Equity Law. Dubai: Law Teacher Net.
35 Eduardo Fernandez-Arias, Charles Sabel, Ernesto Stein, and Alberto Trejos (2016). Two to tango: Public-private collaboration
for productive development policies. Washington: Inter-American development bank.
36 Jean Imbs and Romain Wacziarg (2003) Stages of diversification. American Economic Review, 93(1), 63-86.
37 Malcolm Hawkesford, Walter Horst, Thomas Kichey, Hans Lambers, Jan Schjoerring, Inge Skrumsager Møller, Philip White
(2012). Functions of macronutrients. In P. Marschner, Marsc hner’s Mineral M utrition of Higher Plants (pp. 135 -189). London:
Academic Press.
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CHA RT 6/ THE IMPACT PATHWAY CONCEPTUAL FRAMEWORK
Verifiable indicators are outlined in Chart 7 to validate the impact pathway as the logical
framework38. The priority levels for results are directed to the envisioned structural
change39 aimed at societal transformation.
38 Eduardo Fernandez-Arias, Charles Sabel, Ernesto Stein, and Alberto Trejos (2016). Two to tango: Public-private collaboration
for productive development policies. Washington: Inter-American development bank.
39 Jean Imbs and Romain Wacziarg (2003) Stages of diversification. American Economic Review, 93(1), 63-86.
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CHA RT 7/ THE IMPACT PATHWAY LOGICAL FRAMEWORK OF VERIFIABLE INDICATORS
First Key Result.
Increased Resource Utilisation,
land/water optimum
Second Key Result.
Increased yield
High Value Crop
Third Key Result.
Better Life. Abolition of the cycles of
disadvantage
Objectively Verifiable Indicators
Change in idle land in the areas of
Nawal, Pito & Karao; Gadang, Sagubo
&Pongayan of elevation between 1000-
1400 meters (Decrease)
Change in number of registered small
scale farmers (Increas e)
Change in hectares of land use for tea
plantation (Increase)
Change in road ratio than 22.4 km per
10,000 persons
Change in cost of transportation after
road (Decrease)
Change in frequency of manual hauling
(Abolition)
Change in landslide frequency
(Decrease)
Change in volume of water distribution
(Increase)
Change in traffic volume in no of
vehicles (Increase)
Change in water-related health disease
& deaths (Decrease)
Change in export volume in metric tons
(Increase)
Change in production volume in metric
tons (Increase)
Change in GDP capita (Increase)
Change in number of active farmer
organisations (Increase)
Change in earmarked taxes for equity at
local level (Increase)
Change in GDP contribution for region
& province (Increase)
Change in municipal profile (Positive)
Change in national Government
revenue allocations (Decrease)
Change in taxation collection efficiency
of LGU (Increase)
Change in voter participation (Increase)
Change in farm technology
(Improvement)
Change in earnings and minimum
wages (Increase)
Change in job creation (Increase)
Change in living standards
(Improvement)
Change in number of enterprises in the
municipality (Increase)
Change in number of Out of School
Youth (Decrease)
Change in schooling completion
(Increase)
Change in number of persons living in
poverty (Decrease)
Change in political bashing (Decrease)
Change in number of unemployed
(Decrease)
Change in worker protection
contributions (Increase)
Change in number of Professionals &
skilled workers (Increase)
Means of Validation
Department of Health & WHO Reports
LGU Profile
NAMRIA maps on elevation contours
NAMRIA maps on elevation contours
Philippines Statistics Authority Reports
Bureau of Internal Revenue Reports
DA RED Reports
Department of Agriculture Reports
Department of Trade and Industry
Food Development Authority Reports
PhilExport Portal
Philippines Statistics Authority Reports
Bangko Sentral ng Pilipinas Reports
Department of Education Reports
Department of Labour & Employment
Portal
Department of Social Welfare & UN
Reports
Philippines Statistics Authority Reports
Professional Regulations Report
Assumptions
International standard on built
environments is observed
Tea Corridor proceeds with least
anomalous activity
Trade Treaty of 50 years is observed
Farmers observe international
conventions for tea growth
Tea Corridor does not identify with any
political party
Philippine resilience in currency
exchange volatility
A smallholder farmer is self-motivated
and wants to improve the conditions of
the industry
Philippine resilience in World financial
crisis
Risks
Climate change does not dry out the
water source abruptly
National Government takes over project
ownership and develops the existing
EPZA as the port of entry
Climate change does not reduce rainfall
abruptly
Cotabato Government forges better
trade offer with PRC-BRI
Political party clash on the priority
locations of the Tea Corridor
Infiltration of leftist groups asserting to
maintain the status quo to keep farmers
poor
Political party populism and hegemony
is pursued rather than the pure logic of
structural transformation
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Chapter 3/ GCC Equation
Gateway to Cross-Border Competence
The purpose of this equation is to establish the parameters of Accession. GCC equation or
“Gateway for Cross -border Competence” is the stochastic abstraction of tangible limit that
substantiates the Tea Corridor as a significant strategy for resource use transition. The
GCC equation defines the Tea Corridor parameters in three elements: Yield constant
ratio— Y0; land utilization— and cost of farm produce constant ratio— χ.
GCC equation = χ Y0
The GCC equation has specific mathematical relevance in the theoretical construct,
forecasting production levels of tea cultivation for the Cordillera.
