LUCTOR: Land Use Crop
Technical Coefficient
Generator; version 2.0
A model to quantify cropping systems in
the Northern Atlantic zone of Costa Rica
H. Hengsdijk, A. Nieuwenhuyse & B.A.M. Bouman
grafiek
PE
vignet
ab-dlo
vignet
Quantitative Approaches in Systems Analysis
The Quantitative Approaches in Systems Analysis series provides a platform for publication and
documentation of simulation models, optimization programs, Geographic Information Systems
(GIS), expert systems, data bases, and utilities for the quantitative analysis of agricultural and
environmental systems. The series enables staff members, students and visitors of AB-DLO and PE
to publish, beyond the constraints of refereed journal articles, updates of models, extensive data
sets used for validation and background material to journal articles. The QASA series thus primarily
serves to support peer reviewed articles published elsewhere. The inclusion of listings of programs
in an appendix is encouraged.
All manuscript are reviewed by an editorial board comprising one AB-DLO and one PE staff
member. The editorial board may consult external reviewers. The review process includes
assessing the following: relevance of the topic to the series, overall scientific soundness, clear
structure and presentation, and completeness of the presented material(s). The editorial board
evaluates manuscripts on language and lay-out matters in a general sense. However, the sole
responsibility for the contents of the reports, the use of correct language and lay-out rests with the
authors. Manuscripts or suggestions should be submitted to the editorial board. Reports of the
series are available on request.
Quantitative Approaches in Systems Analysis are issued by the DLO Research Institute for
Agrobiology and Soil Fertility (AB-DLO) and The C.T. de Wit Graduate School for Production
Ecology (PE).
AB-DLO, with locations in Wageningen and Haren, carries out research into plant physiology, soil
science and agro-ecology with the aim of improving the quality of soils and agricultural produce and
of furthering sustainable production systems.
The 'Production Ecology' Graduate School explores options for crop production systems associated
with sustainable land use and natural resource management; its activities comprise research on
crop production and protection, soil management, and cropping and farming systems.
Address for ordering copies of volumes in the series:
Secretariat
TPE-WAU
Bornsesteeg 47
NL-6708 PD Wageningen
Phone:
Fax:
(+) 31 317.482141
(+) 31 317.484892
E-mail:
office@sec.tpe.wau.nl
Addresses of editorial board (for submitting manuscripts):
H.F.M. ten Berge
M.K. van Ittersum
AB-DLO
TPE-WAU
P.O. Box 14
Bornsesteeg 47
NL-6700 AA Wageningen
NL-6708 PD Wageningen
Phone:
(+) 31 317.475951
Phone:
(+) 31 317.482382
Fax:
(+) 31 317.423110
Fax:
(+) 31 317.484892
E-mail:
h.f.m.tenberge@ab.dlo.nl
E-mail:
martin.vanittersum@staff.tpe.wau.nl
LUCTOR: Land Use Crop
Technical Coefficient
Generator; version 2.0
A model to quantify cropping systems in
the Northern Atlantic zone of Costa Rica
H. Hengsdijk, A. Nieuwenhuyse & B.A.M. Bouman
PE
Quantitative Approaches
in Systems Analysis No. ..
Month 1997
ab-dlo
CIP-DATA KONINKLIJKE BIBLIOTHEEK, DEN HAAG
Hengsdijk, H., A. Nieuwenhuyse & B.A.M. Bouman
LUCTOR: Land Use Crop Technical Coefficient Generator; version 2.0 /
H. Hengsdijk, A. Nieuwenhuyse, B.A.M. Bouman. - Wageningen : DLO Research Institute for
Agrobiology and Soil Fertility ; Wageningen : The C.T. de
Wit Graduate School for Production Ecology. (Quantitative approaches in systems analysis ; no. XX)
ISBN 90-73384-34-6
NUGI 835
Subject headings: technical coefficient / cropping systems ;
Costa Rica.
Keywords
technical coefficient generator, cropping systems, input-output coefficients, land use, Costa Rica
Guidelines 'Quantitative Approaches in Systems Analysis'
Manuscripts or suggestions should be submitted to the editorial board (H.F.M. ten Berge,
AB-DLO, or M.K. van Ittersum, TPE-WAU). The final version of the manuscripts should be delivered
to the editors camera-ready for reproduction. The submission letter should indicate the scope and
aim of the manuscript (e.g. to support scientific publications in journals, program manual,
educational purposes). The costs of printing and mailing are borne by the authors.
The English language is preferred. Authors are responsible for correct language and lay-out.
Overall guidelines for the format of the texts, figures and graphs can be obtained from the
publication editor at AB-DLO, or from the PE office:
H. Terburg
Th.H. Jetten
AB-DLO
Secretariat C.T. de Wit Graduate School
for Production Ecology
P.O. Box 14
Lawickse Allee 13
NL-6700 AA Wageningen
NL-6701 AN Wageningen
Phone:
(+) 31 317.475723
Phone:
(+) 31 317.485116
Fax:
(+) 31 317.423110
Fax:
(+) 31 317.484855
E-mail:
h.terburg@ab.dlo.nl
E-mail:
theo.jetten@beleid.spp.wau.nl
Preface
This report describes the model LUCTOR (Land Use Crop and Technology generatOR), version
2.0. LUCTOR quantifies cropping systems in terms of inputs and outputs (technical coefficients) in
the Northern Atlantic zone of Costa Rica. The model has been developed in the project REPOSA
(Research Program on Sustainability in Agriculture) in Guápiles, Costa Rica. REPOSA is a cooperation between Wageningen Agricultural University (WAU), the Centre for research and
Education in Tropical Agriculture (CATIE), and the Ministry of Agriculture and Livestock, Costa Rica
(MAG). LUCTOR is complementary to PASTOR (PASture and livestock technical coefficient
generatOR; Bouman et al., 1998a), a model developed by REPOSA to generate technical
coefficients for pastures, herds and feed supplementing in cattle systems in the humid tropics.
The primary goal of LUCTOR is to generate technical coefficients for linear programming models to
analyse and explore sustainable land use options. However, the model is also useful as a
standalone tool for cost-benefit analysis or to support thinking about and design of new cropping
systems. LUCTOR has been especially developed for the Northern Atlantic zone of Costa Rica.
However, LUCTOR is set up in a generic manner so that, by adapting data files, users can make
LUCTOR suitable to other environments and crops.
The authors thank R. Schipper, P. Roebeling and F. Sáenz for discussions and suggestions during
the development of LUCTOR.
LUCTOR 2.0 can be obtained from:
until December 1998:
REPOSA
Apartado 224-7210
Guápiles
Costa Rica
Tel. (+506) 710 6595
Fax (+506) 710 2327
E-mail: Lquiros@sol.racsa.co.cr
After December 1998:
TPE-WAU
Bornsesteeg 47
NL-6708 PD Wageningen
The Netherlands
Tel.: (+31) 317 482141
Fax: (+31) 317 484892
E-mail: office@sec.tpe.wau.nl
Table of Contents
page
Preface
Samenvatting
Summary
1 Introduction
1.1
1.2
Use and type of technical coefficients in different LP-models
Scope and structure of the report
2 Definition of land use activities
2.1
2.2
2.3
2.4
2.5
Applied approach and concepts
2.1.1
Applied approach
2.1.2
Concepts
Agro-ecological sustainability and environmental impact of land use activities
2.2.1
Agro-ecological sustainability
2.2.2
Environmental impact indicators
Annual crops
2.3.1
Type of activity
2.3.2
Yield level
2.3.3
Type of soil
2.3.4
Crop type
2.3.5
Mechanisation level
2.3.6
Crop residue strategy
2.3.7
Herbicide level
2.3.8
Biocide level
2.3.9
Start of growing season
Perennial crops
2.4.1
Type of activity
2.4.2
Crop type
2.4.3
Mechanisation level
2.4.4
Crop residue strategy
2.4.5
Crop cycle
2.4.6
Biocide level
Plantation forestry activities
3 Technical Coefficient Generator
3.1
3.2
3.3
3.4
Structure of LUCTOR
Concepts and conventions used in LUCTOR
Installation and operation of LUCTOR
Generation of technical coefficients
3.4.1
Manual generation of TCs
1
1
2
5
6
6
7
8
8
8
9
10
10
10
11
12
12
12
13
13
13
14
14
14
15
15
15
15
17
17
19
20
23
23
3.5
3.4.2
Automatic generation of TCs
Customising output files for LP-models
4 Annual activities
4.1
4.2
4.3
4.4
Target yield
Production techniques
4.2.1
Operation periods
4.2.2
Labour requirements
4.2.3
Mechanisation and implement requirements
4.2.4
Crop residue strategy
4.2.5
Variable inputs
Nutrient balances and requirements
4.3.1
Loss processes
4.3.2
Supply processes
4.3.3
Operationalisation of nutrient balances
Costs
5 Perennial activities
5.1
5.2
5.3
5.4
Target yield
Production techniques
5.2.1
Operation periods
5.2.2
Labour, mechanisation and implement requirements
5.2.3
Crop residue strategy
5.2.4
Variable inputs
Nutrient balances and requirements
Costs and discounting of TCs
6 Plantation forestry activities
6.1
6.2
6.3
Target yield
Production techniques
Nutrient balances and requirements
References
25
28
31
31
32
32
33
34
34
35
35
37
39
39
44
45
45
46
46
46
47
48
49
53
55
55
56
57
61
Appendix I: LUCTOR files
2 pp.
Appendix II: Numbers and codes of definition criteria options
1 pp.
Appendix III: Identification codes of land use activities
2 pp.
Appendix IV: Crop parameters
17 pp.
Appendix V: Crop protection agents
2 pp.
Appendix VI: Prices of inputs and outputs
3 pp.
1
Samenvatting
Dit rapport beschrijft het model LUCTOR (Land Use Crop Technology generatOR), versie 2.0.
LUCTOR kwantificeert gewassystemen in termen van inputs en outputs (technische coefficienten)
voor de Noord Atlantische zone van Costa Rica. Technische coefficienten van gewassystemen
omvatten productie (opbrengst in kwaliteitsklassen), kosten van productie, arbeidsbehoefte,
nutrientenbehoeften, behoefte aan gewasbeschermingsmiddelen, inzet van werktuigen, en
nutrienten verliezen naar het milieu. Het model is in staat technische coefficienten te genereren
voor eenjarigen (mais, bonen, cassave en ananas), meerjarigen (banaan, plataan, en palmhart) en
plantage bos (teak en melina). Gewassystemen worden gekararkteriseerd aan de hand van
zogenaamde ‘definitie criteria’ die zowel milieu als management opties omvatten. LUCTOR kan
zowel actuele als alternatieve gewassytemen kwantificeren. Laatstgenoemde zijn technisch
haalbare gewassystemen die nog niet algemeen worden toegepast in de Noord Atlantische zone en
die worden gekenmerkt door een nutrientenbalans die in evenwicht is. Om de inputs en outputs van
deze alternatieve gewassystemen te kwantificeren is de zogenaamde ‘doel-georienteerde
benadering’ toegepast. Hierbij wordt een optimale combinatie van inputs geidentificeerd die nodig is
om een zeker opbrengst-niveau te behalen, gebaseerd op kennis van onderliggende gewasgroei.
Processen an relaties die gebruikt zijn voor de kwantificering van technische coefficienten zijn
gebaseerd op (i) basis informatie over bodems, klimaat, gewassen, gewasbeschermingsmiddelen
en prijzen, (ii) systeem analytische methoden en (iii) gekwantificeerde kennis van experts.
Summary
This report describes the model LUCTOR (Land Use Crop and Technology generatOR), version
2.0. LUCTOR quantifies cropping systems in terms of inputs and outputs (technical coefficients) for
the Northern Atlantic zone of Costa Rica. Technical coefficients of cropping systems include
production (yield in different quality classes), costs of production, labour requirements, nutrient
requirements, required crop protection agents, implement requirements, and nutrient losses to the
environment. The model is able to quantify technical coefficients of annuals (maize, beans and
cassava, pineapple), perennials (banana, plantain and palmheart) and plantation forestry (teak and
melina). Cropping systems are characterised by so-called definition criteria that encompass
environmental and management options. LUCTOR can quantify both actual cropping systems and
alternative cropping systems. The latter are technically feasible production systems not yet widely
applied in the Northern Atlantic zone and characterised by an equilibrium nutrient balance. To
quantify inputs and outputs of alternative land use activities the so-called ‘target-oriented approach’
has been applied which means that an optimal combination of inputs is identified to realise a
particular output level, based on knowledge of (underlying) crop growth. Processes and
relationships underlying the quantification of technical coefficients are based on (i) basic information
on soils, climate, crops, crop protection agents and prices, (ii) systems analytical approaches, and
(iii) quantified expert knowledge.
1
1
Introduction
REPOSA (Research Programme On Sustainability of Agriculture) is an interdisciplinary research
programme aimed at the development and application of methodologies for analysis and
exploration of alternative land use scenarios for agro-ecological sustainable and economically
viable land use at farm, and (sub) regional level.
Within the programme several research methodologies have been developed which use Linear
Programming as an integrative tool. Linear programming techniques in land use analysis can be
used to optimise land use subject to a set of constraints (Hazell & Norton, 1986). Land use activities
are well defined and quantified means of agricultural production in which a unique combination of
inputs results in a unique mixture of agricultural outputs. Land use activities may include growth of
arable crops, perennials, forestry, vegetables, animal husbandry, etc. Outputs of land use activities
include desired (e.g. marketable products or crop residues for fodder) as well as undesired outputs
(e.g. depletion of soil nutrients, biocide emissions to the environment), while inputs include for
example labour, equipment, nutrients, biocides and seed. Subsequently, these in-and outputs can
be expressed into economic terms using prices. In-and outputs of land use activities are defined per
hectare. LP-models allow analysing the interaction among different types of land use activities at
higher aggregation levels, such as farm and region plus their relative importance in contributing to
objectives at these higher scale levels.
One of the pilot areas to test and evaluate the methodologies developed within the REPOSA-project
is the Northern Atlantic zone of Costa Rica. To characterise land use activities in this zone a socalled Technical Coefficient Generator has been developed which allows quantifying their in-and
outputs. The term ‘technical coefficients (TC)’ refers to the in-and outputs of land use activities,
which can be generated with the model. The TCG, which is called LUCTOR (Land Use Crop
Technical coefficient generatOR), is a model that integrates basic data on soils, crop protection
agents, crop characteristics, labour and implement requirements for field operations, documented
calculation rules and explicit formulated assumptions. Land use activities are put into a theoretical
framework in which efficiencies of their in-and outputs are formulated in relation to predefined
production orientations (e.g. agriculture aiming at high productivity or low environmental pollution)
(REPOSA, 1996). LUCTOR is part of a family of Technical Coefficient Generators that has been
developed the last years for a number of explorative land use studies (e.g. Hengsdijk et al., 1996;
Quak et al, 1996; Habekotté, 1994). This report describes the different components of LUCTOR as
developed for the Northern Atlantic zone of Costa Rica. Since LUCTOR has a modular approach it
can relatively easily be made operational in other regions.
1.1
Use and type of technical coefficients in different
LP-models
Different types of Linear Programming models exist, each with their own purpose and spatial and
temporal scales. Within the framework of REPOSA several types of such LP-models are developed.
Their purpose determines to a large extent which and how cropping systems must be quantified.
One of the LP-models developed is aimed at exploring agro-ecologically sustainable land use
options for the Northern Atlantic zone in Costa Rica in the long term (Bouman et al., 1998b). It is
used to explore the agro-ecological sustainable land use options given societal objectives related to
2
land use. This model requires land use activities that are technically feasible and from an agroecological point sustainable, but not necessarily widely applied in the zone. It may be assumed that
such activities use production factors more efficiently than current land use systems due to
supposed future efficiency gains in agricultural production. In LUCTOR nutrient balances are used
to quantify agro-ecological objectives related to sustainability. Agro-ecological sustainability of crop
activities is operationalised in terms of a balanced nutrient supply, i.e. macroelements (N, P and K)
withdrawn from the system are replenished by inputs from various external resources (e.g.
fertilisers). This implies that productivity of these land use activities is maintained over time. These
agro-ecologically sustainable activities are referred to as alternative land use activities, indicating
their technical feasibility and innovative character.
Another type of model is developed within the UNA/DLV1 project, a co-operation between the
National University of Costa Rica and Wageningen Agricultural University, that is closely associated
with REPOSA. This project aims at identification of policy instruments that are effective to induce
changes in land use given farmers objectives and aspirations (Sáenz et al., 1997). For this purpose
linear programming techniques are applied at farm level with a methodology that also uses
econometric techniques. Due to the incorporation of econometric (behavioural) relationships in
these type of LP-models their time horizon is much shorter than in explorative LP-models. In such
short-term studies, future efficiency gains as assumed in alternative activities are less presumably,
and land use activities that represent current means of production are required. Often, but not
necessarily, such land use activities are unsustainable in terms of nutrient balance and may either
be soil depleting or soil enriching. In this case, quantification of actual land use activities is required,
activities that represent the current means of production and incorporate changes in production
techniques that can be expected to be realized on short-term only.
LUCTOR is developed to support both types of LP-models, therefore, both alternative and actual
cropping activities are modelled.
1.2
Scope and structure of the report
This report describes the procedure used for quantification of annual and perennial crops, and
plantation forestry activities for the Northern Atlantic zone of Costa Rica. It describes the used basic
data, assumptions, calculation rules as well as the structure of LUCTOR, i.e. how it can be used
and adjusted by third parties. In the text, reference is made to the most important variable and
parameter names (in italics) used in LUCTOR, so that procedures described in this report can easily
be traced down in the model by users. This report is a combination of a manual, reference book and
a description of used technical relationships.
In Chapter 2 the general approach of defining land use activities is described. This chapter is meant
for readers interested in a brief introduction to LUCTOR, the applied approach and the main
characteristics of the land use activities that can be defined. More technical details are described in
subsequent chapters.
Chapter 3 describes the structure of LUCTOR and how it can be operated. This Chapter is meant
for those who want to directly use LUCTOR and those who want to make own adjustments in the
model. LUCTOR is used to quantify technical coefficients for LP-models developed within REPOSA
1 DLV is the Dutch abbreviation for ‘Duurzaam Landgebruik en Voedselvoorziening in de tropen’ (Sustainable land use and
food security in developing countries’
3
and UNA/DLV. Since requirements of both models differ, customised output format is defined and
an interface is available for both LP-models that is described in this Chapter.
In the subsequent three Chapters, data, relationships and assumptions used to quantify activities
for annual crops, perennial crops, and plantation forestry are described, respectively. Yield levels,
production techniques and nutrient balances and requirements are discussed in detail. Since,
relationships used to quantify annual, perennial and plantation forestry systems do not differ much,
Chapter 4 (annual activities) contains the most comprehensive information. For perennials (Chapter
5) and plantation forestry (Chapter 6) only differences in approach compared to that of annuals are
discussed.
4
5
2
Definition of land use activities
For identification of sustainable land use options in the Northern Atlantic zone of Costa Rica five
major categories are distinguished: annual crops, perennial crops, plantation forestry, animal, and
pasture activities. So-called definition criteria that encompass environmental and management
options characterise each type of land use activity. In the subsections 2.3, 2.4 and 2.5 the definition
criteria for annual crops, perennial crops and plantation forestry are described in more detail,
respectively. For each unique and feasible combination of definition criteria, a production activity
and its in- and outputs per hectare are determined. The characterisation of animal and pasture
activities is described in Bouman et al. (1998a).
Processes and relationships underlying the in-and outputs of land use activities are based on (i)
basic information on soils, climate, crops, crop protection agents and prices, (ii) systems analytical
approaches, and (iii) quantified expert knowledge. For some data and processes the required
knowledge is lacking or insufficient to generalise it into models. In such cases assumptions and
knowledge of experts have been used, e.g. for the quantification of denitrification, volatilisation,
yield reductions due to diseases and pests, length of development stages of crops, labour
requirements for weeding, etc. In this sense, LUCTOR can be considered an expert-system that
‘forces’ field experts to be explicit about their knowledge, and to make that knowledge transparent
and open to critical review by other experts. The advantage is that such knowledge is not left
unused simply because it can not (yet) be formalised into process models (Jansen & Schipper,
1995).