The element Yield Y0 is the designated symbol for yield; defined as a constant ratio with
special mathematical relevance in the GCC equation. Yield Y0 constant ratio is set at the
significance of 1.928412 tonnes with intervals in terms of one hectare. Yield Y0 is deduced
by stochastic abstraction and verified by conventional theory. The weighti ng of the
constant ratio yield Y0 can change in the fact of occurring.
The element Land Utilization is a dependent variable, with special m athematical
relevance in the theoretical construct forecasting tea production for the Cordillera. Land
utilization is at all instances less than the overall land area determined suitable for tea
cultivation by DENR assessment NAMRIA orthoimage estimates, and places priority
on the 1.2 percent indicative country derivative of idle land 40 . Land Utilization is
defined as,
Where n is the forecast labour group, made up of the population between ages 20 and 59,
k is the constant 3 tea farmers per hectares tea cultivation; 23 percent is the recognized
labour in agriculture derived by stochastic abstraction.
The element χ as cost of farm produce χ is a constant ratio set at the significance of
163,680 pesos with intervals in terms of tonne. The value derived by stochastic
abstraction of tangible limit, synthesized auction prices through 25 years, 1998 up to 2022
from the British auctions for Mombasa and Nairobi 41. The cost of farm produce χ constant
ratio is rudimentary in qualifying the farmer equity o f stable earnings and an excellent
hurdle rate for infrastructure investments.
40 Congressional Policy and Budget Research Department (2016), Idle land Tax: Implementation issues and challenges. Manila:
House of Representatives, CPBRD Policy Brief No. 2016 - 02
41 Tea Broker's Association of London; International Tea Committee; African Tea Brokers Ltd. (2022). Tea (Mombasa/Nairobi
auctions), African origin, all tea, arithmetic average of weekly quotes// Unit: US Doll ars per Kilogram//. Ne w York: The World
Bank.
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Chapter 4/ Element Yield Y0
The element Y0 yield is determined at a constant ratio 1.928412 tonnes with intervals in
terms of one hectare. This constant ratio is deduced by stochastic abstraction of yield per
hectare, or yield-hectares.
Y0 is made up of four components; yield-hectares Y, yield-substrate Y1, yield-weed-slumpY2
and yield-envelope Y3.
Yield-hectares Y is deduced from cross-sectional data of 86 tea-producing nations,
synthesized through stochastic abstraction. Information on the overall land area for tea
farming in hectares 42 ; and the production level in tonnes that correspond these tea
producing nations, constitute the origins of the forecast43. The resulting yield-hectares Y
is expanded using studies on variables that enable increased levels in tea yield by
nutritional input s, such as lime to nurture PH content in soil. These yield enhancers are
scrutinized as yield-substrate Y1.
The negative impact on yield when without weed control is adapted in the forecast as
yield-weed-slump Y2 using a number of common weed varieties in terms of abundance,
frequency and dominance 44. The forecast is realigned by impact of ecology, and written as
yield-envelope Y3. Precipitation underscored as the moisture of a rooting environment; if
not a strong watering requirement referred as the yield-envelope. The yield-envelope is
the forecast is based on the captured behaviour recorded in historical weather data for the
past 11 years. Weather data covers the years 2010-2021 monthly detail on temperature,
humidity and rainfall45.
For substantiation of Y0 yield, the volume of tea harvest from the country averages is
synthesized by stochastic abstraction; and defined as yield -hectares Y with an initial value
of 2 tonnes at intervals of one hectare s (see page19-20). By mathematical computation, it
is raised by 37% by scrutiny of yield-substrate Y1
(page 21-22) which obtains the value of
2.74 tonnes. By mathematical analysis deduct (-) 0.8768 as the equivalent 32 percent
yield-weed-slump Y2 (page 25-26) and the value 1.8632 tonnes are obtained.
This value is raised by half of yield-envelope ½ Y3 (pages 27-25) to equal 1.9241 tonnes; or
–seven percent of the value 1.8632 is equal to 0.130424, which is divided by 2 to obtain
0.06521, and added to 1.8632, which equals 1.9241 tonnes. The value of yield -envelope Y3
is halved considering the ideal rainfall of 150-250 mm happens only half of the year.
42 Michael Chamberlain (2019) The Top 62 Countries, and Quantities. London: Tea How.
43 Jordan G. Hardin (2017) List of Tea Producing Countries in the World, London: The tea engineer.
44 Barua, J D (2015) Weed of tea field and therir control. National Seminar on plant protection in Tea, Tea research Association
(pp. 55-56). Tocklai : Tea Research Institute India.
45 DOST PAG-ASA, 2022
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YIELD-HECTARES Y
Yield by dint of physical space is characterised in this research as a constant for tea
volume output, and defined as yield-hectares Y. This constant is crafted using actual
production volume averages for one hectare. Y does not define the quality of tea
cultivation or market value.
Chart 8 is the stochastic abstraction of yield hectares across a probability space of 95 tea
producing nations,46 where the variable Y is expressed in yield-hectare in tonnes. Chart 9
is an illustration of the stochastic abstraction of the tangible limits in terms of yield using
the conventional plant spacing way of thinking.
CHA RT 8/ STOCHASTIC ABSTRACTION YIELD-HECTARES47
CHA RT 9/ STOCHASTIC ABSTRACTION YIELD CROSS–CHECK
Mean, μ = 2.36269,
Standard Deviation, σ = 2.25430
Upper Limit, = 9.12560
Lower Limit,
Stochastic Abstraction yield-hectares Y = 2.0 tonnes at intervals of one hectares
46 Of the enl isted 43 nations, China is the leading tea-producer with 1.939 million tonnes per year and a gigantic land area
dedi cated to tea cultivation to produce 0.830 tonnes per hectare. The same goes for Bangladesh 0.955 tonnes, Azerbaijan 0.857
tonne s and Montenegro 0.813 tonnes. Similar that of China and Bangladesh, large tracts of land are turned into tea gardens to
produce less than one ton every hectares: Russia 0.152 tonnes, Laos 0.215 tonnes, Myanmar 0.258 tonnes and Congo 0.234
tonne s. The majori ty of nations on the tea producer list churn out a tea production slightly over tonne but not over two tonnes:
Georgia 1.939 tonnes, Indonesia 1.308 tonnes, Japan 1.979 tonnes, Kenya 1.831 tonnes, Mali 1.456 tonnes, Mozambique 1.198
tonne s, PNG 1.192 tonnes, Rwanda 1.084 tonnes, Sri Lanka 1.680 tonnes, South Korea 1.180 tonnes, South Africa 1.250 tonnes,
Tanzania 1.907 tonnes, Uganda 1.871 tonnes, Vietnam 1.837 tonnes, Zambia 1.355 tonnes. Nonetheless, a highly efficient tea-
yiel d per hectare production is identified with countries of very diverse settings: Malaysia 9.657 tonnes, Iran 8.652 tonnes, and
Portugal 7.368 tonnes; followed by Thailand 6.927 tonnes, Ecuador 5.607 tonnes and Bolivia 5.015 tonnes.
47 Daniel Workman (2021, May 30) Tea Exports by Country, Retrieved from World's Top Exports:
https://www.worldstopexports.com /tea-exports-by-country
47 Dan Bolton (2016, October 5) Global Tea Production 2015, Retrieved from World Tea News: https://worldteanews.com/tea-
industry-news-and-features/global-tea-production-2015
46. 1 Soma Dutta (2016) Top 25 Agricultural Producing Countries in the World. New York: Yahoo Finance.
47 Kaison Chang (20 15) World tea production and trade Current and future development. Rome: Food And Agriculture
Organization of the United Nations.
47 Mahsa Shahbandeh (2021) Global production and exports of tea from 2004 to 2019,
https://www.statista.com/statistics/264183/ global -production-and-exports-of-tea-since-2004/
0.10000
1.00000
10.00000
1 6 11 16 21
Yield by Rule of thumb of plant spacing of four
varieties and three spacing schemes
0.10000
1.00000
10.00000
-15 525 45 65 85
Stochastic abstraction tangible limit yield-
hectares in a probability space of 86 nations
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The meta-analysis is presented in the cross-sectional data-set of historical yield per
hectare across 43 tea producing nations is placed in Chart 10.
CHA RT 10/ CROS S-SECTIONAL DATA SET OF HISTORICAL YIELD
Cou ntry
He ctares
Yi eld
La bour Agriculture
La bour Force
Argentina
39,600
105,000
9.02%
18,000,000
Azerbaijan
663
568
36.00%
4,680,000
Bangladesh
67,045
64,000
38.30%
66,640,000
Bolivia
274
1,374
30.54%
4,992,000
Brazil
211
763
9.08%
104,200,000
Burundi
9,703
41,817
86.21%
4,245,000
Cameroon
2,168
4,700
43.49%
8,426,000
China
2,336,066
1,939,457
25.33%
791,483,000
Colombia
60
125
15.77%
25,760,000
Congo
12,410
2,900
33.53%
2,890,000
Ecuador
535
3,000
29.74%
6,953,000
Ethiopia
9,400
7,400
66.63%
52,820,000
Georgia
1,702
3,300
38.15%
1,959,000
Guatemala
1,209
510
31.30%
4,465,000
India
628,193
1,939,457
42.60%
476,670,100
Indonesia
113,215
148,100
28.50%
125,000,000
Iran
18,493
160,000
17.37%
30,500,000
Japan
42,858
84,800
3.38%
65,010,000
Kenya
236,200
432,400
54.34%
19,600,000
Laos
4,195
900
61.44%
3,337,000
Madagascar
779
600
64.12%
9,504,000
Malawi
18,094
54,000
76.36%
5,747,000
Malaysia
1,903
18,377
76.36%
13,190,000
Mali
103
150
62.44%
3,241,000
Mauritius
656
1,536
5.97%
1,318,000
Montenegro
123
100
7.15%
251,300
Mozambique
19,202
23,000
70.22%
10,550,000
Myanmar
89,127
23,000
48.85%
22,300,000
Nepal
28,595
20,588
64.38%
16,000,000
PNG
3,943
4,700
56.15%
4,077,000
Portugal
19
140
5.50%
5,395,000
Russia
594
90
5.83%
76,530,000
Rwanda
20,466
22,185
62.29%
4,446,000
South Africa
720
900
5.28%
22,190,000
South Korea
2,712
3,200
5.14%
27,750,000
Sri Lanka
202,540
340,230
24.98%
8,528,000
Tanzania
17,674
33,700
65.09%
24,890,000
Thailand
10,827
75,000
31.43%
38,370,000
Turkey
83,611
212,400
18.11%
31,300,000
Uganda
28,332
53,000
72.13%
17,400,000
Vietnam
116,633
214,300
37.22%
54,800,000
Zambia
664
900
49.64%
6,275,000
Zimbabwe
7,572
19,000
66.19%
3,939,000
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YIELD SUBSTRATE Y1
Yield by dint of substrate or nourishment, is for consistency of plant growth and volume
output improvement. A tea leaf has a chemical composition made up of about thousand
components, which develops a unique phytochemical structure in a classification of
types48. Positive variations between tea leaves of the same type result of the conditions of
climate or altitude; and oxidation or soil49. Tea quality is determined during harvest, by
selection of maturity in terms of the polyphenol content, or a secondary metabolite or sub
group of the polyphenol called catechin50.