The data, underlying processes and relationships of land use activities have been combined and
modelled in LUCTOR which integrates the information and calculates the required in -and output
coefficients of land use activities. LUCTOR allows to adjust data and assumptions whenever new
information and insights come available. In this way effects of arbitrary but verifiable assumptions
on quantitative characteristics of land use activities can be analysed. LUCTOR is therefore not only
an instrument to generate coefficients for LP-models but can also be a tool to support thinking about
and design of new production systems. See for a comprehensive explanation of its use and modular
structure Chapter 3.
LUCTOR is not a management model that can be used for operational decision making nor a crop
growth simulation model although results of the latter type of models can be used as input data in
LUCTOR. The time step of activities is one year and is not suited for daily decision making. Nor
does the model take into account spatial variation in soils and temporal variation in weather, which
encounter farmers in daily life. LUCTOR describes different types of land use systems in complete
operation sequences that include a quantification of all inputs and outputs during these operation
sequences. Although the set of TCs is the final result of each land use activity the processes and
steps leading to the result can be studied as well with LUCTOR. Underlying biophysical processes
that are of relevance to characterise land use activities are modelled, for example nutrient
dynamics. The reasoning behind the input and output relationships is therefore a comprehensive
part of the model.
6
2.1
Applied approach and concepts
2.1.1
Applied approach
To quantify inputs and outputs of alternative land use activities the ‘target-oriented approach’ has
been applied which means that an optimal combination of inputs is identified to realise a particular
output level, based on knowledge of (underlying) crop growth (Van Ittersum & Rabbinge, 1997).
This implies that first a yield level is determined and subsequently the inputs, i.e. nutrients, labour
and mechanisation requirements to attain and maintain this yield level in the long run. With regard
to the soil nutrient stocks of alternative land use activities an equilibrium situation is assumed which
means that the annual nutrient withdrawal and inevitable losses are replenished with nutrients from
natural resources and fertilisers (see also section 2.2). Yield levels of alternative land use activities
can be calculated using crop growth simulation models (de Koning et al., 1995). Since reliable
climatological data for the Northern Atlantic zone and validated crop growth simulation models for
most prevailing crops in the zone were lacking, yield levels of alternative activities are based on
expert knowledge in LUCTOR. For most annual crop activities (maize, beans and cassava) the
estimated maximum yield levels are twice as high as those currently attained. For perennial crops
such as banana and pineapple for export purposes the yield gap between actual and alternative
activities is smaller since current production systems attain already relatively high yields.
For the definition of actual crop activities, a similar approach is followed as for alternative activities,
however, in this case the actual yield level and inputs such as nutrients, labour and crop protection
agents are used to determine other outputs such as nutrient balances and biocide indices (see
paragraph 2.2.2). In some cases data collection of actual systems is inadequate to fully quantify all
inputs of such systems. Hence, also for these systems other inputs such as labour have been
quantified in a target-oriented way using standard task times. The empirically derived yield levels
and inputs are based on various sources, which are indicated in the following Chapters. The major
difference in approach with alternative activities is that nutrient balances of actual crop activities are
a result of loss and gain processes that can be positive or negative while nutrient balances of
alternative crop activities are used as an equilibrium target. In other words, for alternative crop
activities the nutrient input is a resultant of the calculations and for actual crop activities the nutrient
balance is a resultant.
It is assumed that production factors in alternative activities can be applied in a more technically
efficient manner, which is expressed in the following specific points:
Crop characteristics of alternative crop activities are geared towards higher yields compared to
annual activities (i.e. higher harvest indices).
In alternative perennial activities distribution of fruit quality shifts towards higher percentages
first quality fruit due to better crop management.
In general alternative crop activities have higher plant densities.
Fertiliser applications occur timely and more frequently.
Since different yield leves are defined for both actual and alternative activities (see paragraph 2.3.2)
the approach applied implies that alternative crop activities not necessarily have higher yields than
actual crop activities. However, it does mean that alternative crop activities can be practised
theoretically for years to come, while in most actual crop activities due to their depleting effect on
the soil nutrient stock can not be considered sustainable options in the long run.
7
In principle, input and output relationships are expressed per hectare and are scale independent.
However, it is obvious that the rate of return of capital intensive inputs such as machinery will
depend on the scale to which they can be applied. A tractor is not profitable when only used for a
few hectares, but used for larger areas it may be profitable. Therefore, the costs for machinery are
based on rent prices. However, for products that need an on-farm post-harvest processing unit and
specialised infrastructure (e.g. cable systems, drainage, etc. in banana) such an approach is not
possible. To calculate the costs of this type of capital inputs (per hectare) it is implicitly assumed
that such production factors serve an area of about 200-300 ha and that they are utilised optimally.
2.1.2
Concepts
Since land use must comply with various (often) conflicting societal objectives, land use activities
have to be defined that may contribute to the various objectives (Van Ittersum & Rabbinge, 1997).
Inputs and outputs of land use activities for a given crop are completely determined by the physical
environment and the production technique, i.e. the means that determine how a certain yield level is
attained and includes e.g. the required nutrients, crop protection agents and labour. Soil types and
climate represent the physical environment. In this study no different climatological sub zones have
been identified for the Northern Atlantic zone since climatological conditions in the zone do not vary
much. This assumption is confirmed by Bessembinder (1997) who did not find significant
differences in potential production using a crop growth simulation model, also due to the absence of
reliable climatological data.
Since different combinations of production factors can result in identical crop yields substitution of
different types of inputs is possible to a certain extent (De Wit, 1993), which from a viewpoint of
optimal resource allocation has to be taken into account in the definition of land use activities.
Disregarding such substitution possibilities would block prospects for development in certain
directions and would neglect basic economic principles. Examples of such substitution possibilities
include herbicides versus manual weeding, various uses of crop residues and different sowing
dates. The latter can be considered as substitution of production factors in time while nutrients
removed with harvesting of crop residues can be replaced with fertilisers.
Other substitutions of inputs are usually only possible when a yield reduction is accepted. Examples
are the reduced use of biocides through better crop monitoring and more hygienic measures, and
mechanised soil preparation substituted with manual soil preparation that on the one hand limits the
use of capital goods but on the other hand results in less favourable growing conditions reducing
the yield level.
Many other substitution possibilities of production factors are not possible which can be explained
from the physiological function of various production factors within plants, for example radiation
required for photosynthesis can not be substituted by water, and nitrogen can not substitute
carbondioxide. Partial substitution between some of these production factors is sometimes possible,
e.g. between nitrogen and phosphate. However, it neglects basic principles of sustained production:
The application of nitrogen only may increase yields in the short run, but at the same time
decreases phosphorus soil reserves due to an increased phosphorus uptake in the harvested
produce. This can be explained by a change in soil fertility due to crop production. In the long run
such management methods and consequently crop systems run into trouble. Breman (1992) has
demonstrated this theoretical understanding in semi-arid areas where the introduction of N-fixing
species could not offer a long-term solution to soil fertility problems due to an increased phosphorus
deficiency in the soil.
8
2.2
Agro-ecological sustainability and environmental
impact of land use activities
Because methodologies developed within REPOSA aim at exploring and identification of
sustainable land use options, factors which affect agro-ecological sustainability objectives are
explicitly taken into account in the definition of land use activities. Agro-ecological sustainability in
this respect means that yields (in biophysical terms) are not jeopardised in the long run, i.e. the
production potential of a land use activity is not affected. In LUCTOR nutrient balances are used to
quantify agro-ecological objectives related to sustainability.
In addition to this process there are several other processes associated to land use that will not
directly influence the production potential, but do affect sustainability objectives at higher
aggregation levels. These processes include the use of crop protection agents, different types of
gas emissions, and nutrient losses through water flow (off-site effects). Environmental performance
of land use activities in LUCTOR is measured with indicators for the use of crop protection agents
and nutrient losses to the environment through leaching to surface and groundwater, and NH 3, N2O
and NO gas emissions.
2.2.1
Agro-ecological sustainability
Agro-ecological sustainability in this study is related to soil nutrient mining, which in the short or long
run will lead to a reduced production potential. In general, actual activities (see also paragraph
2.1.1) annually lose nutrients, which at the end will lead to a decline in productivity. Alternative crop
activities aim at an equilibrium nutrient balance so that productivity over time is maintained. These
activities are defined in such a way that the annually withdrawn nutrients from the system due to
removal of marketable products and/or crop residues and inevitable losses (leaching, denitrification
and volatilisation) are replenished by nutrients in the annual supply from natural resources
(deposition and nutrient fixing micro-organisms) and in fertilisers. Due to the in general higher
productivity of alternative crop activities many nutrients are required to replenish the system. To
maximise efficiency of fertilisation it is assumed that fertiliser applications occur frequently (split in
stead of single applications) in alternative activities.
2.2.2
Environmental impact indicators
Environmental indicators can be divided into two groups according to their impact. The first group is
related to the emission of nitrogen to the environment due to leaching, volatilisation and
denitrification/nitrification. Leaching of nitrogen can result in pollution of surface and groundwater.
Volatilisation of N will result in acid rain while denitrification/nitrification losses as N 2O and NO will
add to greenhouse gasses. These processes are difficult to quantify and are estimated as a fraction
of the total N-input of a land use activity, differentiated per soil type.
Major emphasis is placed on quantification of a second group of environmental impact indicators
related to the use of crop protection agents. Use of crop protection agents may result in pollution of
water and soil resources and may directly affect human health. These substances prevent or cure
crops damage caused by yield reducing factors (insects, fungi, weeds and nematodes) and include
9
the more common elements: herbicides (against weeds), fungicides (against fungi), insecticides
(against insects) and nematicides (against nematodes). The common denominator to encompass
various types of crop protection agents is kilogram active ingredient (a.i.). Besides this indicator the
amount of active ingredients supplied is expressed in a so-called biocide index (Jansen et al.,
1995). In this study herbicides are considered a separate category of crop protection agents since it
is assumed that herbicides can be substituted by manual weeding methods without yield losses. For
other types of crop protection agents, biocides, no adequate substitutes are available without
accepting a yield decline. With better crop management (e.g. resistant plant material, application of
agents only when threshold damage levels are surpassed, hygienic measures, etc.) biocide use can
be reduced. However, in the humid and warm climate of the Northern Atlantic zone yield reductions
are assumed to be unavoidable for most crops. The consulted crop specialists on banana argued
that with the high risk of infection with Sigatoka a reduced use of fungicide will almost certainly
result in a complete failure of the crop in economic terms. Even when a yield is obtained the high
quality standards for these export crops can not be met. Only for annual crops a low biocide option
is defined in which the use of fungicides and insecticides is reduced with 50% due to better crop
monitoring and hygienic measures, requiring extra labour. However, in this option a yield reduction
is unavoidable and, therefore, crop specific yield reductions are defined. A reduced use of
nematodes is considered unfeasible since soil born diseases can only be controlled without
nematicides by means of appropriate crop rotations. For perennial crops this is not a feasible
option, while for annuals crop rotation related diseases are not taken into account.
2.3
Annual crops
In Table 2.1 the applied definition criteria for annual crop activities and options for each criterion are
shown. Theoretically, these nine definition criteria can be combined in any combination which
means that 2 types of activities, 11 yield levels, 3 soils, 6 crops, 2 mechanisation levels, 2 crop
residue strategies, 2 herbicide levels, 2 biocide levels and 12 dates per year at which crops can be
sown/planted can result in 76032 unique annual crop activities. However, not all combinations are
feasible due to ‘technical’ constraints: soils can be unsuitable to grow certain crops, some crop
residues may not be suitable to be used as fodder after harvesting, crops can only be sown in
certain periods of the year, etc. Moreover, ten of the eleven identified yield levels only apply to
alternative activities.
With the exception of pineapple, which in this study is used as an annual and biannual crop, a
growth calendar is used, that defines how long certain operation periods (field preparation, sowing,
maintenance and harvesting) may last. This enables to identify labour peaks in periods that the
required labour days exceed the available days, indicating that more than one person is required to
finish the operations in that period properly. The labour requirements of pineapple are spread
evenly over the entire year. TCs of pineapple ratoon crop are discounted in a similar way as TCs of
perennial crops.
10
Table 2.1 Definition criteria and the distinguished options for annual land use activities.
Definition criterion
maximum number of options
1. type of activity
2 (actual and alternative)
2. yield level
11 (10 target yield levels for alternative activities, 1 yield level for actual activities)
3. soil type
3 (fertile poorly drained (SFP), fertile well drained (SFW), infertile well drained (SIW))
4. crop type
6 (bean, cassava, maize-grain, maize cobs, pineapple-export, pineapple-local)
5. mechanisation level
2 (low and high mechanisation level)
6. crop residue strategy
2 (harvesting, left at field)
7. herbicide level
2 (low and high herbicide level)
8. biocide level
2 (low and high biocide level)
9. start of growing season 12 (each month)
2.3.1
Type of activity
Actual and alternative crop activities differ in terms of nutrient balances, crop characteristics and the
way some production factors are applied (see also sections 2.1 and 2.2). Actual crop activities
represent current annual production systems in the region, which in general have nutrient balances,
which are not in equilibrium. Alternative crop activities aim at an equilibrium nutrient balance so that
productivity over time can be maintained.
Other differences between actual and alternative crop activities relate to crop characteristics geared
towards higher yields and a more sound use of input factors, such as timely and more frequent
application of fertilisers and crop protection agents, higher plant densities, etc. in alternative
activities
2.3.2
Yield level
For alternative crop activities 10 yield levels are defined. The maximum yield level is based on
expert knowledge.They estimate it on average as twice the current attained yield level in the zone.
The other nine yield levels are derived by a stepwise 10% reduction of the maximum yield. The
lowest level is 10% of the maximum yield level. Although yield levels are defined at an equidistant
range, other outputs and inputs are not. It is assumed that nutrient concentrations increase with
higher yield levels (Van Keulen & de Wolf, 1986). In this way non-linear (diminishing return)
relationships could be derived between nutrient requirements and yield levels. For insecticides and
fungicides it is assumed that their use decreases proportionally with diminishing yield levels based
on De Wit (1994) who suggests that a number of fungal diseases and insects pests require less
effort to be controlled under less favourable growing conditions. Other inputs are kept constant or
relative to the yield level, such as the labour requirements for harvesting.
2.3.3
Type of soil
Three soil types have been identified: Fertile well-drained, fertile poorly drained and infertile welldrained soils. These soil types have been characterised in earlier stages of the REPOSA-project
11
(Nieuwenhuyse, 1996; Schipper, 1996). Soil characteristics determine which soils are suitable to
grow a crop and they also affect nutrient losses and thus nutrient recovery of crops. In Table 2.2 a
summary is given of some soil characteristics.
Table 2.2
Characteristics of three soils (Jansen et al., 1995).
Characteristic
Fertile well drained
Fertile poorly drained
Infertile well drained
(SFW)
(SFP)
(SIW)
Bulk density (g cm-3)
0.5 –0.8
0.8 – 1.1
0.7 – 0.9
Clay (%)
5 – 25
10 – 40
40 – 70
Organic C (%)
3 – 12
1–4
3-6
CEC1) (meq (100 g) *
20 – 45
20 – 40
15 – 20
P-fixation (%)
75-99
25 – 80
40 – 90
pH in H2O
5.5 – 6.0
6.0 – 7.0
3.9 – 4.5
2 – 19
10 – 100
2 - 20
P-Olson (mg
l-1)
*: Cation Exchange Capacity
In earlier stages of the REPOSA-project a qualitative land evaluation has been carried out which
resulted in feasible soil/crop combinations (Schipper, 1996). Table 2.3 shows which combinations of
soil and crops are feasible. Although production of a crop on a particular soil may be possible the
type of soils can limit the productivity. In the model reduction factors have been defined to take this
phenomenon into account based on expert knowledge. Some soil crop combinations are only
suitable after construction of a drainage system.
Table 2.3
Suitable and unsuitable soil and crop combinations.
Crop
Fertile well drained (SFW)
Fertile poorly drained (SFP)
Infertile well drained (SIW)
Banana
suitable *
suitable *
unsuitable
Bean
suitable
unsuitable
unsuitable
Cassava
suitable
unsuitable
suitable
Maize-grain
suitable
unsuitable
unsuitable
Maize-cobs
suitable
unsuitable
unsuitable
Palmheart
suitable
unsuitable
suitable
Pineapple-export
suitable *
unsuitable
suitable *
Pineapple-local
suitable
unsuitable
suitable
Plantain
suitable
unsuitable
unsuitable
Melina (wood)
suitable
suitable *
suitable
Teak (wood)
suitable
suitable *
suitable
*: Only suitable after construction of drainage system.
2.3.4
Crop type
Four annual crops have been defined: maize (Zea mays L.), bean (Phaseolus vulgaris L.), cassava
(Manihot sculenta Crant), and pineapple (Ananas comosus L.). The latter two crops can require
more than one year to produce a marketable yield.
12
Cassava usually has a growth cycle that varies between 6 to 24 months (Purseglove, 1987). In this
study a variety with a growth cycle of 10 months is assumed since cassava in the zone is grown for
export purposes that requires a short growing season in which relatively small roots are produced.
For pineapple two types have been defined, one especially for the export market and one for the
local market. Both differ e.g. in the used variety and planting density but more important is their
difference in post harvesting handling. Pineapple for export is pre-processed at the farm of which
the inputs (capital and labour) are taken into account. In section 2.2 scale aspects concerning post
harvest activities are discussed. For both types of pineapple a one and a two cycle (ratoon) variant
is defined. The ratoon variant produces after 13 or 14 months a first harvest, which is followed by a
second (smaller) harvest 9 to 11 months later.
For maize also two types have been defined, one for grain production and one for production of
fresh cobs. Both have a different economic value and since fresh cobs are harvested in an earlier
stage than grain maize both differ in labour flow over the season and nutrient dynamics.
2.3.5
Mechanisation level
Due to the high rainfall intensities combined with the compaction risk of soils in the Atlantic zone
possibilities for mechanisation are limited. Mechanisation levels are restricted to the use of field
preparation, sowing and fertiliser application during the growing season for some alternative land
use activities. Only for pineapple also the application of herbicides and biocides can be
mechanised.
It is assumed that mechanised soil preparation results in a yield increase due to improved soil
conditions. Moreover, nutrient recoveries are higher using mechanised soil preparation.
2.3.6
Crop residue strategy
Two crop residue strategies are defined: harvesting of crop residues which can be used as fodder
and crop residues left at the field after harvesting the main produce. Pineapple residues can not be
used for fodder; hence they are always left at the field. The crop residue strategy affects the nutrient
balances of crop activities.
2.3.7
Herbicide level
Normally, crop protection agents include all types of agrochemical used for curative and preventive
crop protection, such as herbicides, insecticides, fungicides and nematicides. In this study it is
assumed that herbicides can be substituted by manual weeding methods without affecting yield
levels but requiring more labour (see sections 2.1 and 2.2). It is assumed that substitution of other
crop protection agents, in this study called biocides, by means of e.g. integrated pest management
(IPM) is not possible without substantial yield losses. Two herbicide levels are defined: low and high
herbicide levels. In the low herbicide option chemical herbicides are completely substituted by
manual weeding which requires more labour but does not affect the yield level.
13
2.3.8
Biocide level
Two options are defined: a low and high biocide level. In the low biocide option chemical
insecticides and fungicides are reduced with 50% compared to the high biocide option. It is
assumed that with more crop hygienic measures and crop monitoring, - requiring extra labour -, the
use of fungicides and insecticides can be reduced. However, some yield losses are inevitable
compared to the high biocide option in the disease and plague susceptible (humid) environment of
the Northern Atlantic zone. Due to less favourable growing conditions crop nutrient recoveries of
activities with the low biocide option are assumed to be lower than crop activities with a high biocide
level.