The basic substrate requirement of a tea leaf is relatively high 51. Substrate nourishment
is obvious in the shoot extension length and regeneration pace52. Substrate nourishments
add properties to raise yield; and replace biomass or plant foci depletion over sequential
harvests53. Nutrient depletion in the tea plant is contingent on the intensity and duration
of plucking rounds54. Consecutive leaf harvest empties the macronutrients a nd therefore
replenishment is crucial55.
Nitrogen56, phosphorus57, and potassium are of first level of importance; which is often
supplemented with calcium, magnesium, sulphur and zinc. Low nitrogen content in the
substrate, reduces the ability of feeder roots to take up nutrients58, losing the potential
5800-6400 kg ha, year-on-year stimulated by constant shoots harvest59. Additionally, lime
solutions improve fertility or oxygen levels, soil quality improved and increased pH level
between 5 and 10.3360. Potassium deficiency is v isible in thin, weak young plant branches
affecting leaf fall 61. Feeder roots are less developed feeder roots, including leaf margins
48 Chi-Tang Ho, Jen-Kun Lin, Fereidoon Shahidi (2009) Tea and tea products: Chemistry and health-promoting properties. Boca
Raton, FL, USA: Taylor & Francis Group.
49 Alexandr Yashin, Boris Nemzer, Emilie Combet and Yakov Yashin (2015) Determination of the chemical composition of tea by
chromatographic methods: a review, Journal of Food Research. vol. 4(3), 56 -87
50 Malcolm Hawkesford, Walter Horst, Thomas Kichey, Hans Lambers, Jan Schjoerring, Inge Skrumsager Møller, Philip White
(2012). Functions of macronutrients, In P. Marschner, Marschner’s mineral nutrition of higher plants (pp. 135-189). London:
Academic Press.
51 Shan-Lian Qiu, Li-Min Wang, Dong-Feng Huang, and Xin-Jian Lin (2014) Effects of fertilization regimes on tea yields, soil
fertility, and soil microbial diversity. Chilean Journal of Agriculture, vol. (74) 3, 333-339.
52 Selvaraj Venkatesan, Subramanian Murugesan, Muthukumar Ganapathy, Dinesh Verma (2004) Long-term impact of nitrogen
and potassium fertilizers on yield, soil nutrients and biochemical parameters of tea. Journal of the Science of Food and
Agriculture vol. 84, 1939–1944.
53 Joyce Kamande. (2021). Best tea fertilizer to increase yields. Nairobi: Safiorganic.
54Shahram Sedaghathoor, Ali Mohammadi Torkashv, Davood Hashemabadi and Behzad Kaviani Livani (2009) Yield and quality
response of tea plant to fertilizers. African Journal of Agricultural Research 4(6), 568-570.
55 Isaiah Masinde Tabu, Vivian Moroamoche Kekana and David Murathe Kamau (2015) Effects of varying ratios and rates of
enriched cattle manure on leaf nitrogen content, yield and quality of tea (Camellia sinensis). Journal of Agricultural Science, 175-
181.
56 Kibet Sitienei, Patrick Home, David Kamau, John Wanyoko (2012) Nitrogen and potassium dynamics in tea cultivation as
influenced by fertilizer type and application rates, Naroibi: Tea Research Foundation of Kenya and University of Agriculture and
Technology, Biomechanical and Environmental Engineering Department.
57 Chi-Feng Chen and Jen-Yang Lin (2016) Estimating the gross budget of applied nitrogen and phosphorus i n tea plantations,
Science Direct, Sustainable Environment Research Vol 26 (3), 124-130.
58 Philip Owuor and Daniel Cheruiyot (2010) Effects of nitrogen fertilizers on the al uminium contents of mature tea leaf and
extractable aluminium in the soil, Plant and Soil, vol. 119(2), 342–345
59 Jianyun Ruan, Jóska Gerendás, Rolf Härdtez and Burkhard Sattelmacher (2007) Effect of Ni trogen form and root -zone pH on
growth and Nitrogen uptake of tea plants, Annals of Botany, vol 99(2), 301–310.
60 Tea Research Foundation (1997) Fertilizer use in tea: the case of nitrogen. Nairobi: Food and Agriculture Organization of the
Unite d Nations.
61 Tea Research Foundation of Kenya (2012) Tea Cultivation Manual for Good Agricultural Practices, Nairobi: Tea Research
Foundation of Kenya.
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and tips 62 . Phosphorus deficiency is visible in leaves without its natural gloss and
formation of new wood and roots in tea63. Sulphur deficiency is visible in yellowish leaf
veins, before falling off the branches64. Nitrogen deficiency display short internodes and
faint green colour, stunted development of buds and fewer shoots65.