2.3.9
Start of growing season
This criterion is defined to identify labour peaks during the season. Since no reliable information
exists on effects of the date of sowing on yields it is assumed that yields, with the exception of
beans, are not affected by the date of sowing. Therefore, it is assumed that maize, cassava and
pineapple can be sown/planted throughout the year. Beans are susceptible for excess of water
during flowering and seedsetting. Therefore, sowing only takes place in December or January to
achieve that susceptible growth phases coincide with the relatively dry months February and March.
2.4
Perennial crops
In Table 2.4 definition criteria for perennial crop activities and their options are shown. Compared
with annual activities the start of the growing season is not identified as definition criteria. It is
implicitly assumed that the start of the crop cycle is always January. Any labour peak due to
differences in planting dates will be compensated by the length of the crop cycle, which is 10 or 15
years. It is implicitly assumed that all operation periods can occur simultaneously at one hectare
which is close to reality: Production of perennials is a continuos process in which harvesting,
maintenance, planting etc. occur throughout the year.
It is assumed that perennials have an initial period of 2 years in which yields are lower than in the
following years. In year three a constant production is attained which can be continued for 10 or 15
years. Since the input-output relations are different in the first years, the first four years of
perennials are modelled individually. For the nutrient dynamics also the last year of the crop cycle is
modelled. The input-output relations of the intermediate years are considered to be equal to those
of year four. Since the crop cycle of perennial crops lasts 10 or 15 years the in-and outputs are
discounted over these periods.
Theoretically, the nine definition criteria can be combined in any combination which means that 2
types of activities, 11 yield levels, 3 soils, 3 crops, 2 mechanisation levels, 2 crop residue strategies,
2 length of crop cycles, 2 herbicide levels and 2 biocide level can result in 6336 unique perennial
crop activities. However, not all combinations are feasible: soils can be unsuitable to grow certain
crops (see Table 2.3), crop residues may not be suitable to be used as fodder after harvesting, the
low biocide level is in the current setting of LUCTOR infeasible, etc.
14
Table 2.4
Definition criteria and the distinguished options for perennial land use activities.
definition criterion
Maximum number of options
1. type of activity
2 (actual and alternative)
2. yield level
11 (10 target yield levels for alternative activities, 1 yield level for actual activities)
3. soil type
3 (fertile poorly drained (SFP), fertile well drained (SFW), infertile well drained (SIW))
4. crop type
3 (banana, palmheart, plantain)
5. mechanisation level
2 (low and high mechanisation level)
6. crop residue strategy 2 (harvesting, left at field)
7. crop cycle
2 (10 and 15 year)
8. herbicide level
2 (low and high herbicide level)
9 biocide level
2 (low and high biocide level)
See for remarks on the definition of yield level, soil and the herbicide level subsection 2.3.2, 2.3.3
and 2.3.7 respectively.
2.4.1
Type of activity
See subsection 2.3.1 for the definition of actual and alternative annual activities. In contrast to most
annual crops, in most perennial crops fertilisers are currently applied. This may imply that actual
perennial activities have positive nutrient balances. Yields of actual perennial activities are
estimated based on a variety of sources (literature and farm survey data collected within REPOSA),
yields of alternative crops are based on expert knowledge.
2.4.2
Crop type
Three perennial crops have been defined: banana (Musa AAA), plantain (Musa AAB) and palmheart
(Bactris gasipaes). For alternative banana and plantain activities it is assumed that meristeme
cultures are used that are on the one hand more expensive than traditional planting material but on
the other hand more resistant to nematodes. They require in the first two years no nematicides.
2.4.3
Mechanisation level
Due to the high rainfall intensities combined with compaction risk of soils in the Atlantic zone and
crop characteristics (narrow passage and height) possibilities for mechanisation are limited in
perennial crops. Mechanisation levels are restricted to the use for field preparation and aerial
spraying. Non-mechanised soil preparation does not results in a yield decrease and a lower nutrient
recovery as assumed for annual crops since the length of the crop cycle of perennial crops will
compensate any lower production and nutrient recovery in the first production cycles. Other
operations in bananas such as application of fungicides with an aeroplane, preparation of a
drainage system and harvesting with a cable system always occurs mechanised. Alternative
plantain activities are assumed to occur in a similar way as bananas, -with cable system, and
aeroplane to combat diseases, etc.-, thus requiring much more capital than actual plantain
activities.
15
2.4.4
Crop residue strategy
Two crop residue strategies are defined: harvesting of crop residues, which can be used for fodder
and crop residues, left at the field after harvesting the main produce. These residue strategies are
applied throughout the entire crop cycle, i.e. at each harvest a part of the crop residues can be
harvested or left at the field. Both options affect labour requirements and nutrient dynamics since
carry over effects of nutrients to following crop cycles are taken into account.
2.4.5
Crop cycle
The crop cycle is the time that a hectare of land is planted with a crop. For perennial crops cycles
are set at 10 or 15 years. The length of the crop cycle determines the time in which initial
investments can be reimbursed. The first two years are required to establish the crop and yields are
therefore lower. In year three a stable yield level is obtained that can be maintained till the end of
the crop cycle.
2.4.6
Biocide level
The biocide options, although identified as definition criteria, is not operational since crop specialists
argue that banana and plantain production with export quality is not possible with a reduced use of
biocides (especially the use of fungicides against Sigatoka). In palmheart no fungicides and
insecticides are used, so that a low biocide options will not change the input output relationships.
When new information comes available the biocide criteria can be made operational.
2.5
Plantation forestry activities
In Table 2.5 definition criteria for plantation forestry activities and options are shown. Wood
production is assumed to depend on soil type. Neither biocide, herbicide nor mechanisation options
have been defined since their use is very low in plantation forestry production. Also no separate
yield levels for alternative activities have been defined since insufficient knowledge exists on the
input-output relations at lower yield level of plantation forestry systems.
Table 2.5
Definition criteria and the distinguished options for plantation forestry activities.
definition criterion
maximum number of options
1. type of activity
2 (actual and alternative)
2. type of tree
2 (teak and melina)
3. soil type
3 (fertile poorly drained (SFP), fertile well drained (SFW), infertile well drained (SIW))
See paragraph 2.3.1 and 2.3.3 for the type of activity and soil type, respectively. Two tree species
are defined, teak (Tectona grandis) and melina (Gmelina arborea). These tree species are fairly
new to the Northern Atlantic zone and knowledge about e.g. production potentials, quality aspects
16
of their wood (that determine its price), thinning regimes, etc. is limited. Therefore, also production
data and management of tree plantations in Guanacaste have been used completed with expert
knowledge. Since knowledge on production possibilities is insufficient, yield levels of actual and
alternative plantation forestry activities are kept similar. Both types of activities only differ in nutrient
balances.
The length of crop cycles is arbitrarily set. In practice it is determined by a combination of
production and economic considerations. In this study the length of crop cycles depends on the
productivity, which on its turn is determined by the soil type. At fertile soils (SFW and SFP) with a
high productivity the cycle is shorter than at poor soils (SIW). Just as for perennial crops the final
inputs and outputs are discounted over the length of the crop cycle.
17
3
Technical Coefficient Generator
This chapter describes the technical background and structure of LUCTOR and guidelines for
operation.
LUCTOR is modelled in Excel 5.0 (Microsoft, 1995) but also operates under Microsoft Office 97.
Only basic knowledge of the spreadsheet program is required to operate it: how to open files,
change windows, entering cell values, what the menu bar is, etc. No external data are required to
run LUCTOR. The user only needs to select the options for each definition criteria, which
characterises the required land use activity (see Chapter 2 for the options for the definition criteria
of land use activities). Since the structure of LUCTOR is modular, i.e. data and calculations are in
separate files, the user can adjust data relatively easy. Moreover, each parameter or calculation
rule is described in the files so that they are self-explanatory and transparent. To adjust one of the
calculation files and their formulas and to add other definition criteria a more thorough knowledge of
Excel is required.
Since TCs are customised to the requirements of two different LP-models, used by UNA/DLV and
REPOSA, in the following reference is frequently made to these models.
3.1
Structure of LUCTOR
LUCTOR comprises a set of Excel-files (in Excel they are called workbooks) which can be
subdivided in four types:
1
Files required for automatisation of procedures that return frequently. These files are macrofiles with an XLM-extension, except for LIST.XLS which includes (1) lists with definition criteria
and their options and (2) lists with files which are required for the generation of each type of
land use activity (annuals, perennials, pineapple and plantation forestry). Each time a new type
of land use activity is selected LUCTOR reads from LIST.XLS which files can be closed and
need to be opened. This file is also used each time a definition criterion is changed. For
generation of land use activities TCG.XLM and LIST.XLS always have to be open. In Appendix
I the files required for each type of land use activity are listed. It is noticed that in the macrofiles mainly the Excel 4.0-macro language is used which is compatible with higher Excel
versions.
Principally, macro files (XLM-files) are not essential to generate technical coefficients. These
files contain customised instructions that have to be carried out frequently and that are created
for LUCTOR to follow. They make life a lot easier. They do not contain calculations or data
manipulation.
2
Files with data and basic parameters:
BASIC.XLS: parameters concerning nutrient efficiencies
SOIL.XLS: soil and crop suitability parameters, equation coefficients for wood production
CROPS.XLS: perennial crop parameters, e.g. task times of operations, number of operations
type and quantity of crop protection agents, crop specific parameters (dry matter distribution
over crop parts, nutrient content of crop parts, yields and yield reduction factors, etc.)
18
LUST.XLS: annual crop parameters, e.g. task times of operations, number of operations, type
and quantity of crop protection agents, crop specific parameters (dry matter distribution over
crop parts, nutrient content of crop parts, yields and yield reduction factors, etc.)
BIOCIDE.XLS: list of crop protection agents, their characteristics and prices
PRIX.XLS: prices of other inputs and outputs
3.
Files with calculation rules based on the selected options for each definition criteria, which are
in these workbooks.
ANNUAL.XLS: calculations for annual crops
PERENIAL.XLS: calculations for perennial crops
PINA.XLS: calculations for pineapple (one and two year ratoon crop)
FOREST.XLS: calculations for tree plantations
Although, in Chapter 2 pineapple is described as an annual crop a separate workbook was required
due to the possibility to grow a ratoon pineapple crop.
Calculation workbooks consist of more than one worksheet. At the bottom of the screen tabs with
names of all worksheets are visible. At the top of the worksheet named ‘main’ the definition criteria
are set. In the worksheets ‘UNA_DLV-output’ and ‘REPOSA-output’ the final technical coefficients
of a generated land use activity are stored for respectively the UNA/DLV and REPOSA LP-model.
Since the LP-model of UNA/DLV does not take into account plantation forestry activities there is no
worksheet with TCs in FOREST.XLS for this LP-model.
For PINA.XLS, PERENIAL.XLS and FOREST.XLS additional calculation files are required:
PINNUI.XLS, PERNUI.XLS and FORNUI.XLS, respectively. These extra files include annuity
calculations for the TCs of crops that last longer than one year.
4.
Output files in which the generated coefficients of a great number of activities are stored.
ANN_IO.XLS output of annual crops
PIN_IO.XLS output of pineapple crops
PER_IO.XLS output of perennial crops
FOR_IO.XLS output of plantation forestry crops
To generate technical coefficients for one type of land use activity (annuals, pineapple, perennials
and plantation forestry) several files need to be opened which are automatically linked. The macro
files take care of this. Some of the data-type files are used for different types of land use activities.
Calculation-files are always used for only one type of land use activity.
File names of LUCTOR are unique and should not be renamed.
In general LUCTOR has a more generic structure than its predecessor developed for the Koutiala
region in south Mali (Hengsdijk et al., 1996) and could be used in other agro-ecological zones. For
example, there is strict file delineation between data and calculation rules. However, experience
with the development of this type of models shows that data availability and local specific issues
determine to a large extent which relationships and too what extent they can be taken into account.
The TCG developed for Koutiala, for example, contained generic modules to calculate water-limited
production and labour requirements for transport of crops and residues with manpower and different
types of animal traction. Despite their generic character both modules are not used in LUCTOR
since production is not limited by water and transport by manpower and different types of animal
traction is not feasible in the Northern Atlantic zone.
19
3.2
Concepts and conventions used in LUCTOR
The definition criteria characterise each type of land use activity. They determine which calculation
rules and data are applied. Definition criteria can be found at the top of the worksheet ‘main’ of the
calculation files of each crop activity (see also section 3.1). For reason of convenience the definition
criteria are also shown at the top of other worksheets of the calculation files. However, only
adjustment of the definition criteria in the worksheet ‘main’ will affect calculations!
Beside the worksheet ‘main’ and two worksheets with the final TCs for the LP-models, two other
important worksheets are available. In the worksheet ‘production+labour+biocide use’, - the name is
self-explanatory -, the target biomass is calculated, its distribution over crop parts, labour, traction
and equipment use and biocide use and a credit balance is calculated. In the worksheet ‘nutrient’
balances and requirements are calculated for three macronutrients. Due to the specific properties of
plantation forestry activities their worksheets are differently organised. Their worksheets are called
‘labour+costs’ and ‘nutrient+production’.
The calculation files (ANNUAL.XLS, PINA.XLS, PERENIAL.XLS and FOREST.XLS) contain in the
first column information on the type of calculations that follow. In the second column a detailed
description of the variable or parameter values is given. In the third column the unit of the variable
or parameter is given while in the fourth column their names in italics can be found so that these
can easily be distinguished from other text and information. In this document will also be referred to
the variable and parameter names using italic letter type. In the fifth column the actual variable or
parameter value can be found. The italic names in the fourth column refer to these cells. These
names are important because in other parts of the worksheet or in other worksheets they can be
referred to. See Figure 3.1 for an example of a part of a calculation file (PERENIAL.XLS).
Instead of commonly used column and row references in spreadsheet programs, user-friendly cell
names are applied to refer to other cells. Pressing the [F5] key the complete list with names
appears. By selecting one of the names and pressing [OK] the active cell is automatically moved to
the location with the assigned name. In most cases the variable and parameter names are
described directly at the left side of the cell name. Some names are placed directly above the cell
ranges to which they apply. Particularly, in the worksheets ‘nutrient’ this is frequently done.
Remember that with pressing [F5] the cell range to which the name is assigned always can be
identified.
Calculation files are linked with the data files to retrieve data and parameters. Retrieving of data in
the calculation worksheets, especially from LUST.XLS and CROPS.XLS, occurs by means of the
so-called intersection method. This method implies that the cell value is returned at the intersection
of two range names. In both files abbreviated names of crops and combinations of crop and type of
activity (e.g. ‘mai_al’ standing for alternative maize) are vertical range names while parameters are
defined with horizontal range names. Combination of a horizontal and vertical range name result in
the value of their joint cell.
20
Figure 3.1
Example of a part of a calculation file, in this case PERENIAL.XLS.
Since worksheets can contain a lot of data and/or calculation rules in horizontal as well as vertical
directions, in all worksheets the part containing information is bordered on the right side and bottom
with a double line.
In data files is indicated which cells with data can be changed by the user. In general cells with
numbers, including zeros (0), can be changed and affect the values of the TCs. Cells with a dash (-)
must not be changed. Changing these cells will result in erroneous results or does not affect the
calculation of TCs.
Cells can contain information in cell notes, which are indicated by a red dot in the upper right corner
of the cell. By double-clicking cells with a red dot the cell note will show its content and can be
edited. These notes contain e.g. assumptions or literature references. They do not disturb the
program and calculation rules.
3.3
Installation and operation of LUCTOR
Before starting to use LUCTOR it should be installed. Therefore, copy all files to a directory and
open TCG.XLM in Excel. Press [Ctrl+d] to activate an input box to enter the name of the directory,
e.g. C:\USR\TCG (see Figure 3.2).
21
Figure 3.2
Input box that appears after pressing [Ctrl+d] to set the directory of LUCTOR.
After having pressed [Return] a new input box asks you to enter the directory in which customised
output files for the REPOSA-model are stored, e.g. C:\USR\TCG\REPOSA. This directory must be
different from the directory in which LUCTOR is stored! The interface (macro) which combines
Excel output files and generates new ASCI files suited for the LP-models (see section 3.5) will
delete all files in this directory each time it is run. After pressing [Return] a similar input box appears
to enter the directory name for output files for the UNA/DLV-model, e.g. C\USR\TCG\DLV. Also in
this directory files will be automatically deleted by a macro, hence the directory has to be used only
for storing output files. Preferably, both output directories should be created with a filemanager
before starting the installation procedure, although they can also be created in a later stage (but
before output files are manipulated for the LP-models!). It is obvious that only an output directory for
that LP-model is required for which output files will be created. Either pressing the Cancel button or
pressing [Esc] can skip one of the input boxes. After having performed the installation procedure,
save TCG.XLM so that the directory assignments will be saved.
To use LUCTOR proceed with the following steps:
1. Press [Ctrl+t] to activate a macro that opens a dialogue box with user options (see Figure 3.3).
On the background of the dialogue box you see part of the macro-file TCG.XLM. The dialogue
box includes a list box with (i) land use activities that can be generated, (ii) options to combine
different output files into customised files for the LP-models, (iii) options to quit the dialogue
box without changes and to return to the active worksheet, or (iv) to close all worksheets and
return to TCG.XLM. Moreover, at the top is a check box to indicate for which LP-model TCs
22
2.
3.
4.
5.
6.
7.
must be generated. This check box is only of importance for generating a batch of land use
activities.
Select one of the land use activities and turn the check box on for which LP-model you want to
generate TCs. Press the [Return] key.
LUCTOR will open the required Excel files for the selected activity as listed in Appendix I.
After opening of the files the active window contains the output file in which the automatically
generated TCs are stored. With [Alt+w] you can view the list of files that are opened by the
macro. By clicking the filename in the file list you can switch among files. Also with [Ctrl+F6]
you can switch among files.
Go to the calculation file (ANNUAL.XLS, PINA.XLS, PERENIAL.XLS or FORESTRY.XLS).
Go to the worksheet with the name ‘main’. Worksheet names can be found in the tabs at the
bottom of the your screen. You can switch between worksheets by pressing [Ctrl+page
down/up] or by clicking on the tab.
The worksheet ‘main’ includes the definition criteria, which guide the generation of TCs. The
generation of TCs of the specified land use activities can now be started (see section 3.4).
To generate TCs for another type of land use activity (annuals, pineapple, perennials or plantation
forestry) the macro should be activated again with [Ctrl+t]. A new type of land use activity can be
selected and/or the check box for output of another LP-model can be changed. The files, which are
not required for the new type of land use activity, will be closed. This can take a few moments.
While closing a calculation file Excel will return with a question whether you want to save changes.
When you made no structural changes but only generated TCs there is usually no reason to save
changes since you can always generate the same TCs using the same criteria options, Therefore,
press [No]. When you changed formulas, added or changed data, etc. you may prefer to save
changes: press [Yes]. Subsequently, the macro will automatically open the new files, which are
required for the generation of TCs for the chosen type of land use activity. Press never [Cancel]
because this will hamper a sound closing and opening of files. In case you did press [Cancel],
activate [Ctrl+t], select the same type of land use activity again, and continue.
23
Figure 3.3
Dialogue box with the list box including land use activities and check box to indicate for which
LP-model TCs must be generated. The dialogue box appears after opening TCG.XLM and
pressing [Ctrl+t].
3.4
Generation of technical coefficients
TCs can be generated manually or automatically. For the former the user fills in manually the
definition criteria as numbers one by one. For the automatic generation custom-made macros are
available to select definition criteria using dialogue boxes with check boxes. Subsequently, other
macros allow generation a batch of TCs for annual, pineapple, perennial and plantation forestry
activitiesat once.
3.4.1
Manual generation of TCs
Generation of TCs can start after having performed the steps described in section 3.3. The Mainworksheet contains a range of bordered cells with definition criteria (see Figure 3.4). These cells
have the name ‘characteristics’ placed on top and are linked with a formula to cells on the right
hand side with ‘input’ on top. These cells contain numbers corresponding with the criteria options.