Chart 11 is the stochastic abstraction of yield-substrate in a probability space of five
decades, and twelve African and Asian countries. The compilation of cross-sectional data-
set of substrate nutrients’ upper and lower limit for studied in twelve nations for a span of
50 years. Chart 12 is the stochastic abstraction of tangible limit of Y2 yield-weed-slump
within the probability space of 52 weed species in terms of abundance, frequency and
abundance.
CHA RT 11/ STOCH ASTIC ABSTRACTION Y1 YIELD-SUBSTRATE
CHA RT 12/ STOCHASTIC ABSTRACTION YIELD-WEED-SLUMP Y2
For an overall 40 varying experiments and 11 different substrates
Mean, μ = 98.76923
Standard deviation, σ = 36.26558
Upper Limit = 207.56597
Lower Limit
Stochastic Abstraction yield substrate Y1
The soil types suitable for teas is characterised as rich in humus, good amount of lime.
Light loamy soil with porous subsoil; and acidic soil of pH between 4.5 and 5.0.The
substrate quality in Bokod is characterised as Ambassador Silt Soil with pH level
between 5.0 and 5.5 in Nawal; Guimbalaoan Annam Complex with pH level between 5.0
and 5.9 in Pito; and undifferentiated Mountain Soil having pH level between 4.6 and 4.9
in Karao. The substrate quality in Kapangan is described to contain soil types of
Balakbak with pH between 4.9 and 5.7; Mountain Soil with pH level between 4.4 and 6.6;
Puguis Gr L with pH level between 4.9 and 6.2; and Rough Mtn L having pH between 4.4
and 6.4.
62 Jakia Sultana, Noor-E-Alam Siddiqus, Kamaruzzaman Halim and Abdul Halim (2014), Conventional to ecological: Tea plantation
soil management in Panchagarh District of Bangladesh. Journal of Science, Technology and Environment Informatics, vol. (1)3,
27-35.
63 Günter Neumann and Volker Römheld (2012) Rhizosphere chemistry in relation to plant nutrition, In P. Marschner,
Marschner’s mineral mutrition of higher plants. (pp. 3347-3368), London: Academic Press.
64 Tanmoy Karak and Rajiv Bhagat (2010). Trace elements in tea leaves, made tea and tea infusion: A review. Food Research
International, vol 43(9), 2234-2252.
65 Janendra De Costa, Anoma Janaki Mohotti and Madawala Wijeratne (2007) Eco physiology of tea. Brazilian Journal of Plant
Physiology 19(4), 299-332.
1
10
100
1000
-1 4 9 14 19 24
Stochastic abstraction tangible limit of yield-
substrate in a probability spaces of 13 nutrients,
and 5 decades studies in Africa and Asia
0.01000
0.10000
1.00000
10.00000
100.00000
-30 20 70 120 170
Stochastic abstraction tangible limit of yield-
weed-slump in a probability spaces of 52 tea weed
species frequency, abundance and dominance
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CHA RT 13/ CRO SS-SECTIONAL DATA SET Y1 YIELD-SUBSTRATE
Nutrition Impact On Yield
Source of Field Experimentation/Date
Ammonium (NH4)
South Africa, Malawi, China & Japan
19-76%
Hilton, Palmer-Jones & Ellis (1973)
Li,Wang and Stewart (2005)
Okano, Chutani and Matsuo (1997)
Sitienei, Home, Kanyiri & Kamau (2013)
Lime (CaO3)
Japan, Iran , Bangladesh
5-68%
Ruan, Gerendás, Härdter and Sattelmacher (2007)
Zheng, Xu, Li, Hui, Wu & Huang (2013)
Zhu, Zhang, Meng, Zhang, Yang, Müller, & Cai (2014)
Magnesium (Mg)
Iran, China & Japan
5-86%
Abe, Hashi, Masunaga, Yamamoto, Honna & Wakatsuki (2015)
He, Chen, Zhang, Huang, Yin, Weng, Yang, Wu, Zhang and Wu
(2023), Saha (2015)
Sedaghathoor, Torkashv & Livani (2009)
Monosodium glutamate (MSG)
Taiwan
2-18%
Ruan, Gerendás, Härdter & Sattelmacher (2007)
Sedaghathoor, Torkashv & Livani (2009)
Nitrogen (N)
Tanzania, Kenya, Iran, Vietnam, India,
Bangladesh, Pakistan, Japan & Africa
5 -141%
Abe, Hashi, Masunaga, Yamamoto, Honna & Wakatsuki (2015)
Dutta (2011), Wen-sheng (2007)
He, Chen, Zhang, Huang,Yin, Weng, Yang, Zhang & Wu (2014)
Hoang, Thang, Thu, Binh, Toan & Hoang (2021)
Jianyun, Yanliang & Xun (2002)
Owuor & Cheruiyot (2010)
Sedaghathoor, Torkashv & Livani (2009)
Tea Research Foundation of Kenya (2012)
Phosphorus (P)
India, Tanzania & Vietnam
15-141%
Abe, Hashi, Masunaga, Yamamoto, Honna & Wakatsuki (2015)
Liu, Jin & Mao (2021)
Zaman, Islam, Hamid, Ahmad & Aslam 2016
Potassium (K)
Africa, Iran, Vietnam, India, Japan,
Tanzania
5-141%
Abe, Hashi, Masunaga, Yamamoto, Honna & Wakatsuki (2015)
Hoang, Thang, Thu, Binh, Toan & Hoang (2021)
Mukhopadhyay & Mondal (2017)
Sitienei, Home, Kanyiri, & Kamau (2013)
Venkatesan, Murugesan, Ganapathy & Verma (2004)
Sulfur (S)
Africa, Vietnam, India
10-99
Hoang, Thang, Thu, Binh, Toan & Hoang (2021)
Mukhopadhyay & Mondal (2017)
Sedaghathoor, Torkashv a& Livani (2009)
Sodium Nitrate (NaNO3)
China, Japan, India
27-97%
Abe, Hashi, Masunaga, Yamamoto, Honna & Wakatsuki (2015)
Dutta (2011)
Urea (CH4N20)
Japan, China, India
12-86%
Mukhopadhyay & Mondal (2017)
Nookabkaew, Rangkadilok, Prachoom, Satayavivad (2016)
Sitienei, Home, Kanyiri, & Kamau (2013)
Zheng, Xu, Li, Hui, Wu & Huang (2013)
Zinc (Zn)
Africa, Iran, Japan, China
5-68%
Okano, Chutani and Matsuo (1997)
Ruan, Gerendás, Härdter & Sattelmacher (2007)
Zheng, Xu, Li, Hui, Wu & Huang (2013)
Zhu, Zhang, Meng, Zhang, Yang, Müller, & Cai (2014)
Organic Substances
Hong Kong, China, Japan
6-84%
Ming-jua (2013), Mukhopadhyay & Mondal (2017)
Sedaghathoor, Torkashv & Livani (2009)
Venkatesan, Murugesan, Ganapathy & Verma (2004)
Fish flour , bone meal, oil cake
Japan
31-42%
Abe, Hashi, Masunaga, Yamamoto, Honna & Wakatsuki (2015)
Ye, Wang, Wang, Hong, Jia, Kang, Lin, Wu & Wang (2022)
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YIELD WEED-SLUMP Y2
Weeds form a critical biological constraint that curbs plant productivity 66. Due to the
increased competition on resources, tea yield can slump to about 31.5 percent; between
22.7 and 36.5 percent, over wet and dry months respectively67 . Specifically for tea, a
rather severe competition for nutrients and water uptake occurs when the young plant is
congested with weed. Observations of adverse effects on the young tea growth include few
primary branches and smaller sized tea leaves68. Tea weeds that have been repeatedly
studied include: 240 species of plants as tea weeds from Java, examined by Backer and
van Slooten as early as 192469. In 1949 Ano and Nakayama listed 125 species from the
temperate tea gardens of Japan; then again by Soedarsan et al in1974 scrutinized weeds
found in tea estates at 690 and 1570 meters altitude70.
Weeds that are aluminium accumulators 71 prove ideal tea locations 72 . Among these
aluminium accumulator weeds common to tea gardens, the following are identified to be
common to Benguet; serving as indicators of the suitability of tea: Ageratum conyzoides,
Bidens pilosa, Crassocephalum crepidioides, Galinsoga parviflora, Paspalum conjugatum
and Portulaca oleracea 73. Chart 13 shows dominance in weeds that would mean the
prevalence of the weed specie, frequency for the repeated appearance over a specific
period of time; and abundance is the weed specie profusion.
The stochastic abstraction of tangible limits of the cross sectional dataset presented in
Chart 13 is illustrated in the earlier Chart 12/ stochastic abstraction yield-weed-slump Y2
results in 32 percent.
Mean, μ = 25.89462
Standard deviation, σ = 35.44292
Upper Limit, = 132.22338
Lower Limit,
Stochastic Abstraction yield-weed-slump Y2
66 Kapila Prematil ake, Robert Froud-Williams and Punchi Ekanayake. (2004). Investigating of increasing glyphosate herbicide
efficiency with nitrogen in control of tea weeds. Weed Biology and Management, vol. (4)4, 239-248.
67 Jayanta Deka and Iswar Barua (2015). Weed of tea field and their control. National Seminar on plant protection in Tea, Tea
research Association (pp. 55-56). Tocklai : Tea Research Institute India.
68 Tigist Bidira, Tamiru Shimales, Melaku Adissu and Tadesse Eshetu (2021). Weed species dominance and abundance in Tea
(Camellia sinensis L.) plantation of southwest Ethiopia. American Journal of Plant Biology, (6)4, 89 -94.
69 Cornelis Andries Backer and Dirk Fok Van Slooten (1924) Javaansche Theeonkruiden. Bata- via Drukkerijen Ruygrok & Co.
70 Hidehiro Nagaki and Masashi Tsushi (2020) Intraspecific variation in Sonchus Oleraceus, a biennial weed species, inside and
outside of tea gardens, Annals of Ecology and Environmental Science, Vol. 4(3), p. 26-30
71 Shigeki Konishi,Sobun Miyamoto and Takayuki Taki . (1985). Stimulatory effects of aluminum on tea plants grown under low
and high phosphorus supply. Soil Science and Plant Nutrition Volume 31(3), 361-368.
72 Masahiko Ohsawa. (1998). Weeds of tea plantations. In E. G. Camarasa, Temperate rain forests. Biosphere. Vol 6.
Tokyo: Temperate rain forests. Biosphere. Vol 6.