The numbers refer to positions in the lists of criteria options in LIST.XLS for each definition criteria.
Changing one of these numbers will change the definition criteria in the bordered part after having
pressed the [F9] (recalculation key) with which the required changes in calculation rules (guided by
definition criteria) are carried out. Only the numbers in the bordered cells should be changed. Do
24
not change the contents of the bordered cells. See Appendix II for an overview of the criteria
options and their corresponding number.
Figure 3.4
Example of main worksheet of ANNUAL.XLS with the definition criteria that have to be changed
by the user.
Each option can be selected and recalculated with pressing [F9]. The generated TCs of the
selected options can be found in the worksheets ‘REPOSA-output’ and ‘UNA_DLV-output’. This
process can be repeated over and over to generate TCs of activities with other characteristics.
In both output worksheets a section is reserved for checking combinations of definition criteria that
are technically unfeasible or combinations that are not required by the LP-model. An example of the
first type is that not all soils are suitable for all crops because of drainage characteristics or soil-pH
(see Table 2.3). An example of the second type of check is when crop residues are not used for
feed in the LP-model there is no need to calculate the harvesting option of residues. When the user
fills in a combination with infeasible criteria options these are identified in this section of the outputworksheets. Its result is indicated with a ‘TRUE’ or ‘FALSE’ statement for feasible and infeasible
combinations respectively just below the land use activity code (see for an explanation of the
activity code Appendix III). In Table 3.1 a list is shown of criteria for which checks are performed.
Since LUCTOR is used for different LP-models some checks are LP-model specific, which are
indicated in Table 3.1.
25
Table 3.1
Criteria for which feasibility checks are performed (between brackets the LP-model for which
feasibility checks refer to).
Feasibility checks
1. Soil is suitable to growth a crop (UNA/DLV & REPOSA)
2. Beans only planted in December or January (UNA/DLV )
3. Crop residue always left at field (REPOSA)
4. Crop residue of cassava always harvested and pineapple always left at field (UNA/DLV)
5. Month of sowing is always January (REPOSA)
6. Maize fresh cobs can not be selected (UNA/DLV)
7. Target yield level of 10% not possible (UNA/DLV & REPOSA)
8. Actual activities only with 100% production level (UNA/DLV& REPOSA)
9. Low biocide option combined with banana, plantain or palmheart not possible (UNA/DLV & REPOSA)
10 Planting ratoon-pineapple (2 year) crop not after May (UNA/DLV)
To remove one of these checks, the appropriate cell in the output worksheet should be set manually
to ‘TRUE’. For the automatic generation of TCs (see paragraph 3.4.2) the checks are programmed
at the end of the macro-files for each type of land use activity (ANNUAL.XLM, PINA.XLM,
PERENIAL.XLSM or FOREST.XLM). Here are instructions how to delete feasibility checks.
Land use activities generated for the REPOSA model get a special label indicating whether they are
suitable for land units with steep slopes and stoniness. In general, land use activities that require a
kind of mechanised operation are unsuitable at these land units (see section 3.5). Teak is also
excluded from steep slopes because of erosion risks. The label says ‘slope limitation’ or ‘no slope
limitation’ and is located in the cell just below the label for the feasibility check.
3.4.2
Automatic generation of TCs
TCs can also be generated for a batch of activities using custom made dialogue boxes in which all
criteria options are listed. These are directed macros, which enable to generate TCs for multiple
land use activities of the same type (annuals, pineapple, perennial or plantation forestry)
simultaneously. Macros take care of combining all criteria options in all possible combinations,
taking into account the feasibility checks shown in Table 3.1 and write the TCs to the relevant
output file (ANN_IO.XLS, PIN_IO.XLS, PER_IO.XLS or FOR_IO.XLS).
For the automatic generation of multiple activities it is important that the check box (for which LPmodel TCs are generated) is turned on for the right LP-model (see Figure 3.3). Since the TCs differ
for both LP-models the output files are also different although the same file name is used, e.g.
ANN_IO.XLS for annual crops. Therefore, these output files need to be formatted for the desired
LP-model before starting the generation of TCs. After having performed the steps described in
section 3.3 press [Ctrl+o] to format the output file. Be aware that existing data of output files are
automatically deleted with this action. When you want to save your data copy the file to another
filename or copy it to another directory before you start working with LUCTOR. In case you lose a
data file you can create a new one by opening a new Excel file and save it with the output file name
of the land use activity (see section 3.1) before starting LUCTOR.
After having formatted the output file the generation of TCs can be started by pressing [Ctrl+s]. For
each definition criteria a dialogue box with its options as check boxes will appear. The definition
26
criteria for the land use activities are shown in Chapter 2. In Figure 3.5 an example of such a
dialogue box for the definition of the soil type is shown. Select the options of the definition criteria
for which TCs must be generated by turning on/off the check boxes.
Figure 3.5
Dialogue box for selection of definition criteria for soil types.
After having selected all options for the definition criteria with the dialog boxes, the options
(represented as numbers as indicated in Appendix II) are automatically combined in all possible
combinations. Each unique combination is written to a line in ANN_IO.XLS with in the first colum the
number of the combination. After combining all options the total number of combinations is indicated
in cell [C11] with the name Ncomb and the number of feasible combinations is indicated in cell
[D11] with the name Nact. See Figure 3.6
27
Figure 3.6
Example of ANN_IO.XLS after macos have combined all selected options in all feasible
combinations. In cell [C11] the total number of combinations is given and in cell [D11] the
number of feasible combinations. A line with numbers indicates the characteristics of an unique
land use activity.
Subsequently, another macro is automatically started that copies each combination of definition
criteria (a single row with numbers) to the ‘main’ worksheet of the calculation file and calculates TCs
of that specific combination. After recalculation, TCs in the selected output worksheet (‘UNA_DLVoutput’ or ‘REPOSA-output’) are copied to the output file at the end of the row with the definition
criteria.
The previous step is continued till all land use activities (lines in the output file) are generated. To
speed up the generation process the screen is updated each fifty calculations. When LUCTOR is
ready the screen looks like shown in Figure 3.7.
28
Figure 3.7
Example of how the screen looks after performing the generation of all selected land use
activities.
The TCs of the generated land use activities are placed behind the numbers that characterise their
options. To distinguish land use activities they each have a unique identification code. They are
different for the UNA/DLV-output and REPOSA-output and are explained in Appendix III.
3.5
Customising output files for LP-models
Individual output files of crop activities (ANN_IO.XLS, PIN_IO.XLS, PER_IO.XLS and FOR_IO.XLS)
can be automatically combined in one file, which is subsequently split up in smaller files (with ASCI
and Excel-format), customised to the needs of the LP-model of REPOSA. For the LP-model of
UNA/DLV a similar interface is available to combine ANN_IO.XLS, PIN_IO.XLS and PER_IO.XLS.
The procedure can be started pressing [Ctrl+t]. Select one of the two selection options that will start
the customising operation (see Fig. 3.1): Combine crop activities (REPOSA model) or combine crop
activities (UNA/DLV model), and press [return].
LUCTOR opens the required output files that will be combined. With pressing [Ctrl+c] the user starts
the procedure for the REPOSA model. The output interface for the UNA/DLV-model can be
activated with [Ctrl+v].
29
The procedures use the directories that have been defined at the installation of LUCTOR (see
section 3.3) as working directories to save the new created files. Old files are deleted in these
directories. For reasons of safety the user gets a message before the procedure starts which warns
the user. The user can halt the macro pressing the Cancel button and copy or move the files to
another directory.
When the procedure is continued output files are copied into one file, formatted and land use
activities are sorted in ascending order. The combined files are called REPOSA.XLS and
DLVCROP.XLS depending on for which LP-model files are combined. A message will appear on
your screen asking to replace existing files with the same name. Press always the OK button.
A last warning will appear on your screen telling the user that files will be deleted when proceeding.
It is a last chance to halt the macro and copy and/or move files to another directory. In case the
macro is halted the macro can be reactivated with [Ctrl+f] for the REPOSA output and [Ctrl+m] for
the UNA/DLV-output.
Continuation of the macro will split the created REPOSA.XLS or DLVCROP.XLS in smaller files
customised for the LP-models. Be aware that this process can take several minutes. For the
REPOSA model a special procedure is carried out that creates a file with activities that require flat
land and no stoniness to allow mechanisation. This allows building an extra constraint in the LPmodel for soils unsuitable for mechanisation (see also paragraph 3.4.1).
The Excel-files created for the REPOSA-model include:
LUST_COM.XLS = files with all activity codes
LUST_MEC.XLS = file with activities requiring flat and no stony soils enabling mechanisation
LUST_YLD.XLS = file with yields
LUST_LAB.XLS = file with average labour requirements per month
LUST_COST.XLS = file with total costs of production
LUST_SUS.XLS = file with sustainability indicators
LUST_EXT.XLS = file with fertilizer inputs and P and K-losses
LUST_MAT.XLS = file with implement use and other inputs
The same files are saved in ASCI code with a PRN-extension that are used for the REPOSA LPmodel.
The Excel-files created for the UNA/DLV-model include:
CROP_REL.XLS = files with all activity codes
CROP_OUT.XLS = file with outputs of crop activities
CROP_IN.XLS = file with inputs of crop activities
The same files are saved in ASCI code with a TXT-extension that are used for the UNA/DLV-LPmodel.
30
31
4
Annual activities
This Chapter describes how annual crops (pineapple for local market, pineapple for export market,
cassava, grain maize, maize cobs and beans) are quantified and which data have been used.
Where appropriate, literature references are given of the used parameter values. All literature
references used can be found in cell notes of the Excel-files (see also section 3.2). However, many
data are based on quantitative expert knowledge and not based on published data. See Appendix I
for an overview of the files required generating annual crop activities.
In the preceding text italics words refer to the range names of parameters and variables in the
LUCTOR- files. For a complete description of the parameter and variables, we referr to the
LUCTOR-files. A range name in the text without a file name refers to the last named file in the text.
4.1
Target yield
Crop yields are used as a starting point to determine other inputs and outputs of alternative and
actual crop activities and are stored in parameter yld in LUST.XLS. For actual activities this
parameter indicates the average current yield of a crop (Brink, 1988; Stolzenbach, 1990; Chin-FoSieeuw, 1994; Den Daas, 1993; Tonjes, 1994; Cortés, 1994; BNCR, 1992), while for alternative
crop activities it is the maximum crop yield for the Atlantic zone estimated by crop experts. Since
these yields can only be attained when growing conditions are in optimum, for less suitable
conditions yield reduction factors have been defined. Three reduction factors have been defined: for
manual soil preparation (cor_mec), for low biocide application (cor_bio) and for acid soils (SIW)
(cor_siw). Values of these yield reduction factors are based on expert estimations.
The final target yield (yld_target) used in the further calculations is determined in ANNUAL.XLS
with:
YLD_TARGET = YLD*(100-COR_MEC)/100*(100-COR_BIO)/100*(100-COR_SIW)/100
When for alternative activities lower yield levels are selected (see section 2.3) the target yield is
lowered. Ten yield levels can be selected which decrease the maximum yield stepwise with 10%
(Notice that for the REPOSA LP-model and UNA/DLV LP-model only the nine highest levels are
feasible, see Table 3.1). Although yield levels are defined at an equidistant range, other outputs and
inputs are not. It is assumed that nutrient concentrations increase with higher yield levels (Van
Keulen & de Wolf, 1986). In this way non-linear (diminishing return) relationships could be derived
between nutrient requirements and yield levels. The use of insecticides and fungicides decreases
with lower target yields since under less favourable growing conditions crops become less
susceptible to obligate parasitic fungal leaf diseases and insect pests, such as mildew, rusts, aphids
and plant hoppers (De Wit, 1994). Since for herbicides and nematicides such relationships are
unambiguous, the amounts are kept similar at all target levels. Other inputs are kept constant or
relative to the yield level, such as the labour requirements for harvesting.
The target yield (yld_target) is multiplied with the percentage dry matter (dm_perc) to attain the dry
matter target yield.
32
For pineapple two quality fruit classes are identified, each with specific fruit weights (wght_fruit1,
wght_fruit2 in CROPS.XLS). The target yield of pineapple is distributed over the two quality classes:
75% of the actual target yield is first class fruit (fr_fruit1) and 25% is second class fruit (fr_fruit2). In
the second (ratoon) year the fraction first class fruit is lower (65%). For alternative activities
fractions in the first year have been estimated at 80% and 20%, respectively, and 70% first class
fruit in the second year. The final target yield of pineapple is expressed as (1) the number of fruits in
each quality class, (2) the number of boxes of pineapple (only for export pineapple) and (3) the total
weight of pineapples in each class.
The yield of cassava tubers is divided in three classes (LUST.XLS): 60% is export quality (fr_root1),
30% is suitable for the local market (fr_root2) and 10% is refused (fr_root3). The target yield of
cassava is expressed in kg ha-1 for three quality classes (root_clas1, root_clas2, root_clas3 in
ANNUAL.XLS).
For the yield of maize and beans no different qualities are distinguished.
Subsequently, the target yield is used to derive biomass of other crop parts (grains/fruits, earstructures/crown, leaves, stems, tubers and roots) using crop specific distribution parameters
(par_gra, par_infr, par_lea, par_ste, par_tub, par_roo). It is assumed that for alternative crop
activities due to breeding a more favourable distribution of biomass can be attained resulting in
higher harvest indices. See Appendix IV for used crop parameters.
4.2
Production techniques
Production techniques describe how yields as defined in section 4.1 are realised, i.e. which
operations and what kind and quantities of inputs are required. These production techniques differ
in terms of labour use, mechanisation and implement use, material inputs and how crop residues
are used (Stomph et al., 1994). General knowledge about field operations that are required to grow
a specific crop and assumptions about, for instance, crop residue strategies can be used to quantify
production techniques. The definition criteria mechanisation level, crop residue strategy, herbicide
level, biocide level are used to characterise production techniques of each crop. The definition
criteria’ start of the growing season’ determines the temporal distribution of inputs and outputs
through the year. Because some input may be mutually substitutable (see paragraph 2.1.2) a yield
level may be realised with various production techniques.
4.2.1
Operation periods
With the exception of pineapple labour operation periods are identified in which well-defined field
operations have to be carried out. Timeliness is essential for some operations, such as land
preparation and sowing. Delaying such operations affects yield negatively. A distinction in operation
periods helps to identify labour peaks by matching available time in each period with the labour
requirements of operations. Five operation periods are distinguished: soil preparation (prep), sowing
(sow), maintenance (main), harvest (har) and the remainder of the year (rest). Available days in
each period are defined as a fixed number of days and hence they together determine the total
growing period. For pineapple labour requirements are spread out evenly over the entire year.
With the exception of pineapple for which three years are modelled, two years have been taken into
account so that crops can be planted at the end of year one and harvested in the year two. The
33
start of the growing period is a definition criterion (start) which can be adjusted by the user. Every
month of the year can be chosen. Because no reliable information exists on how yields depend on
the time of sowing, it does not affect yields. Only for beans an exception is made, This crop can
only be grown in December or January due to the abundant rainfall in other periods resulting in
possible crop failures.
Labour requirements per operation period are transformed in ANNUAL.XLS to labour requirements
per decade of days. See Appendix IV for the length of the operation periods.
A short description of the distinguished operation periods:
1. Soil preparation
The period of soil preparation starts 15 (maize, beans and cassava) to 30 days (pineapple) before
sowing or planting and can include the following operations: manual (man_prep) or mechanical
(mec_prep) field preparation, chemical (herb_1) or manual (man_we_1) weeding, preparation
seedlings (prep_seed) (cassava and pineapple) and furrowing (furrow) (cassava) and drainage
construction (drain_prep) only for pineapple for export purposes. The type of operations carried out
depend on the definition criteria selected, e.g. the low herbicide option uses manual weeding in
stead of the use of herbicides.
2. Sowing
In the sowing period, seed is sown manually (man_sow) or mechanically (mec_sow) (maize and
beans), manual or chemical weeding, transplantation of seedlings (trans_seed) (cassava and
pineapple), manual or mechanical fertiliser application (man_fert_1, mec_fert_1), and guarding
against birds (guar_1) (maize). The type of sowing and fertiliser application depends on the
selected mechanisation level. For the sowing period 10 (beans) to 30 (pineapple) days are
available.
3. Maintenance
The maintenance period varies between 65 days for beans and 240 days for cassava. Operations
that can be carried out in these periods depend on the selected definition criteria: maximum three
manual or chemical weeding operations, four manual or mechanical fertiliser applications, five
insecticide applications, five fungicide applications, (only for pineapple) a hormone application
(hor_1) and field inspection (inspec_no) for crop hygienic measures and to improve the efficiency of
application of biocides allowing a reduction in the required amount.
4. Harvesting
The harvesting period varies between 5 (beans) and 60 days (pineapple). Three field operations are
defined for this period: guarding (guar_2) (against birds, only for maize), doubling of maize to
prevent rotting of the cob (doub) and harvesting of the main produce (har_main).
5. Remainder of the year
Depending on crop residue strategy selected (utres), residues are harvested in this period (har_res)
or left at the field.
4.2.2
Labour requirements
Labour requirements for operations are based on a several sources, Van Heemst et al. (1981), Van
Duivenbooden et al. (1991), Hengsdijk et al. (1996), BNCR (1992), Cortés (1994), Dijksterhuis
(1997), Bessembinder (1997) and data collected within the REPOSA-project, visited crop experts,
34
and defined as the number of mandays required to complete an operation. The time required for
harvesting (main produce and crop residues) is defined as a function of the output level (per kg).
For pineapple (export and local market) the time required for application of fertiliser is defined as a
function of the amount of fertilisers. Other labour requirements are expressed per ha. See Appendix
IV for the standard task times for the various operations.
4.2.3
Mechanisation and implement requirements
Due to high rainfall intensities and compaction risk of soils, possibilities for mechanisation are
assumed to be limited in the Northern Atlantic zone, with the exception of pineapple. Mechanisation
is limited to the use of tractors plus implements for soil preparation, sowing and fertiliser application
in most crops. Traction requirements are expressed in traction days. In the field preparation period
two operations can be carried out mechanically: soil preparation and furrowing. In the sowing period
mechanical sowing and a fertiliser application are possible. In the maintenance period only fertiliser
applications can be carried out with traction. The high-mechanised option of pineapple is completely
mechanised, including the application of crop protection agents and a semi-mechanised harvesting
operation. The assumption that only the cultivation of pineapple can be mechanized completely is
questionable. In the cultivation of pineapple special tracks for machinery are available along
relatively narrow fields. Spraying booms can be operated using these tracks. Even for harvesting of
pineapple, machinery does not enter the field but drives along the field using a transport band that
is pulled over the field. This band is supplied with mannually picked pineapple. For other crops this
seems a less feasible option.
For quantification of mechanisation requirements the approach followed is identical to that
described in paragraph 4.2.2 for labour requirements.
Requirements for the use of implements (back pack sprayers, sowing machine, fertiliser distributor,
and plough) are not separately specified in LUST.XLS. Requirements of these implements are
determined in the calculation file ANNUAL.XLS based on the traction requirements and labour
requirements for herbicide and biocide application, which equal requirements for implement use.
4.2.4
Crop residue strategy
The crop residue strategy affects the nutrient balance of land use activities. Two residue strategies
are defined which characterise the fate of crop residues:
(i)
(ii)
Harvesting of crop residues from the field, which subsequently can be used to feed animals,
and
Crop residues are left in the field to maintain soil organic matter and nutrient stores.
Since animals can not consume all types of residues, in LUST.XLS is indicated (pos_res) which
crop residues are suitable for fodder consumption. In parameter qual_res the fodder quality
characteristics are specified.
Fractions of crop parts removed from or left at the field are indicated in LUST.XLS (or CROPS.XLS
for pineapple). The parameter range names are a combination of the abbreviation used for the
crops and har and plo for harvesting of crop residues and residues left at the field, respectively.