73Jone s Napaldet, Jhunedy Antonio, Margarette Bacate, Jackson Butag, Sheinalene Ladoan and Gina Vicente (2020)
Vascular plant di versity in Benguet State University La Trinidad main campus, Philippines: A status report and a database
to support the attainment of sustainable development. Journal of Wetlands Biodiversity, vol.10, pp. 21 -42
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CHA RT 14/ CROS S-SECTIONAL DATA SET OF TEA WEEDS
Sp e cies
Frequency (%)
Abundance
Dominance
Ageratum conyzoides
80
19.1
19.4
Ageratum conyzoides
77.2
14.9
24.2
Amaranthus debius
5
0.3
0.3
Amaranthus dubius
14.3
0.8
1.3
Amaranthus hybrids
11.4
0.7
1.2
Amaranthus hybridus
5
0.2
0.2
Biden spilosa
55
7.7
7.8
Bidens pachyloma
5
0.1
0.1
Bidens pilosa
37.2
1.2
2.0
Bidens polychyma
11.4
0.7
1.1
Caylusiaabyssinica
5
0.3
0.3
Commelina benghalensis
74.3
10.1
16.4
Commelina benghalensis
75
16.2
16.4
Commelina subulata
10
0.1
0.1
Coniza albida
15
0.9
1.0
Convolvulus arvensis
2.9
0.4
0.7
Conyza albida
5.7
0.3
0.5
Corchorus olitorius
5.7
0.1
0.2
Crassocephalum crepidioides
10
2.7
2.7
Cynodon spp
11.4
2.0
3.3
Cynodon spp.
5
0.1
0.1
Cynoglossum lanceolatum
14.3
0.3
0.5
Cyperus cyperoides
28.6
2.9
4.7
Cyperus erectus
28.6
2.5
4.1
Cyperus esculentus
5.7
0.1
0.2
Cyperus rotundus
5.7
0.2
0.3
Cyprus cypriodes
5
1.3
1.3
Cyprus rotundus
5
0.2
0.3
Datura stramonium
5
0.2
0.2
Datura stramonium
2.9
0.6
1.0
Digitaria abyssinica
5.7
0.3
0.5
Echinochloa colona spp
5.7
0.3
0.5
Echinocloa colona
5
0.2
0.2
Galinsoga parviflora
57.2
4.8
7.8
Galinsoga parviflora
5
0.7
0.7
Guizotia abyssinica
2.9
0.3
0.5
Hydrocotyle american
77.2
14.2
23.1
Hydrocotyle Americana
45
31.3
31.7
Hygrophila auriculata
5
0.2
0.2
Kyllinga bulbosa
25
4.1
4.2
Nicandra physalodes
2.9
0.1
0.2
Paspalum conjugatum
2.9
0.1
0.2
Plantago lanceolata
11.4
0.8
1.3
Plantago laneolata
5
0.5
0.5
Polygonum nepalense
45
14.1
14.3
Polygonum spp
34.3
2.0
3.3
Portulaca oleracea
5.7
0.4
0.7
Rumex abyssinicus
5
0.1
0.1
Solanum incanum
2.9
0.3
0.5
Solanum nigrum
5
0.2
0.2
Solanum nigrum
2.9
0.1
0.2
Xanthium strumarium
2.9
0.1
0.2
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The weed aluminium accumulators that evidence that suitability of tea in the Cordillera,
in the report of Agricultural scientists Dr. Peter Bagawen and Gr acelyn Marcos of the
Kapangan Municipal Agricultural Office
Ageratum conyzoides Amaranthus dubius
Biden spilosa Cyperus erectus
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Hydrocotyle Americana
Commelina benghalensis
Caylusia abyssinica Cynodon spp.
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Corchorus olitorius Amaranthus hybrids
Cyperus cyperoides Cyperus rotundus
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Cynoglossum lanceolatum Digitaria abyssinica
Cyperus esculentus Echinocloa colona
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Crassocephalum crepidioides
Datura stramonium
Coniza albida
Echinochloa colona spp
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Hygrophila auriculata Kyllinga bulbosa
Polygonum nepalense
Paspalum conjugatum
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Portulaca oleracea
Polygonum spp
Solanum nigrum
Solanum incanum
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Guizotia abyssinica
Galinsoga parviflora
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YIELD ENVELOPE Y3
Tea bush growth is robust in atmospheric conditions between 20°C and 30°C.
Temperatures above 35°C and below 10°C are harmful for the bush. Tea yield and quality
are excellent under heavy rainfall between 150 cm and 250 cm; and in substrate rich in
humus, typically a mixture of lime74 and iron75. Tea is a water-loving plant commonly
grown on the windward side of the mountain range, granting cultivation areas are well
drained land and without water residue. Tea grown in higher altitude tends to exhibit
many desirable traits and often fetch a higher price as compared to teas grown in lower
altitudes. Some conditions become too cold to grow tea especially above 2400m elevation76.
Yield-envelope establishes probable climatic conditions in the localities for the
determination of an overall effect on yield, if any. The stochastic abstraction for tangible
limit computed across a probability space of 120 months for temperature, relative
humidity and rainfall as recorded from the DOST PAG-ASA data for the past ten years,
2010-202077.