35
Hence, the range mai_plo indicates the parameters used to calculate the fraction of maize residues
that are left at the field.
4.2.5
Variable inputs
Variable inputs include the amount of seed, seedlings, biocides, herbicides, and fertilisers for actual
land use activities. These parameters are defined in LUST.XLS or CROPS.XLS (for pineapple).
Data used for herbicide and biocide use are from Bessembinder (1997), BNCR (1992), MAG
(1991), Bartholomew (1996) and data collected within the REPOSA-project. In the low biocide
option the insecticide and fungicide amounts as shown in LUST.XLS are reduced with 50%, while at
the same time extra labour requirements are taken into account for crop hygienic measures
(inspec_no).
For biocides and herbicides a code of the chemical agent in LUST.XLS (or CROPS.XLS) indicate
which type is applied. For example, parameter bio_herb_3 comprises the type and amount of the
agent applied at the third herbicide application. These codes correspond with codes in
BIOCIDE.XLS in which characteristics of herbicides and biocides are specified required to
determine the biocide index. Changing the type of herbicide or biocide is possible after checking the
code for the specific agent in BIOCIDE.XLS and replacing the old code with the new one in
LUST.XLS (or CROPS.XLS). For reasons of convenience also the common names of the chemicals
are given in ANNUAL.XLS but they are not used in calculations. Actual and alternative land use
activities always use the same type of biocide or herbicide but the amount applied can differ. See
Appendix V for the complete list of crop protection agents available in LUCTOR to choose from.
The amount of seed and the number of seedlings are based on standard agronomic literature such
as Cortés (1994), Purseglove (1985, 1987) and data collected within the REPOSA-project.
For actual land use activities fertilisers fixed amounts of N, P and K (N_fert_app, P_fert_app and
K_fert_app) are defined in LUST.XLS (in CROPS.XLS for pineapple), while for alternative land use
activities fertilisers inputs are a resultant of nutrient balance calculations (see section 4.3). Data on
actual fertiliser input are based on Chin-Fo-Sieeuw (1994) for maize, Den Daas (1993) for
pineapple for local market, and MAG (1991) for pineapple export market. No fertilisers are applied
at beans and cassava in the actual situation. See Appendix IV for the applied variable inputs.
4.3
Nutrient balances and requirements
To monitor the nutrient status of cropping systems, balances are used which compare in- and
outputs of nutrients in a system (Smaling, 1993). The in- and output processes taken into account
are shown in Table 4.1. A crop activity is considered sustainable in terms of nutrients when nutrient
supply and losses of N, P and K are in equilibrium.
36
Table 4.1
Supply and loss processes used to calculate nutrient balances of crop activities.
supply:
loss:
application of inorganic fertilisers (N, P and K)
removal of marketable crop parts (N, P and K)
wet and dry deposition (N, P and K)
removal of crop residues (N,P and K)
biological fixation (N)
denitrification (N)
volatilisation (N)
leaching (N and K)
erosion (N, P and K)
fixation (P)
Application of manure and weathering are not considered supply processes. Currently, both are of
minor importance in the Northern Atlantic zone and therefore not taken into account.
For actual land use activities the nutrient inputs and outputs are quantified and in a bookkeeping
procedure compared. For alternative land use activities the same procedure is used, followed by
another calculation procedure to estimate the nutrient requirements to attain a closed nutrient
balance (see also sections 2.1 and 2.2): After comparison of all inputs and outputs, a negative
balance indicates the net fertiliser requirements to sustain production of alternative land use
activities. Subsequently, gross fertiliser requirements are determined by taking into account
calculated loss fractions per type of nutrient.
In Table 4.1 is indicated which input and output processes refer to which type of nutrient. Biological
fixation, denitrification and volatilisation are unique for nitrogen. Leaching losses are taken into
account for both nitrogen and potassium. Phosphorus is a relatively immobile nutrient and therefore
leaching losses are negligible. However, irreversible P-fixation to clay minerals (especially
allophane) is taken into account. In the following two subsections the various loss and supply
processes are further discussed.
The total losses due to denitrification, volatilisation, leaching and fixation (only P) are expressed as
a fraction of the nutrients that are supplied. It is likely that more nutrient losses occur under
suboptimum growing conditions (de Wit, 1993). To take into account the less suitable growing
conditions for production orientations with low mechanisation and/or low biocide use, and/or for
actual crop activities it is assumed that their nutrient losses due to denitrification (N), volatilisation
(N), leaching (N and K) and irreversible fixation (P) are higher than for other production orientations.
The loss fraction due to these loss processes is therefore adjusted depending on the production
orientation:
LOSS_FERT = 1 - (( 1 - LOSS_PRE ) * (COR_REC_LB * COR_REC_LH * COR_REC_AC ))
In which:
LOSS_FERT
LOSS_PRE
=
=
COR_REC_LB =
COR_REC_LM =
COR_REC_AC =
Adjusted loss fraction
Unaccounted loss fraction due to denitrification, volatilisation, leaching or
fixation (subsection 4.3.1)
Correction factor for loss fraction due to low biocide application
Correction factor for loss fraction due to low mechanisation
Correction factor for loss fraction of actual type of activities
37
In Table 4.2 estimated values of these correction factors are shown. For a high biocide application,
high mechanisation level, and alternative type of activities the values of correction factors are 1.
They are not specified per crop type. The correction factors are defined in BASIC.XLS. The
complementary fraction of LOSS_FERT can be considered the nutrient recovery of a land use
activity.
Table 4.2
Correction factors for loss fraction due to denitrification, volatilisation, leaching and irreversible
fixation at various production situations (see text).
definition criteria option
name (in BASIC.XLS)
value correction factor
Low biocide level
cor_rec_lb
0.75
Low mechanisation level
cor_rec_lm
0.85
Actual land use type
cor_rec_ac
0.80
It is assumed that the availability of nitrogen originating from organic N-sources (symbiotic bacteria
and crop residues) is 40% lower than from inorganic N-sources (rainfall and fertiliser). This is
expressed in the parameter cor_om_N in BASIC.XLS. Part of the nitrogen from organic sources
mineralises outside the growing season and comes available when there is no crop, while inorganic
N-fertilisers can be applied during the growing season at times that the crop requires nutrients most.
Nitrogen from inorganic fertilisers is taken up by the crop before it can be lost by volatilisation,
denitrification and leaching. For phosphorus it is assumed that availability (cor_om_P in
BASIC.XLS) of phosphorus originating from organic sources (for P only crop residues) is 25%
higher than from inorganic P-sources because the relatively immobile organic P can become
available during subsequent growing seasons.
4.3.1
Loss processes
Export of crop nutrients
Nutrients exported from the field with the main produce and crop residues are a function of the
target yield, crop residue strategy and nutrient concentrations in the various crop components. The
latter are based on minimum concentrations given by Hengsdijk et al. (1996), MODUS (Stoorvogel
et al., 1995), Van Duivenbooden (1992), Bessembinder (1997), Van Dijk (pers. com.) and Tonjes
(1994) multiplied by correction factors (f_cor in LUST.XLS) which depend on the type of activity
(yield level) and accounts for the fact that nutrients are hardly never diluted to their minimum
concentrations due to the effects of other growth reducing factors (Van Keulen & de Wolf, 1986).
See Appendix IV for nutrient contents and correction factors of annual crops.
For the different yield levels of alternative activities these correction factors are adjusted since
nutrient contents increase with higher yields (Van Keulen & de Wolf, 1986). The maximum yield
level equals the correction factor used for alternative activities, while the minimum yield level (20%
of the maximum yield for the REPOSA and UNA/DLV LP-model) equals the correction factor used
for actual activities plus 0.2 times the difference between correction factors for alternative and
actual activities. For nutrient crop contents of other crop yields a linear interpolation occurs between
both extremes. In this way nutrient contents increase linearly with increasing yields of alternative
crop activities. This is a simplification since yields of actual activities are higher than the 20% level
of alternative land use activities, but have lower nutrient contents in LUCTOR. It is noticed that the
38
minimum and maximum nutrient contents in LUCTOR are derived form various literature sources. In
most cases, however, it was unclear to which yield and fertilizer levels these numbers relate.
Therefore, minimum and maximum nutrient levels as quantified in LUCTOR are to some extent
arbitrary.
It is assumed that nutrients in crop residues left at the field after harvest are available for crop
uptake during the growing season discounted for losses as described in section 4.3. Implicitely, this
presumes that the same crop is cultivated year after year. In contrast to the approach of De Koning
et al. (1995) land use activities in LUCTOR are not defined on the basis of crop rotations since
hardly any information existed on cropping sequences and related effects.
Volatilisation and denitrification
Losses due to volatilisation are set at 5% of the applied inorganic organic. Losses due to
denitrification are based on Veldkamp & Keller (1997) which measured nitrogen oxide emissions in
banana plantations in the Northern Atlantic zone. Denitrification losses are a function of the soil
type. At fertile poorly drained (SFP) soils losses are set at 10%, at infertile well-drained (SIW) and
fertile well-drained soils at 6% of the applied nitrogen in inorganic form. At drained SFP-soils
denitrification is reduced with 25% (N_drain_nit), hence it is 7.5%.
Leaching
Nitrogen and potassium can leach to soil layers beyond the rooting zone. The fraction inorganic N
and K lost in this way is a function of the amount of percolated water. The percentage N lost by
leaching is set to 60% of the ratio percolated and total infiltrated water. Sevenhuysen & Maebe
(1995) estimated for the three identified soil types the percentage surface runoff (surf_run_un in
SOIL.XLS) and percolation (ground_run_un in SOIL.XLS). For soils that can be drained, SFP and
SFW, both hydrology characteristics change. Parameters for drained soils are stored in surf_run_dr
and ground_run_dr, respectively and are based on the same source as for undrained soils. The
annual rainfall is set at 4000 mm (rain in BASIC.XLS).
For potassium the fraction lost in percolated water depends on the clay content of the soil. The
higher the clay content, and hence the higher the adsorption capacity for cations, the lower the Klosses. The minimum fraction of K lost in percolated water is set to 0.25 (Kles_mn) for soils with a
clay content of 50% (Kles_arg_mx). The maximum fraction lost is 0.9 (Kles_mx) for soils with a clay
content of 2.5% (Kles_arg_mn). For soils with intermediate clay contents the fractions of K-losses
are determined by means of linear interpolation. So-calculated K-fractions in percolated water are
0.59 for SFP-soils, 0.73 for SFW-soils and 0.18 for SIW soils.
P-fixation
Phosphate fixation is considered an additional loss process. It is assumed that this process at
relatively young volcanic soils is irreversible and that a part of the P supply is fixed and no longer
available for crop uptake. It is set at 30% of the applied phosphate (P_fix) for SFP and SFW soils,
and set at 40% for SIW soils.
Erosion
Erosion poses no serious problem in the Northern Atlantic zone. To estimate erosion the Universal
Soil Loss Equation (USLE) can be used which is developed for conditions in the U.S. (Wischmeier
& Smith, 1960). Since accurate calibration of parameters for the USLE are lacking for the Northern
Atlantic zone it seems less appropriate to use the USLE. Therefore, erosion is set at 1 ton ha -1
which is less than the annual top soil formation in the humid tropics (FAO/IIASA, 1991). Nutrients in
the eroded material are lost from the soil store in a quantity equal to the product of soil loss and soil
39
nutrient concentration. In SOIL.XLS the relevant soil P and K-concentrations are shown (P_tot and
K_tot). Soil N-content is based on the soil organic matter contents (om) and the C/N ratio of soil
organic matter, which is set to 15 (cn_om in BASIC.XLS). Erosion losses of P and K are calculated
applying an enrichment factor of 2 (f_enrich) which accounts for the higher nutrient content in the
top soil, because of higher proportion organic material and fine particles. For nitrogen no
enrichment factor is applied because with the organic matter content higher erosion is implicitly
accounted for.
4.3.2
Supply processes
Wet and dry deposition
Wet and dry deposition is defined as a function of annual rainfall: 0.42 g N, 0.04 g P and 1.35 g K
per ha per mm of rainfall (N_rain, P_rain and K_rain) (Parker, 1985).
N-fixation by associated and free living bacteria
The supply of nitrogen through associated nitrogen fixing bacteria is estimated at 5 kg ha-1 year-1
(N_fix_sym) (Stoorvogel, 1995).
Microbial N-fixation
For leguminous beans it is assumed that 75% of the nitrogen taken up by this crop is derived form
the crops' specific N-fixing capacity (f_N_fix in LUST.XLS) (Hengsdijk et al., 1996).
Fertiliser
The external supply of nutrients from fertilisers to actual crop activities is described in paragraph
4.2.6. Fertiliser efficiency is determined by the overall loss processes as described in paragraph
4.3.1 and calculated as a loss fraction. For alternative activities the fertiliser supply is a resultant of
the nutrient balance calculations.
4.3.3
Operationalisation of nutrient balances
For actual crop activities nutrient balances are applied straightforwardly. The sum of the loss and
that of the supply processes as presented in Table 4.1 are compared to each other. The following is
an example of the procedure performed to calculate a nitrogen balance of an actual crop activity.
Relevant definition criteria of the crop activity are: maize, fertile well drained soil, crop residues
harvested, high mechanisation and biocide level. The procedure involves five steps: (i) calculation
of N-uptake by the crop in different crop components, (ii) calculation of total N-loss fraction, (iii)
calculation of total N-supply, (iv) calculation of total N-loss and, finally, (v) calculation of the Nbalance in which N-supply and N-loss are compared.
40
1. N-UPTAKE
Calculation of the N-uptake by the crop in different crop components is required to determine the
amount of nitrogen that is removed from the system.
I. Calculations for removal of marketable products and crop residues.
biomass (kg ha-1) minimum N-content (g kg-1)
correction factors N-uptake (kg ha-1)
grains
2010
11
1
22
ear-structures
377
4.5
1.1
2
leaves
1256
8.0
0.9
9
stem
1571
4.0
0.9
6
roots
1068
4.5
0.9
4
total
6282
43
2. TOTAL N-LOSS FRACTION
Before the total amount of lost nitrogen can be calculated, in preliminary calculations the nitrogen
lost by erosion and other loss processes are calculated as a fraction of the supplied nitrogen.
II. Calculation of soil nitrogen loss due to erosion
erosion (a)
1 ton ha-1
organic matter content (b)
4.4%
C/N ratio (c)
15
fraction C in organic matter (d)
0.58
Total N-loss (a * 1000 * b/100 *d/c)
1.7 kg N ha-1
III. Calculation of loss fraction due to denitrification, volatilisation and leaching
a1. volatilisation and denitrification
0.05 + 0.06 = 0.11
a2. leaching:
infiltration
2760 mm
percolation
1720 mm
percolated fraction
1720/2760 = 0.62
fraction N in percolated water
0.6
fraction N lost by leaching
0.62 * 0.6 = 0.37
losses (a1 + a2) (N_loss_pre)
0.11 + 0.37 = 0.48
correction factor for low mechanisation level and actual crop 0.8 * 0.85 = 0.68
activity (Table 4.2)
Total loss fraction inorganic-N (N_loss_fert)
1 - ((1-0.48) * 0.68) = 0.65
3. TOTAL N-SUPPLY
N-supply encompasses two processes, nitrogen from external and natural resources.
a. Application of fertilisers, a given amount (see subsection 4.2.5):
65
b. Wet and dry deposition:
b1. rainfall: 4000 mm * 0.00042 kg N mm -1 (see subsection 4.3.2)
1.7
b2. N-fixing by symbiotic bacteria (see subsection 4.3.2):
5
Total N-supply (a+b)
71.7 kg N ha-1
41
4. TOTAL N-LOSS:
Subsequently, total N-losses can be calculated taken into account all input processes that are
subject to losses and nutrient withdrawal with crop harvest. Notice that supply losses c2 and c3 are
adjusted for extra organic N-losses (see end section 4.3).
a. Removal of main produce: grains and ear-structures (box I):
24
b. Removal of crop residues: stems and leaves (box I):
15
c. Supply losses due to denitrification, volatilisation and leaching:
c1. rainfall: 1.7 * 0.65 =
1.1
c2. N-fixing symbiotic bacteria: 5 * (0.65 + (0.35*0.4)) =
4.0
c3. fertiliser: 65 * 0.65 =
42
c4. recycled organic matter (roots): 4 * (0.65 + (0.35*0.4)) =
3.2
d. Erosion soil N (box II)
1.7
Total N-loss (a+b+c+d)
91 kg N ha-1
5. N-BALANCE:
The N-balance is the difference between the N-supply as calculated in step 3 and the total N losses
calculated in step 4: SUPPLY - LOSS = 71.7 – 91 = - 19.3 kg N ha-1
In the preceding example the nutrient-balance, as in most other actual production systems in the
Northern Atlantic zone, is negative indicating soil depletion. It is emphasised that the N-balance is
not equal to the nutrient requirements needed for a zero nutrient balance. To counterbalance its
nutrient deficit an amount of nutrients is required that takes into account losses (N_loss_fert) that
occur after application of nutrients.
For alternative crop activities, first the difference between nutrient loss and supply from natural
sources is determined.Subsequently, the quantity of fertilisers is calculated required to cover the
deficit. In the following an example is given of the calculation of nitrogen requirements of an
alternative maize crop activity. The N-balance of this activity must be in equilibrium. The definition
criteria of the crop activity are similar as the previous example: fertile well drained soil, crop
residues harvested, high mechanisation and biocide level. The procedure involves four steps: (i)
calculation of the N-uptake by the crop in different crop components, (ii) calculation of the total loss
fraction, (iii) calculation of the net N-availability and (iv) calculation of the N-requirements.
1. N-UPTAKE
Calculation of the N-uptake by the crop in different crop components is required to determine the
amount of nitrogen that is transported from the system.
I. Calculations for removal of marketable products and crop residues.
biomass (kg ha-1) minimum N-content (g kg-1)
correction factors
N-uptake (kg ha-1)
grains
4971
11
1.8
98
ear-structures
809
4.5
1.1
4
leaves
1850
8.0
2
30
stem
2312
4.0
2
18
roots
1618
4.5
2
15
total
11560
165
42
2. TOTAL N-LOSS FRACTION
Before the total amount of lost nitrogen can be calculated, in preliminary calculations the nitrogen
lost by erosion and other loss processes are calculated as a fraction of the supplied nitrogen.
II. Calculation of soil nitrogen loss due to erosion.
erosion (a)
1 ton ha-1
organic matter content (b)
4.4%
C/N ratio (c)
15
fraction C in organic matter (d)
0.58
Total N-loss (a * 1000 * b/100 *d/c)
1.7 kg N ha-1
III. Calculation of loss fraction due to denitrification, volatilisation and leaching.
a1. volatilisation and denitrification
0.05 + 0.06 = 0.11
a2. leaching:
infiltration
2760 mm
percolation
1720 mm
percolated fraction
1720/2760 = 0.62
fraction N in percolated water
0.6
fraction N lost by leaching
0.62 * 0.6 = 0.37
losses (a1 + a2) (N_loss_pre)
0.11 + 0.37 = 0.48
correction factor for low mechanisation level (Table 4.2)
0.85
loss fraction inorganic-N (N_loss_fert)
1 - ((1-0.48) * 0.85) = 0.56
3. TOTAL NET N-AVAILABILITY
The net N-availability is a function of external resources and recirculated crop residues, discounted
for nitrogen losses as calculated in step 2.