The atmospheric temperature ideal for tea growth is within 20 and 30 degrees Celsius. No
temperatures should go above 35 degree Celsius and below 10 degrees Celsius. For the
past 20 years, monthly climatic data provided by DOST PAG-ASA state there has been no
incidence of these localities to have temperature outside the ideal ecological envelope.
Altitude or elevation largely affects regional or local climate. Rainfall increases at higher
elevations, temperatures generally is variable and h umidity becomes less. This c an be
attributed to a wide range of processes such as orographic lift. At higher altitudes, air
pressure is low and air expands as it rises, making it unable to hold all its water vapour
resulting to the formation of clouds. This occurrence often effects precipitation in the form
of snow or rain, defined as the orographic lift. It explains why the wind facing or
windward side of a mountain has high precipitation, and the leeward side tends to be
dry78 . Seasonality of precipitation is another important factor in determining the tea
variations, and leaves h arvested during different seasons produce a finished product with
different characteristics. This clarifies why many regions that are well-known for tea
production have strongly seasonal climates79.
In terms of the ecological apt; barrio Bokod averages humidity between 75 and 81 percent;
and in barrio Kapangan humidity averages between 78 and 88 percent. Temperature in
Bokod is between 14oC and 25oC; in Kapangan is between 18oC and 25oC. Rainfall in
Bokod averages 140 cm, while Kapangan averages 232 cm. These vicinities under study
experience the most rainfall in the month of August, with the highest recorded rainfall
over the past ten years, in August 2012 for 2021 mm. The compilation of linear data set
on DOST weather data from 2012 is located in Chart 1 5. Strong rains between 1003 mm
and 2021 mm extend into the months of September and October; and had recorded to
74 Ashim Kumar Saha. (2015). Requirement of lime in tea soil to improve tea growth and yield . Chittagong: Bangladesh Tea
Research Institute Soil Science Division.
75 Chen Yulong, Yueming Jiang, Jun Duan, John Shi, Sophia Xue, and Yukio Kakuda (2010) Variation in catechin contents in
relation to quality of ‘Huang Zhi Xiang’ Oolong tea at various growing altitudes and seasons. Food Chemistry, 119 (2) 648 -652.
76 Bo Wen, Shuang Ren, Yanyuan Zhang, Yu Duan, Jiazhi Shen, Xujun Zhu, Yuhua Wang, Yuanchun Ma, Zhongwei Zou and
Wanping Fang. (2020). Effects of geographic locations and topographical factors on secondary metabolites distribution in green
tea at a regional scale. Food Control, Volume 110.
77 Climate data Free Issue Form Reference A-052022-069 Approved 11 June 2022
78 Fanqiao Menga,Yuhui Qiaoa, WenliangWua, Pete Smith and Steffanie Scott (2017) Environmental impacts and production
performances of organic agriculture in China: A monetary valuation. Journal of Environmental Management, 49 -57.
79 Anna Nowogrodzki. (2019) How climate change might affect tea. Nature Briefing, 3
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start out early i n the month of July in years 2019, 2016 and 2013. Rainfall is belo w the
ideal volume for tea cultivations at 150-250 mm of the past ten years, months of October,
November and December. Between January and April, rainfall is below 150 mm for the
past ten years; averaging for the month of January 19 mms, February 21 mms an d March
44 mms. The other months with averages of low rainfall are November 91.5 mm,
December 63.1 mms and April 109 mms.
CHA RT 15/ CROS S-SECTIONAL CLIMATE DATA SET
Rainfall
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
January
0.0
96.0
17.5
11.4
0.0
11.3
5.2
0.0
15.2
3.4
14.4
February
0.0
13.8
80.8
26.8
0.0
7.3
4.2
71.5
1.2
0.0
4.4
March
15.3
93.4
151.9
63.6
5.9
57.1
9.4
4.7
5.8
12.8
20.6
April
148.6
11.9
72.6
70.3
126.3
122.0
62.0
61.3
204.0
97.4
113.0
May
242.6
462.5
187.7
338.7
213.0
245.5
213.3
570.1
283.2
393.5
293.2
June
254.0
529.1
659.0
232.8
401.7
282.5
176.3
208.5
552.6
274.1
336.0
July
543.7
427.5
1,020.0
368.2
444.2
1,493.9
426.8
751.0
1,002.5
437.7
345.6
August
536.6
1,096.3
2,200.7
1,220.4
531.9
1,031.6
955.6
449.6
1,822.6
1,525.2
398.8
September
296.8
619.7
288.3
590.1
985.4
263.6
412.1
206.9
1,219.6
739.9
226.2
October
920.1
332.4
72.4
240.0
107.1
1,212.2
583.2
230.0
268.6
136.6
282.9
November
226.4
81.6
57.8
53.5
39.2
8.0
23.2
120.0
17.8
121.2
166.5
December
47.4
67.4
10.8
23.6
9.5
167.1
82.0
28.4
22.8
16.5
155.6
Temp-Max
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
January
23.3
22.8
23.9
23.8
21
21.8
23.2
0
23.3
23.2
23.8
February
24.9
23.6
23.7
25.5
22.4
22.1
22.8
22.2
24.2
23.7
23.9
March
25.6
24.4
24.7
25.5
24.1
23.8
24
24.4
24.4
25
25.7
April
26.9
25.7
26.2
26.8
24.4
24.4
26.2
25.1
24.3
25.6
25
May
26.1
25
25.6
25.1
25
24.5
25
24.6
24.9
24.4
24.4
June
25.1