IV. Net N-availability from external and recirculated resources. Notice that posts b and c are
corrected for extra organic N-losses.
supply
%
eff. org./
loss
inorg. N
net N-availability
a. Rainfall (=4000 mm*0.00042 kg N mm-1)
1.7
0.56
1
1.7* 056=0.75
b. Symbiotic bacteria (see subsection 4.3.2)
5
0.56
0.6
5*(1-0.56)*0.6= 1.3
c. Recycled org. matter (roots)
15
0.56
0.6
15*(1-0.56)*0.6= 4.0
(=1618kg ha-1*0.0045 kg N kg-1*2)
total net N-availability (a+b+c)
6.0 kg N ha-1
4. N-REQUIREMENT
The N-requirement of alternative activities is a function of on the one hand the total N-uptake (box I)
and soil nitrogen lost with erosion (box II), and on the other hand net N-availability (box IV).
box I
box II
box IV
N-uptake crop
Loss of N in soil organic matter due to erosion
N-shortage
Net N-availability
N deficit that has to be supplied with fertilisers
165
1.7 +
166.7
6.0 160.7
N-requirements discounted for losses (160.7/(1-0.56))
368 kg N ha-1
43
Analyses of the different components of the calculations shows that the loss percentage due to
denitrification, volatilisation, leaching (Box III) has the largest impact on the N-requirement. Since
accurate measurements of leaching are lacking, process based knowledge combined with expert
knowledge have been used to estimate it. Logical starting point is the water surplus, combined with
an arbitrary fraction of N in the percolated water. The user can easily change this parameter value
and other loss-parameter values in LUCTOR to assess different assumptions. Decreasing the total
loss fraction (N_loss_fert) with 25% from 0.56 to 0.42 results in a proportional 25% reduction in Nfertiliser requirements (274 kg N ha-1). Reducing the fraction of N in percolated water with 25% from
0.6 to 0.45 decreases the fertiliser N-requirement with ± 16% to 309 kg N ha-1. When no additional
losses are assumed due to low mechanisation (Box III) fertiliser N-requirements decrease from 368
kg to 310 kg N ha-1. Omitting the assumption that the availability of inorganic nitrogen sources is
40% higher than from organic N-sources (section 4.3) decreases fertiliser-requirement the least (to
360 kg N ha-1). This is due to the fact that in this example there is hardly any N supply form organic
sources since crop residues are harvested.
Operationalisation of the phosphate balance of actual and alternative crop activities is similar to that
for nitrogen. However, as denitrification, volatilisation and leaching do not affect phosphorus
dynamics, the procedure is simpler. It is assumed that loss processes for phosphorus include the
removal in main produce, crop residues, irreversible P-fixation of the applied phosphate and
erosion. A large uncertainty is related to P-fixation at soils from volcanic origin that include SFP,
SFW and SIW soils. Changing the value of P_fix may show consequences of different assumptions
concerning P-fixation.
N-requirements of alternative activities do not linearly decrease with decreasing yield levels. Since
values of correction factors for nutrient contents decrease linearly with yield level (see section 4.1)
production per kg applied nitrogen is higher at lower yield levels than at higher yield levels. In Figure
4.1 an example is shown of this procedure for two different production orientations for maize grain,
a high biocide and a low biocide level. The curves are a simplified version of what the theoretical
relationship between yield and N-gifts looks like (Van Keulen & de Wolf, 1986). The maximum yield
level of the low biocide orientation is lower than of the high biocide option. Moreover, the production
per kg applied N is lower for the low biocide option due to the assumed higher nutrient loss fractions
under less favourable growing conditions (see section 4.3 and Table 4.2).
44
7000
Yield (kg/ha)
6000
5000
4000
3000
high biocide option
2000
low biocide option
1000
theoretical high biocide curve
0
0
50
100
150
200
250
300
350
N-gift (kg N/ha)
Figure 4.1
Relationship between yield and N-gift for two production orientations of maize grain as produced
by LUCTOR. For comparison a theoretical curve of the relationship is given for the high biocide
level derived from Van Keulen & de Wolf (1986).
For pineapple the nutrient balance is more complicated since the crop has a growing period of
about two years. For each year a nutrient balance is calculated assuming that a part of the nutrients
is turned over to the following year in the ratoon crop. When crop residues are left in the field at the
end of the second (and last) year they are considered recycled organic matter in the first year and
their nutrients (accounted for losses) thus are available for crop uptake. Since this can be a
considerable amount, the nutrient balances of pineapple can be positive in the first year. The
calculation procedure of nutrient balances for ratoon crops is similar as for perennial crops that are
elaborated in section 5.3.
4.4
Costs
Gross revenue is calculated for each crop activity and defined as the difference between income
from products and variable costs for fertilisers, sowing seed, crop protection agents, etc. (netrev in
ANNUAL.XLS, net1 and net2 for both production years of pineapple in PINA.XLS). The gross
revenue as TC is only used for the UNA/DLV-model. The REPOSA-model uses a TC representing
total costs without costs for labour (cost in ANNUAL.XLS, cost1 and cost2 for two production years
of pineapple in PINA.XLS). For pineapple for export purposes also initial establishment costs for a
post harvest processing unit, drainage system, infrastructure, etc. are taken into account. Since, the
lifetime of these capital goods exceeds the crop cycle of pineapple they are discounted over a
period of 15 years. These discounted costs are treated in a similar way as variable costs. See
Chapter 5 for the used discounting procedure.
Prices for inputs are based on the price level of 1996, while prices for outputs are based on an
average price for the years 1994, ’95 and ’96. See Appendix VI for the complete list of prices used
for inputs and outputs.
45
5
Perennial activities
This Chapter describes how perennial activities (banana, plantain and palmheart) are quantified and
what type of data has been used. Where appropriate literature references are given of the current
used parameter values in LUCTOR. All literature references can be found in cell notes of the Excel
files (see section 3.2). See Appendix I for an overview of the files required generating perennial
crop activities.
In the preceding text italics words refer to range names of parameters and variables in the
LUCTOR-files. For a complete description of the parameters and variables, we refer to the
LUCTOR-files. A range name in the text without a file name refers to the last named file in the text.
5.1
Target yield
As in annual crop activities yields are the starting point to determine inputs and other outputs of
perennial crops. Target yields expressed in terms of fresh weight per year are stored in yld in
CROPS.XLS and include post-harvest losses. The average yields of actual activities are based on
(Roeland, 1994; Cortés, 1994; Soto, 1985; Bessembinder, 1997), while target yields of alternative
activities are based on expert knowledge. A complication with perennial crops is that yield levels are
not uniform throughout crop cycles. It is assumed that the first two years are required to establish
the crop and yields are therefore lower. In year three a stable yield level is obtained that can be
maintained till the end of the crop cycle. Although one may expect that yields drop at the end of a
crop cycle due to soil born diseases and deteriorating planting material, no reliable data were found
that supports this idea. In the case of banana and plantain perhaps the continuous replanting with
new and usually more resistant and higher productive planting material prevents a decline in
production throughout the years. Three parameters control yield levels in the different production
cycles: yld_1, yld_2 and yld_3. The former two are used to define yields in the first two years of
production, the latter is used for the third and following production cycles. Since inputs and outputs
of perennials are defined per year the length of production cycles (e.g. cyc1_dur for first production
cycle) is set at 12 months.
As for perennial crops yield reduction factors are identified to take into account less suitable
growing conditions for manual soil preparation (cor_mec), for low biocide use (cor_bio) and for soils,
cor_siw and cor_sfp for SIW and SFP soils, respectively. Since, crop cycles of palmheart, banana
and plantain are at least 10 years it is assumed that less suitable growing conditions due to low
mechanised field preparation can be neglected. These are currently set at zero, but can be changed
in CROPS.XLS. See section 4.1 for further details concerning reduction factors and calculations
used to determine final target yields.
For banana two yield quality classes are identified. Distribution over the two quality classes for
actual banana activities is based on CORBANA (1992): 75% in the first (export quality) class, 25%
in the second class (used for local market or feed). Crop specialists confirm that improved
management can increase the share of first class bananas. Therefore, in alternative activities the
distribution is set at 85% for first class and 15% for second class bananas. The quality distribution is
valid for all production cycles of banana.
46
For plantain the approach resembles that of banana: Distribution is set at 85% in the first class and
15% in the second class for actual activities. In the alternative activities the distribution is set at 90%
and 10 % (Vargas, 1995). The quality distribution is valid for all production cycles of plantain.
For palmheart one quality class is defined which include hearts with a weight of 1.3 kg (Schipper,
pers. com.).
In addition to the total fresh weight of fruits per quality class, the final target yield of plantain and
banana is also expressed per box of 23.6 kg (Vargas, 1996) and per box of 18.16 kg (Ramírez
Calderón, 1993), respectively. The final target yield of palmheart is also expressed as the number of
palm hearts.
Subsequently, target yields (fruits, boxes, or hearts) and dry matter contents (dm_perc) are used to
derive biomass of other crop parts using crop specific distribution parameters (par_gra, par_inf,
par_lea, par_ste, par_roo). For palmheart these parameter values are based on Jongschaap
(1993), for banana and plantain they are based on several sources (Turner, 1972; Turner & Lahav,
1983; Stover & Simmonds, 1987 and Bessembinder, 1997). In Appendix IV crop parameters of
perennial crops are shown.
5.2
Production techniques
The definition of production techniques is to large extent similar to those of annual crop activities.
However, there are a few differences, which will be discussed in the following sections.
5.2.1
Operation periods
In contrast with annual crops no distinct operation periods have been defined. This is according
practice of perennial crops in which field operations (including e.g. harvesting) are carried out
throughout the year. This implies that labour requirements during a year are evenly spread over the
entire year. For the first year establishment operations are defined including manual or mechanical
field preparation, construction of a drainage system and cable system (banana) and preparation
and transplantation of seedlings. For following years maintenance operations have been defined
such as pruning shoots, weeding (manually or with herbicide), fertiliser-, insecticide-, nematicide-,
and fungicide- application, underpinning of plants (banana), bagging fruits (banana), harvesting
main produce and crop residues, packing time boxes, field monitoring to reduce biocide use and
maintenance of the cable system, and replanting.
5.2.2
Labour, mechanisation and implement requirements
Labour requirements for operations are based on several sources, Soto (1985), Vargas (1995),
Morales Chacón & Bejarano. (1991), Ramírez Calderón (1994), BNCR (1992), MAG (1983),
Bessembinder (1997) and data collected within the REPOSA-project. Labour and mechanisation
requirements are shown in Appendix IV.
In contrast to annual crops (with the exception of pineapple) labour requirements for fertiliser
application are a function of the input level. It is assumed that 50 kg of nutrients (N, P and K) can
applied manually per hour and per ha (fert_man), or 500 kg mechanically (fert_mec). This approach
47
has been chosen as for perennials much more nutrients are applied than for annual crops. The
error using fixed labour requirements for fertiliser application of annual crops is relatively small.
Moreover, since operation periods are distinguished for annuals modelling labour requirements as a
function of the fertiliser amount requires knowledge about which amount is applied in what period.
For perennials it is assumed that application occurs throughout the year.
Labour requirements for harvesting (main produce and crop residues) are defined as a function of
the output level, underpinning of plants (only banana) and bagging of plants (banana and plantain)
as a function of the number of plants, other labour requirements are expressed per ha. In general
plantain is not strutted (Morales Chacón & Bejarano, 1991).
Mechanisation is limited to operations used for soil preparation (tr_prep, tr_drain, tr_cable).
Implement requirements are determined in the calculation file PERENIAL.XLS in similar way as for
annuals. Banana and plantain activities are always drained. For SFP soils 2.2 times as much labour
and machinery requirements are needed for drainage construction than at SFW since the
excavated volume of poorly drained soils is 2.2 times higher (Sevenhuysen & Maebe, 1995). The
associated higher costs of these drainage operations are taken into account.
5.2.3
Crop residue strategy
Crop residue dynamics of perennials is quite complex since harvesting and production of residues
continues throughout the year. After each harvest a few suckers are left to produce a following
harvest. For e.g. bananas and plantain the old stem is cut down and left to decompose on the soil
surface between plants. The biomass residue of these crops is much larger than for palmheart of
which only leaves are left at the field. This has consequences for the nutrient requirements and
balances.
Two crop residue strategies can be defined:
(i) harvesting (a part) of the crop residues after each harvest which can be used e.g. to feed
animals, and
(ii) crop residues are left at the field after harvesting to maintain soil organic matter and nutrient
stocks.
Both options affect labour requirements and nutrient dynamics of the crop activities since carry over
effects of nutrients to following crop cycle are taken into account.
Banana and plantain produce two types of animal feeds, second grade fruits and crop residues. In
parameter pos_res (CROPS.XLS) is indicated whether crop residues are suitable for feeding
purposes and in parameter qual_res the feed quality of crop residues.
Fractions of crop parts removed from or left at the field are indicated in CROPS.XLS. The
parameter names for these fractions are a combination of the abbreviation used for crops (pal, ban,
pla) and _har and _plo for harvesting of crop residues and residues left at the field, respectively. For
example, pal_plo comprises the parameters used to estimate the amount of crop residues of
palmheart left at the field. These parameters (fractions of crop residues) refer to the last year of a
crop cycle of a crop cycle. However, at the end of each year/production cycle a part of the crop
parts can be harvested or left at the field. These fractions are used to determine nutrient dynamics
over the years of perennials in PERENIAL.XLS (see also section 5.3). To quantify the amount of
crop residues available for one of the strategies, three extra parameters have been defined: The
fraction of leaves and stems that is harvested (res3_c), the fraction of leaves and stems that is left
48
at the field (en_f) and the fraction of roots that is returned to the soil each production cycle (to
decompose) and which nutrients are available for the next production cycle (la_f ). Estimated values
of these fractions (in CROPS.XLS) differ among crop types since morphology and physiology of
crops differs.
5.2.4
Variable inputs
Variable inputs include the amount of planting material (plant in CROPS.XLS), biocides, herbicides,
fertilisers.
The number of seedlings is based on standard agronomic literature such as Cortés (1994), Vargas
(1994), Vargas (1995), Morales Chacón (1991), Soto (1985) and Ramírez Calderón (1994).
For actual land use activities fertilisers are fixed amounts of N, P and K (N_fert_app, P_fert_app
and K_fert_app) which are applied every year/production cycle. For alternative land use activities
fertiliser inputs are a resultant of nutrient balance calculations (see section 5.3). Data on actual
fertiliser input for palmheart are based on Jongschaap (1993) and for banana and plantain on data
collected within the REPOSA project. They are shown in Appendix IV.
Compared to annuals a different approach is applied concerning biocides and herbicides. Labour
requirements per application of biocides and herbicides are defined in CROPS.XLS. For example,
insec indicates the labour requirements (in hr ha-1) per insecticide application. Besides, the number
of applications for three production years (for e.g. insecticides in insec_no1, insec_no2, insec_3) is
given for biocides and herbicides. It is assumed that the use of crop protection agents can differ
over the years, e.g. during crop establishment more herbicides are required since the crop is less
able to compete with weeds. It is assumed that after the third year the use of crop protection agents
stabilises and equals those of the third year.
A maximum of three different types of herbicides and biocides can be applied, in for example
num_insec the number of insecticides is defined while they are characterised in bio_insec_1,
bio_insec_2 and bio_insec_3 by their codes (see also subsection 4.2.5) and amounts applied. It is
assumed that herbicides and biocides are applied in a mixture or alternating to avoid development
of resistance to any of the agents. In banana and plantain production this is already common
practice (Bessembinder, 1997; Vargas, 1995). The type of crop protection agent is similar for actual
and alternative activities but the applied quantities can be different. In PERENIAL.XLS an average
amount of crop protection agents per year is determined based on the number of agents and the
number of applications per year. For reasons of convenience the common name of crop protection
agent is also given in CROPS.XLS but is not required for calculations. To change the type or
quantity of herbicide and biocide the same procedure need to be followed as described in
subsection 4.2.5. Most data used for herbicide and biocide use are derived from Bessembinder
(1997), Vargas (1995), MAG (1991), Cortés (1994), Soto (1985), Morales Chacón & Bejarano
(1991), Corrales & Salas (1997) and data collected within the REPOSA project. See Appendix IV for
the used crop protection agents in perennials and Appendix V for the entire lists of crop protection
agents available to choose from.
49
5.3
Nutrient balances and requirements
To determine nutrient balances of perennials the same approach has been followed described in
section 4.3 for annuals. The same nutrient efficiencies for production situations and loss processes
are taken into account, except for the correction factors for nutrients from organic sources. It is
assumed that they are as efficiently taken up as nutrients from inorganic sources since perennials
require and are able to take up nutrients throughout the year. Lower nutrient losses in ratoon crops
due to a better-developed root system than in the first year (Twyford & Walmsley, 1974) are not
accounted for. Nutrient contents of crop components of banana, plantain and palmheart are based
on Jongschaap (1993), López & Solís (1991a), López & Solís (1991b) and Bessembinder (1997).
Because the growth of perennials,- by definition-, last longer than one year nutrient balances in
different years have been modelled taking into account nutrients that turn over in the crop to the
following year: The first three years in which the production is not at its maximum, the following
years in which a constant yield level is attained and the last year in which the crop is removed from
the field or left at the field decomposing. Nutrients released from crop residues left at the field in
year n come available in year n+1. Nutrients released with crop residues in the last year of the crop
cycle come available in year 1.
In Table 5.1 an example is given of an actual banana activity with a crop cycle of n years. After the
fourth year a constant level of nutrient withdrawal is supposed. The yield level used in Table 5.1 is
45 t ha-1 of export quality bananas (2478 boxes) in year three and succeeding years. A total N loss
fraction (N_loss_fert) of 0.61 is used to determine net N-supply. The loss fraction is calculated in a
similar way as described in paragraph 4.3.3. Actual fertiliser gifts of perennials do not differ over the
years.
50
Table 5.1
Example of the procedure to calculate the nitrogen balance of actual perennial activities.
Relevant definition criteria of activity: banana, fertile well-drained soil, crop residues left at field,
high biocide option (N = 10 or 15 years).
Year 1 up to n
year 1
year 2
year 3
year 4 up to n-1 year n
application of fertiliser
387
387
387
387
387
crop residues left at field from previous year
439
175
198
233
233
wet and dry composition
1.7
1.7
1.7
1.7
1.7
N-fixing
5
5
5
5
5
fruit
78
88
104
104
104
fruit stem
11
13
15
15
15
leaves
190
215
253
253
253
stems
63
71
84
84
84
roots
76
86
101
101
101
total
418
474
557
557
557
application of fertilisers
149
149
149
149
149
crop residues left at field from previous year
169
67
76
90
90
wet deposition
1
1
1
1
1
symbiotic bacteria
2
2
2
2
2
154
175
206
206
-102
-156
-111
-111
Total gross supply of nutrients (kg N ha-1 yr-1):
Crop N-uptake (kg N ha-1 yr1):
Total net supply of nutrients (kg N ha-1 yr-1)*):
N-turned over in crop to next year (kg N ha-1 yr-1):
N-balance = Total crop uptake – Total net supply –
-98
turn-over (kg N ha-1 yr-1):
*) Assuming loss fraction of 0.61
Calculations in Table 5.1 indicate that with the current fertiliser application rate banana systems are
soil depleting supposing our loss estimates are correct. Despite the large amount of decomposing
crop residues in the first year the available nutrients are not sufficient to build up the entire biomass
apparatus without drawing on the soil nutrient stock. In following years ratoon crops utilise the
remaining standing biomass and are partly nourished by decomposing plant residues of the old
stem. However, due to the larger N-withdrawal (higher yields) N-balances in ratoon years remain
negative. There are several reasons that may explain this phenomena: (i) A discrepancy in the
amount of crop residues left at the field. Little information exists on how much crop residues, - and
more important how much nutrients-, are left at the field after harvesting. In the example shown in
Table 5.1 about 14 ton dm is returned to the soil each year (year 3 and following up to n-1) which
seems in agreement with Hernandez & Scott (1996) and Godefrey (1974) cited by Lahav & Turner
(1983). (ii) An overestimation of nutrient losses in crop residues left at the field after harvest.
Twyford & Walmsley (1974) argue that the ratoon root system is widespread and efficient and they
therefore suppose much lower nutrient losses than in the first year. In the example of Table 5.1
nutrient losses of crop residues are assumed to be as large as losses of fertilisers since the rate of
decomposition of crop residues is very high and nutrients come available in a rapid flush. Godefroy
51
(1974) cited by Lahav & Turner (1983) showed that 10% of banana trash remained after four
months. (iii) An overestimation of the total nitrogen losses (N_fert_loss). However, Godefroy et al
(1975) in Ivory coast reported nitrogen losses due to leaching of 60-85% of the applied nutrients in
banana, which is lower than the total loss fraction used in the example. (iv) Soil depletion rates as
shown in Table 5.1 can be compensated for a certain period of time by net mineralisation rates of
about 200 kg N/ha in the Atlantic zone (R. Plant , pers. com.). (v) Actual fertiliser rates are higher
than assumed. Exact information about the physical quantities of inputs used in plantation farming
is hard to obtain.
For the nutrient dynamics of alternative banana activities in Table 5.2 an example is shown. Too a
large extent the same procedure is used as for actual activities, with a difference that for alternative
activities the N-requirements are estimated under the condition of equilibrium N-balance. The total
N-loss fraction (N_loss_fert) is lower than in actual activities, 0.52. The same remarks are valid as
made earlier concerning the estimated N-loss fraction. The yield (export quality bananas) in this
particular example is 68 ton ha-1 in the steady state years, which is 23 ton more than in the example
of the actual activity in Table 5.1. The N-withdrawal and N-requirements are therefore proportionally
larger.
52
Table 5.2
Example of procedure to calculate the nitrogen balance of alternative perennial activities.
Relevant definition criteria of activity: banana, fertile well drained soil, crop residues left at field,
high biocide option (n = 10 or 15 years).
Year 1 up to n
year 1
year 2
year 3
year 4 up to n-1 year n
Crop residues left at field from previous year
591
236
267
314
314
wet and dry composition
5
5
5
5
5
N-fixing
2
2
2
2
2
fruit
121
137
161
161
161
fruit stem
16
19
22
22
22
leaves
257
291
342
342
342
stems
86
97
114
114
114
roots
101
115
135
135
135
total
581
659
775
775
775
Crop residues left at field from previous year
285
114
129
152
152
wet deposition
1
1
1
1
1
symbiotic bacteria
2
2
2
2
2
N-turned over to next year (kg N ha-1 yr-1):
208
236
277
277
N-shortage = Total crop uptake – Total net supply – 394
335
409
344
344
609
695
847
714
714
0
0
0
0
0
Total gross supply of nutrients (kg N ha-1 yr-1):
Crop N-uptake (kg N ha-1 yr1):
Total net supply of nutrients (kg N ha-1 yr-1) *):
N-turnover in crop (kg N
ha-1
yr-1):
N-requirements = N-shortage / (1-N_loss_fert) (in
kg N ha-1 yr-1)
N-balance
*) Assumed loss fraction of 0.52
High inputs of nitrogen are required for such a crop activity to be completely sustainable in terms of
nutrients. It is noticed that although alternative activities are defined in such a way that nutrient
balances are in equilibrium in some cases nutrient balances may become positive. This occurs
particularly for the first year of palmheart in which large amounts of crop residues are added from
the previous crop cycle while the build up of biomass (N-requirements) is insufficient to absorb the
releasing nutrients.
The same remarks concerning the relationship between N-requirements and target yield levels
apply for perennials as made in section 4.3.2 for annual crops. The nutrient balances of pineapple
ratoon crops are treated as a perennial.
53
5.4
Costs and discounting of TCs
A gross revenue is calculated for each crop activity and defined as the difference between income
from products and variable costs for fertilisers, sowing seed, crop protection agents, etc. and (only
for the first year) establishment costs (net1 up to net4, representing four years, the first 3 years and
year 4 which also represents the following years). In the first year establishment costs for a
drainage system, housing, infrastructure and processing unit, as applicable, are taken into account.
Up to year 3 yields increase and individual gross revenues are calculated. After year 4 management
costs and income (yields) stabilise. The gross revenue TC is only used for the UNA/DLV-model.
The REPOSA-model uses a TC representing total costs without costs for labour (cost1 up to cost4
for four years). In Appendix VI prices for inputs and outputs are presented. Prices for inputs are
based on the price level of 1996, while prices for outputs are based on an average price for the
years 1994, ’95 and ’96.
Perennial crops occupy land for a number of years, in LUCTOR the length of a perennial crop cycle
lasts 10 or 15 years (see section 2.2). In the first years costs are higher than the benefits of
perennials, while the reverse is true in later stages of the crop cycle. Since the LP-models for which
the TCs are generated are one period (one year) models, values of different years have to be
added. Values that occur in earlier years are worth more than those that occur later. Therefore, all
TCs are discounted over the length of the crop cycle (10 or 15 years) to value future cost and
benefits in their present values (Schipper, 1996). This discounting procedure is done in
PERNUI.XLS with a discount rate of 7% (parameter discount in CROPS.XLS). For pineapple this
procedure occurs in PINNUI.XLS.
DTC 
In which:
DTC =
DR =
i
=
y
=
i
DR
TCy


i
y
((1DR)  1) y1 (1DR)
discounted TC
discount rate
length crop cycle
year of crop cycle
54
55
6
Plantation forestry activities
This Chapter describes how plantation forestry activities (teak and melina) are quantified and what
type of data has been used. Where appropriate literature references are given of the current used
parameter values. All literature references can be found in cell notes of parameters in the LUCTORfiles (see section 3.2). See Appendix I for an overview of the files required generating perennial
crop activities.
In the preceding text italics words refer to range names of parameters and variables in the
LUCTOR-files. For a complete description of the parameters and variables, we refer to the
LUCTOR-files. A range name in the text without a file name refers to the last named file in the text.
6.1
Target yield
The approach used to estimate target yields of teak and melina is different from the one used for
annuals and perennials. Since wood production does not reach a stable production after a couple of
years, the individual years until the final harvest are modelled in order to quantify nutrient balances
of plantation forestry activities. Therefore, yields of plantation forestry activities are simulated using
growth curves which are based on empirical measurements of wood production in the first years
(data collected within REPOSA) and extrapolations for the later years. These data have been used
to fit a logistic growth function (De Wit & Goudriaan, 1993):
Yt 
Ym
(1  K * e RGR * t )
In which:
Yt
=
Ym
=
K
=
RGR =
t
=
Gross wood production at t (m 3 yr-1)
maximum gross wood production (m 3 yr-1)
constant
relative growth rate (yr-1)
time (yr)
See Figure 6.1 for an example of this growth function applied for the production of melina. Before
final cut, intermediate thinnings are done that are indicated by the dips in the estimated’ data. Since
the simulated curves do not take into account these thinnings, production in some parts of the
growth are underestimated while in other stages overestimated.
Since growth of tree crops is site specific (Chaves & Fonseca, 1991; Murillo & Valerio, 1991) two
different growth curves are calculated: One for a low production site, the SIW-soils, and one for a
high production site, the SFW and SFP-soils- The latter soils are only suitable after construction of a
drainage system. Parameters describing these growth functions, age of thinning and final cut and
volume of thinnings are defined in SOIL.XLS. It is assumed that a final harvest is earlier at high
production sites than low production sites and that at high production sites in earlier stages of
growth thinnings are required.
56
500
gross wood volume (m3)
R2=0.89
400
high production site
300
low production site
R2=0.77
simulated high production site
200
simulated low production site
100
0
0
5
10
15
years
Figure 6.1
Estimated and simulated growth curves of melina at high and low production sites.
Since teak and melina are relatively new crops in the Atlantic zone neither empirical nor validated
model data exist on current or potential wood production. The above described growth functions
with the same parameter values have been used for actual as well as alternative activities.
Therefore, in the current setting actual and alternative activities only differ in nutrient balances.
However, in CROPS.XLS two parameters are defined that allow to increase or decrease the
maximum production (Ym) and the relative growth rate (RGR) in the above shown equation for both
production sites, Ymax_plus and RGR_plus respectively.
The growth curves shown in Figure 6.1 relate to the gross wood production. The economic valuable
part that is leaving the field is only 65% of the gross volume (vol_mel and vol_tec) in SOIL.XLS.
Biomass of trees is distributed into three components: wood (bole and bark), litter (leaves, branches
and twigs) and roots, par_wood, par_litter and par_roots in CROPS.XLS based on Negi et al.
(1990). These parameters, target yields and dry matter content (dm_perc in CROPS.XLS) are used
to calculate the biomass production of other crop parts. In Table 6.1 an example is given of the
wood production of melina at an SFW-soil. See Appendix III for used parameters.
6.2
Production techniques
No mechanisation level, crop residue strategy, herbicide and biocide levels are defined for
plantation forestry activities. Field preparation is always manual (man_prep in FOREST.XLS), while
for establishment of a drainage system (only at SFP soils) a dragline is used (drain_prep). For
thinning and harvesting sawing equipment is used and tractors for transport, except for the first
thinning which remains in the field. Residues are always left at the field. Herbicides are only used in
the first two years of the crop cycle while biocides are not used at all.
For plantation forestry activities no specific operation periods are defined. Since plantation forestry
activities require relatively little labour field operations are defined on annual base. Labour
requirements and other input data are based on Floors (1996), De Vriend (1998) and Bessembinder
(1997). In the first year 1111 seedlings are transplanted (trans_seed) of which 10% is replanted
(replant) in the second year. Two manual (man_weed) and chemical weeding (herb_1) operations
are required in the first year, in the second year only one operation of each is required. Cleaning
and maintenance operations (clean) are each year required, just as maintenance of ditches in case
57
of a drainage system (ditch). For the actual activities fertiliser is only applied in the first year while in
alternative activities fertilisers each year can be required to replenish the soil nutrient stocks (fert).
Pruning activities take place in the same year as thinning operations (pruning). The average
monthly labour requirements per year for plantation forestry activities (lab) does not take into
account the labour for thinning and harvesting, except for the thirst thinning, since it is assumed that
wood is sold on stem. This implies that required labour and costs concerning harvesting are for the
purchaser.
For actual plantation forestry activities fertilisers fixed amounts of N, P and K (N_fert_app,
P_fert_app and K_fert_app) are applied in the first year. For alternative land use activities fertiliser
inputs are a resultant of the nutrient balance calculations (see section 6.3). Data on actual fertiliser
input are collected within the REPOSA project.
Only one type of herbicide can be applied which characteristics (code and amount) are defined in
bio_herb_1.
6.3
Nutrient balances and requirements
In principle the same procedure is used to determine nutrient balances as described for perennials.
The only difference is that nutrient balances are calculated for each year since annual growth and
nutrients leaving the field with harvested wood differ each year. Turn over of nutrients to following
years are taken into account while annual fall of litter, twigs, branches and decaying of roots are not
taken into account. Due to the fact that tree crops are present throughout the entire year and their
deep rooting system, N and K losses due to volatilisation, denitrification and leaching are reduced
arbitrarily with 50% compared to perennial crops using a correction factor (cor_rec_for in
BASIC.XLS). There is no difference distinguished in nutrient use efficiency between early and later
stages of growth as supposed by Drechsel & Zech (1993).
Data on nutrient contents of different tree components are based on Negi et al. (1990). Used
nutrient contents of wood are a weighted average of bole wood and bark, while the contents of litter
are a weighted average of twigs, branches and leaves.
In Table 6.1 an example is given of the N-balance and N-requirement calculations of a melina
activity. Since, actual and alternative forest activities do not differ in production level and nutrient
loss fraction Table 6.1 illustrates the N-balance for an actual melina activity and the N-requirement
calculation of an alternative option.
The N-balances of plantation forestry activities fluctuate considerably, from negative to positive
numbers in the first year due to the bulk of nutrients released after the final harvest. The large
negative N-balances are partly caused by an overestimation of the gross wood production after
thinning as illustrated in Figure 6.1; Compare for that the estimated curves and the simulated
curves. For the low production site the simulated curve overestimates the wood production less
after thinning (see Figure 6.1) and the overestimation of the real gross wood production will
therefore be less. Although the applied approach is a rather coarse method to derive nutrient
balances due to an inadequate simulation of the real (‘shark vin’) wood production it is obvious that
nutrient dynamics of plantation forestry fluctuate more than perennials due to irregular harvests
(withdrawal of nutrients). Perennials are harvested each year and they will only show a similar
‘shark-vin’ production as plantation forestry within the timestep of LUCTOR which is one year.
58
Table 6.1
Example of procedure to calculate N-balance and N-requirement of a melina activity at an SFW-soil. The total N-loss fraction (N_loss_fert) of 0.23 is used to
determine N-requirements. The codes indicate variable names in LUCTOR.
years
unit
code
1
2
3
4
5
6
7
8
9
10
11
12
Standing gross wood volume at start of the year
m3 ha-1
ann_wood
39
70
118
185
264
339
398
439
463
477
484
488
Gross wood volume of thinning 1
m3
ha-1
thin_1
0
28
0
0
0
0
0
0
0
0
0
0
Gross wood volume of thinning 2
m3 ha-1
thin_2
0
0
0
0
84
0
0
0
0
0
0
0
Gross wood volume of thinning 3
m3
ha-1
thin_3
0
0
0
0
0
0
132
0
0
0
0
0
Gross wood volume of thinning 4
m3 ha-1
thin_4
0
0
0
0
0
0
0
0
0
119
0
0
Gross wood volume of final cut
m3
thin_last
0
0
0
0
0
0
0
0
0
0
0
488
Total gross wood volume cut
m3 ha-1
thin_tot
0
0
0
0
84
0
132
0
0
119
0
488
Wood production:
ha-1
59
years
Nutrient balance components:
unit
1. Cumulative N in standing wood
2. Cumulative N in standing litter
code
1
2
3
4
5
6
7
8
9
10
11
12
kg N ha-1 N_ann_wood
36
64
108
170
242
311
366
403
425
438
445
448
kg N ha-1 N_ann_litter
30
54
92
144
205
264
310
341
360
371
377
380
ha-1
3. Cumulative N in standing roots
kg N
13
24
40
64
91
116
137
150
159
164
166
168
4a. N in wood of thinning 1
kg N ha-1 Nw_thin_1
0
26
0
0
0
0
0
0
0
0
0
0
4b. N in litter of thinning 1
kg N ha-1 Nl_thin_1
0
22
0
0
0
0
0
0
0
0
0
0
0
0
0
0
78
0
0
0
0
0
0
0
0
0
0
0
66
0
0
0
0
0
0
0
0
0
0
0
0
0
121
0
0
0
0
0
0
0
0
0
0
0
102
0
0
0
0
0
0
0
0
0
0
0
0
0
0
109
0
0
0
0
0
0
0
0
0
0
0
93
0
0
0
0
0
0
0
0
0
0
0
0
0
448
0
0
0
0
0
0
0
0
0
0
0
380
0
0
0
0
0
0
0
0
0
0
0
168
142
241
378
538
692
813
895
944
972
988
996
ha-1
N_ann_roots
5a. N-wood in thinning 2
kg N
5b. N-litter in thinning 2
kg N ha-1 Nl_thin_2
ha-1
Nw_thin_2
6a. N-wood in thinning 3
kg N
6b. N-litter in thinning 3
kg N ha-1 Nl_thin_3
ha-1
Nw_thin_3
7a. N-wood in thinning 4
kg N
7b. N-litter in thinning 4
kg N ha-1 Nl_thin_4
ha-1
Nw_thin_4
8a. N-wood in last cut
kg N
8b. N-litter in last cut
kg N ha-1 Nl_thin_last
ha-1
Nw_thin_last
8c. N-roots that comes available after last cut
kg N
9. Total N-uptake by wood, litter and roots (1+2+3) *)
kg N ha-1 N_cum_wood 80
ha-1
Nr_thin_last
10. N-turned over after thinning to next year
kg N
0
80
95
241
378
395
692
590
895
944
770
988
11. N-absorbed each year (9-10)
kg N ha-1 N_abs
N_turn
80
62
146
137
160
297
121
305
50
28
218
8
wet deposition
kg N ha-1
1
1
1
1
1
1
1
1
1
1
1
1
symbiotic bacteria
kg N ha-1
4
4
4
4
4
4
4
4
4
4
4
4
546
0
37
0
0
72
0
112
0
101
0
15
0
0
0
0
0
0
0
0
0
0
0
566
5
42
5
5
77
5
117
5
5
107
5
Net N-availability of resources:
ha-1
available from thinned wood that remains at the field
kg N
available from fertiliser application
kg N ha-1 N_fert
ha-1
12. Total net N-availability
kg N
13.
N-shortage (11-12)
kg N ha-1 N_bal
-486 57
104
132
155
220
116
188
45
23
111
3
14.
N-requirement
kg N ha-1 N_req
0
135
171
200
284
150
243
58
30
143
4
* Numbers between brackets indicate line numbers.
N_av_tot
74
60
61
References
Bartholomew, D.P., 1996, Newsletter of the Pineapple working group, International Society for
Horticultural Science, Vol. 2. no. 1, http://agrss.sherman.hawaii.edu/pineapple/ pnewsv21.htm.
Bessembinder, J.E.E., 1997, Uncertainty and temporal aspects in long-term explorations of
sustainable land use – with reference to the Northern Atlantic Zone of Costa Rica. PhD thesis,
Wageningen Agricultural University, pp. 166.
BNCR, 1992, Data bank of Banco Nacional de Costa Rica, San José, Costa Rica.
Bouman, B.A.M., A. Nieuwenhuyse, H. Hengsdijk, 1998a, PASTOR: A technical coefficient
generator for pasture and livestock systems in the humid tropics, version 2.0. Quantitative
Approaches in Systems Analysis. AB-DLO/C.T. de Wit Graduate school for Production ecology.
Wageningen (in prep.)
Bouman, B.A.M., R.A. Schipper, A. Nieuwenhuyse, H. Hengsdijk & H.G.P. Jansen, 1998b,
Quantifying economic and environmental trade-offs in land use exploration at the regional level: a
case study for the Northern Atlantic zone of Costa Rica. (submitted).
Breman, H, 1992, Agro-ecological zones in the Sahel: Potentials and constraints. In: Sustainable
development in semi-arid sub-saharan Africa (Blokland, A. & F. van Loon eds.). Development
Cooperation Information Department DVL/OS of the ministery of Foreign Affairs, pp. 19-35.
Brink, M., 1988, Doblar o quitar. Sistemas de producción de maíz en la parte norte de la Zona
Atlantico de Costa Rica. Field Report no.16. CATIE/WAU/MAG, pp. 92.
Chaves, E. & W. Fonseca, 1991, Teca. Tectona grandis L.f. Especie de arbol de uso multiple en
America central. Serie Técnica. Informe Técnico No. 179.CATIE, Turrialba, Costa Rica, pp. 47.
Chin-Fo-Sieeuw, S.C., 1994, Agricultural research and extension in maize, palmheart and cassave
in the Atlantic zone of Costa Rica: Activities, experimental. Field Report no.93, CATIE/WAU/MAG,
pp. 54.
CORBANA, 1992, Informe annual 1991. Departemiento de investigacion y diversification agricola.
CORBANA, San Jose, Costa Rica.
Corrales, L. & A. Salas, 1997, Diagnostico ambiental de la actividad bananero en Sarapiqui,
Tortuguero y Talamanca- Costa Rica. MINAE, UICN/ORMA. pp. 129.
Cortés, G. (eds.), 1994, Atlas agropecuario de Costa Rica. San José, Costa Rica, pp. 532.
Den Daas, J.L., 1993, Producing pineapple in the Atlantic zone of Costa Rica: Agronomic and
marketing aspects. Report no. 51, Field report no. 97. CATIE/WAU/MAG, pp. 27.
62
De Koning, G.H.J., H. van Keulen, R. Rabbinge & H. Janssen, 1995, Determination of input and
output coefficients of cropping systems in the European Community. Agricultural Systems 48 (1995)
485-502.
De Vriend, 1998, 1998, Teak: An exploration of market prospects and the outlook for Costarican
plantations based on indicative growth tables. Field Report, Phase 2, CATIE/WAU/MAG, Turrialba,
Costa Rica (in press).
De Wit, C.T., 1993, Ressource use efficiency in Agriculture. Agricultural Systems 40: 125-151.
De Wit, C.T., 1994, Resource use analysis in Agriculture: A struggle for interdisciplinary. The future
of the land: Mobilizing and integrating knowledge for land use options (Fresco, L.O., L. Stroosnijder,
J. Bouma & H. van Keulen eds.), Chichester, John Wiley & Sons. pp. 41-55, pp. 409.
De Wit C.T. & J. Goudriaan, 1993, The growth of yeast. In: On systems analysis and simulation of
ecological processes. With examples in CSMP and Fortran (P.A. Leffelaar eds.) Current issues in
production ecology Volume 1. Kluwer Academic Publishers. pp. 294.
Dijksterhuis, 1997, Female participation in the agricultural sector of the northern Atlantic zone of
Costa Rica. Field Report 129, Phase 2, CATIE/WAU/MAG, Turrialba, Costa Rica. pp. 46.
Drechsel P. & W. Zech, 1993, Mineral nutrition of tropical trees. In: Tropical forestry handbook.
Volume 1 (Pancel, L., eds.) . Springer-Verlag, Berlin. pp. 516-567.
FAO/IIASA, 1991, Agro-ecological land resources assessment for agricultural development
planning: A case study of Kenya: Resources data base and land productivity. Technical Annex 2.
Main report and 8 technical Annexes. Rome, AGL-FAO. 9 vols. 1150 pp.
Floors, A., 1997, Plantation forestry in Guanacaste, Costa Rica: A LUST description of plantation
forestry (teak and melina) in Guanacaste, Costa Rica. Field Report 121, Phase 2,
CATIE/WAU/MAG, Turrialba, Costa Rica.
Godefroy, J., E.J. Roose & M. Muller, 1975, Estimation des partes par les eaux de ruisslement et de
drainage des éléments fertilisants dans un sol de bananeraie du sud la Côte d’Ivoire. Fruits 30: 22335.
Habekotté, B., 1994, TCG-CROP, a model for the calculation of production and environmnetal
variables of different crops, developed for the project ‘Introduction integrated Arable farming’ (in
Dutch). Simulation Reports CABO-TT No. 35. Research Institute for Agrobiology and Soil Fertility
(AB-DLO), Wageningen, The Netherlands.
Hazell, P.B.R. & R.D. Norton, 1986, Mathematical programming for economic analysis in
agriculture. Macmillan Publishing Company, New York, pp. 400.
Hengsdijk, H., W. Quak, E.J. Bakker & J.J.M.H. Ketelaars, 1996, A Technical Coefficient Generator
for land use activities in the Koutiala region of South Mali. DLV-report no. 5, DLO/WAU, pp. 96.
Hernandez, C.E. & S.G. Witter, 1996, Evaluating and managing the environmental impact of banana
production in Costa Rica: A systems approach. Ambio Vol. 25, no. 3, pp. 171-178.
63
Jansen, D.M. & R.A. Schipper, 1995, A static, descriptive approach to quantify land use system.
Netherlands Journal of Agricultural Science 43 (1995) no.1, pp. 31-46.
Jansen, D.M. & J.J. Stoorvogel & R.A. Schipper, 1995, Using sustainability indicators in agricultural
land use analysis: an example of Costa Rica. Netherlands Journal of Agricultural Science 43 (1995)
no.1, pp. 83-110.
Jongschaap, 1993, Palmito (bactris gasipeas H.B.K.) growth and management in Atlantic zone of
Costa Rica. Field report no. 60, Phase 2 CATIE-WAU-MAG, Turrialba, Costa Rica, pp. 49.
Lahav, E. & D. Turner, 1983, Fertilizing for high yield banana. International Potash Institute, IPI
Bulletin, Switzerland, pp. 38.
López, A. & P.B. Solís, 1991a, Contenidos e interacciones de los nutrimentos en tres zonas
bananeras de Costa Rica. CORBANA, Revista de la Cooperacion Bananera nacional V15 no. 36,
pp. 25-32.
López, A. & P.B. Solís, 1991b, Acumulacion de cobre en suelos dedicados al cultivo de banano en
el Pacifico sur de Costa Rica. I. Estado actual de las plantatciones. CORBANA, Revista de la
Cooperacion Bananera nacional V16 no. 38, pp. 9-19.
Microsoft, 1995, EXCEL 5.0 Microsoft Cooperation.
Ministerio de Agricultura y Ganaderia, 1983, Estimacion de costos, ingresos y rentabilidad de
pejibaye (Bactris gasipaes). Pejibaye para palmito. Pejibaye para fruta. Serie Economía para
Mercado tecnia DOC 6 D.E.E. pp. 17.
Ministerio de Agricultura y Ganaderia, 1991, Aspectos de tecnicos sobre cuarente y cinco cultivos
agricolas de Costa Rica.San José, Costa Rico, pp. 560.
Morales Chacón J.L. & G. Bejarano, 1991, Situacion del cultivo de platano (Musa AAB, cv Curraré)
en San Carlos de Costa Rica. CORBANA, Revista de la cooperacion Bananero Nacional V15 no.
36, pp. 6-8.
Morales Chacón, J.L., 1991, Patrones de siembra de platano (Musa AAB) en la perla de San
Carlos, Costa Rica. CORBANA, Revista de la Cooperacion Bananera Nacional V15 no. 36, pp. 3441.
Murillo, O. & J. Valerio, 1991, Melina. Gmelina arborea Roxb., Especie de arbol de uso multiple en
america central. Serie Técnica. Informe Técnico No. 181. CATIE, Turrialba, Costa Rica, pp. 69.
Negi, J.D.S, V.K. Bahuguna & D.C. Sharma, 1990, Biomass production and distribution of nutrients
in 20 year old teak (Tectona Grandis) and Gamar (Gmelina Arborea) plantation in Tripura. The
Indian Forester, Vol 116, no. 9 pp. 681-686.
Nieuwenhuyse, A., 1996, Soils, geology, and soil-related sustainability aspects of the perhumid
tropical Limon Basin, Costa Rica. Serie Técnica, Informe Técnico/CATIE no. 272, pp. 80.
Parker, G.G., 1985, The effect of disturbance on water and solute budgets of hillslope tropical
rainforest in northeastern Costa Rica. PhD thesis, University of Georgia, Athens, GA, USA.
64
Purseglove, J.W., 1985, Tropical crops. Monocotyledons. Longman, London, pp. 607.
Purseglove, J.W., 1987, Tropical crops. Dicotyledons. Longman, London, pp. 719.
Quak, W., H. Hengsdijk, E.J. Bakker, K. Sissoko & M.S.M Touré, 1996, Description agronomique
quantitative des systèmes végétale en zone soudano-sahélienne. Rapport PSS 27 IER/ABDLO/DAN-WAU, pp. 80.
Ramírez Calderon, A.D., 1994, Aspectos basicos para la construccion de una matriz insumoproducto de banano. Inernal Atlantic Zone Programme paper. pp. 19.
REPSOA, 1996, REPOSA research priorities and workplan 1997-1998. Conclusions of the meeting
between the delegation from Wageningen and the Costa Rica field team, dec. 8-15, 1996, Guápiles,
Costa Rica. Phase 2 Report no. 114. Working Document no. 30. pp. 39.
Roeland, R., 1994, Palmito (Bactris gasipaes H.B.K.). Cultivation in the Atlantic and Northern zone
of Costa Rica. Phase 2, Report no. 86, Field report no. 132. CATIE/WAU/MAG, pp. 51.
Sáenz, F., R. Ruben & P. Roebeling, 1997, Farm household investment behaviour and the impact of
agrarian policies on sustainable land use: a case study in the Atlantic zone of Costa Rica. Paper
presented at the seminar ‘Policy instruments to enhance sustainable land use in the Atlantic zone of
Costa Rica’, UNA-WAU research programme, Heredia, Costa Rica.
Schipper, R.A., 1996, Farming in a fragile future. Economics of land use with application in the
Atlantic zone of Costa Rica. PhD thesis, Wageningen Agricultural University, pp. 282.
Sevenhuysen, R. & P. Maebe, 1995, Hydrology drainage options Atlantic zone of Costa Rica. Serie
tecnica. Informe tecnico / CATIE no. 247, pp. 66.
Smaling, 1993, An agro-ecological framework for integrated nutrient management, with special
reference to Kenya. PhD thesis, Wageningen Agricultural University, pp. 250.
Soto, 1985, Bananos, Cultivo y commercializacion. LIL S.A., San José, Costa Rica, pp. 627.
Stolzenbach, A.F.V., 1990, Aspectos de la produccion de raices y tuberculos en la zona Atlantica de
Costa Rica, 1986-1988. Field report no. 48. CATIE/WAU/MAG, pp.31.
Stomph, T.J., L.O. Fresco & H. van Keulen, 1994, Land use system evaluation: Concepts and
methodology. Agricultural Systems 44: 243-255.
Stoorvogel, 1995, Geograhical Information Systems as a tool to explore land characteristics and
land use. With reference to Costa Rica. PhD thesis, Wageningen Agricultural University, pp. 151.
Stoorvogel, J.J., R.A. Schipper & D.M. Jansen, 1995, USTED: a methodology for a quantitative
analysis of land use scenarios. Netherlands Journal of Agricultural Science 43(1995) 5-18.
Stover, R.H. & N.W. Simmonds, 1987, Bananas. Third Tropical agricultural series.
65
Tonjes, J.J., 1994, The pineapple in the Atlantic zone of Costa Rica - A study on the sustainability of
the cropping system of pineapple in the Atlantic zone. Report no. 89, Field report no. 135,
CATIE/WAU/MAG, pp. 26.
Turner, D.W., 1972, Banana plant growth. 2. Dry matter production, leaf area and growth analysis.
Australien Journal of experimental Agriculture and Animal husbandry, Vol. 12, 216-224.
Turner, D.W. & E. Lahav, 1983, The growth of banana plants in relation to temperature. Australian
Journal of Plant Physiology 10, 43-53.
Twyford, I.T. & D. Walmsley, 1974, The mineral composition of the Robusta banana plant. IV The
application of fertilizers for high yields. Plant and Soil 41, 493-508.
Van Duivenbooden, N., P.A. Gosseye & H. van Keulen (eds), 1991, Compétition pour les
ressources limitées : le cas de la cinquième région du Mali. Rapport 2. Productions végétales,
animales et halieutiques. CABO. Wageningen, Pays-Bas, ESPR, Mopti, Mali. pp. 266.
Van Heemst, H.D.J., J.J. Merkelijn & H. van Keulen, 1981, Labour requirements in various
agricultural systems. Quarterly Journal of International Agriculture 20: 178-201.
Van Ittersum, M.K. & R. Rabbinge, 1997, Concepts in production ecology for analysis and
quantification of agricultural input-output combinations. Field Crops research 52 (1997) 197-208.
Van Keulen, H. & de J. de Wolf, 1986, Modeling of agricultural production: weather, soils and crops.
Pudoc, Wageningen, pp. 464.
Vargas, A., 1995, Validicion de tecnologia de producción para alto rendimiento en el cultivo del
platano Curraré o Falso Cuerno (Musa AAB) en el Atlántico de Costa Rica (segunda cosecha).
CORBANA 20 (43): 28-31.
Vargas, A., 1996, Distribucion de costos variables de produccion en platano (Musa AAB cv ‘Falso
Cuerno’) bajo un sistema de cultivo de alta densidad de poblacion y ciclo corto. CORBANA 21(45):
57-61.
Veldkamp, E. & M. Keller, 1997, Nitrogen oxide emissions from a banana plantation in the humid
tropics. Journal Geophysical research (in press).
Wischmeier, W.H. & D.D. Smith, 1960, A universal soil-loss estimating equation to guide
conservation farm planning. 7 th Int. Congress Soil Sc., Vol I, pp. 418-425.
I-1
Appendix I: LUCTOR files
Files used for each type of cropping system
Name of file:
annuals: beans,
pineapple
maize, cassava
TCG.XLM
X
ANNUAL.XLM
X
PINA.XLM
X
perennials: banana, Plantation forestry:
plantain, palmheart
melina, teak
X
X
X
PERENIAL.XLM
X
FOREST.XLM
X
BASIC.XLS
X
X
X
X
SOIL.XLS
X
X
X
X
X
X
X
CROPS.XLS
LUST.XLS
X
BIOCIDE.XLS
X
X
X
X
PRIX.XLS
X
X
X
X
LIST.XLS
X
X
X
X
ANN_IO.XLS
X
PIN_IO.XLS
X
PER_IO.XLS
X
FOR_IO.XLS
ANNUAL.XLS
PINA.XLS
X
X
X
PERENIAL.XLS
X
FOREST.XLS
PINNUI.XLS
PERNUI.XLS
FORNUI.XLS
X
X
X
X
I-2
Type of files used in LUCTOR and their content.
Type of file:
Filename and content:
macro files
TCG.XLM = coordinates which files have to be opened for which crop activity
ANNUAL.XLM = macro file to generate TCs of annuals automatically
PINA.XLM = macro file to generate TCs of pineapple crops automatically
PERENIAL.XLM = macro file to generate TCs of perennials automatically
FOREST.XLM = macro file to generate TCs of plantation forestry automatically
data files
BASIC.XLS = parameters concerning nutrient efficiencies
SOIL.XLS = soil, crop suitability and planatation forestry production parameters
CROPS.XLS = perennial crop parameters
LUST.XLS = annual crop parameter
BIOCIDE.XLS = lists of biocides, their characteristics and price
PRIX.XLS = prices of outputs and inputs (except for biocides)
LIST.XLS = definition criteria and files required for each type of crop activity
output files
ANN_IO.XLS = TCs for annual crop actvities
PIN_IO.XLS = TCs for pineapple crop activities
PER_IO.XLS = TCs for perennial crop activities
FOR_IO.XLS = TCs for plantation forestry activities
calculation files
ANNUAL.XLS = calculations for annual cropping activities
PINA.XLS = calculations for pineapple cropping activities
PERENIAL.XLS = calculations for perennial cropping activities
FOREST.XLS = calculations for plantation forestry activities
PINNUI.XLS = annuity calculations for TCs of pineapple cropping activities
FORNUI.XLS = annuity calculations for TCs of plantation forestry activities
PERNUI.XLS = annuity calculations for TCs of perennial cropping activities
I-3
II-1
Appendix II: Numbers and codes of definition
criteria options
number of definition criteria options in main worksheet (input)
definition criteria
1
2
3
4
5
6
7
8
9
10
type of activity
AC
AL
production level
10
20
start of growing season
JAN FEB MAR APR MAY JUN JUL
30
40
50
60
70
80
90
100
annual crops
BEA CAS MAE MAI
soil types
SFP SFW SIW
mechanization level
HM
LM
biocide level
HB
LB
herbicide level
HH
LH
type of pineapple
PIL
PIN
perennial crops
BAN PAL PLA
cycle pineapple
1
2
cycle perennials
10
15
wood crops
MEL TEC
See Appendix III for meaning abbreviations.
AUG SEP OCT
11
12
NOV DEC
I-2
III-1
Appendix III: Identification codes of land use
activities
Identification codes of activities for the UNA/DLV-model:
The crop activities have the following identification code, for example A1.MAI.FW.T1L.M1.H4
The code is divided in sub codes which are seperated with dots.
sub code 1: Type of activity
A1…A9 = alternative land use activities with yield level; 9 stands for the highest level while 1 is
20% of the highest level
A0 = actual land use activity
sub code 2: Crop type
MAI = maize
BEA = beans
PI1 = local pineapple one cycle
PI2 = local pineapple two cycles
PI3 = export pineapple one cycle
PI4 = export pineapple two cycles
BA1 = banana 10 cycles
BA5 = banana 15 cycles
PL1 = plantain 10 cycles
PL5 = plantain 15 cycles
PA1 = palmheart 10 cycles
PA5 = palmheart 15 cycles
CAS = Cassava
sub code 3: Soil type
FW = Soil fertile well drained
IW = Soil infertile well drained
FP = Soil fertile poorly drained
sub code 4: Combination of herbicide, biocide and mechanization level
T1 = Low herbicide and high biocide
T2 = High herbicide and high biocide
T3 = Low herbicide and low biocide
T4 = High herbicide and low biocide
followed by an ‘L’ indicating a low mechanization or an ‘H’ indicating a high mechanization
level.
sub code 5: Start of growing season
M1 up to M12 = January up to December
sub code 6: Crop residue strategy and quality
P = left at field
H = harvesting of crop residues
A number 1 up to 10 indicating the quality for fodder purposes follows ‘H’, 0 always follows ‘P’.
III-2
Identification codes of activities for the REPOSA-model:
Codes for the REPOSA model have the following identification code, for example:
SFP.MA.F9HHL.10
The code is divided in sub codes which are seperated with dots.
sub code 1: Soil type
SFW = Soil fertile well drained
SIW = Soil infertile well drained
SFP = Soil fertile poorly drained
sub code 2: Crop type
AC = pineapple for export
AM = pineapple for local market
BG = palmheart
GA = melina
MA = banana
MB = plantain
ME = cassava
PV = beans
TG = teak
ZM = grain maize
ZC = grain cobs
sub code 3: Combination of type of land use, yield level, biocide level, herbicide level and
mechanization level.
First letter plus number: type of land use activity plus yield level (F0=actual land use activity,
F1 up to F9 alternative land use activity plus nine yield levels with 9 as the highest level and 1
is 20% of this level.
Second letter: biocide level (L = low, H = high)
Third letter: herbicide level (L = low, H = high)
Fourth letter: mechanization level (L = low, H = high)
code 4: Length of crop cycle
01 for annual crops
02 for pineapple ratoon crop
10 or 15 years for perennial crops
12 14, 20 or 24 years for wood crops
IV-8
Appendix IV: Crop parameters
The following tables contain values of used crop parameters. They are organized as follows:
The headings contain crop names which contain two sub columns. The colum indicated with
‘alternative’ contains data only used for alternative activities and the column with ‘actual’ contains
data used for actual crop activities. The data are a bit differently organized then in the files
LUST.XLS and CROPS.XLS from which they originate. However, the code names (in italics) refer to
the names of range names used in the original file so that parameter values can easily be traced
down.
IV-2
IV-8
V-1
Appendix V: Crop protection agents
-9 = missing value
fraction a.i. = fraction active ingredient in the commercial formulation of the crop protection agent.
WHO code = indication of toxicity related to WHO ( Jansen et al., 1995).
duration = indication of duration of existence of toxin of crop protection agent in system.
price = in colones 1996.
CODE
commercial name
form amount fraction WHO
duration
price
a.i.
code
(days)
(col)
common name
B1601.2
Afalon
kg
0.5
0.5
III
131
3902
Linuron
B1600
Basagram
li
1
0.04
III
48
4178
Bentazone
B1609.904 Banvel-S
li
1
0.48
III
48
2028
Dicamba
B1600.202 Diuron
li
1
0.8
III
64
1442
Diuron
B1600.204 Karmex
kg
1
0.9
III
64
2591
Diuron
B1609.907 Fusilade-II-12.5CE
li
1
0.125
III
64
5498
Fluazifop-butil
B1601.001 Gardoprim
li
1
0.5
III
70
1803
Terbuthylazine
B1601.201 Gesaprim-500
kg
1
0.5
III
50
1893
Atrazine
B1600.206 Goal-2EC
li
1
0.02
III
35
5363
Oxyfluorfen
B1600.208 Gramoxone
li
1
0.2
II
1.00E06 1016
Paraquat
B1601.002 Hedonal
li
1
0.48
II
8
1064
2,4D
B1600.212 Lazo-EC(=Lasso)
li
1
0.04
III
84
1654
Alachlor
B1600.215 Prowl-500E
li
1
0.5
III
171
2758
Pendimethalin
B1601.005 Rimaxil-24D
li
1
0.414
II
8
671
2,4D
B1600.217 Round-up-(lt)
li
1
0.41
III
30
1839
Glyphosate
B1609.91
li
-9
-9
III
3
2474
Propanil
B1601.202 Tribunil
kg
1
0.7
III
135
3559
Methabenzthiazuron
B1603.2
Velpar
kg
1
0.9
III
62
11740
Hexazinona
B1611
Afungil-50PM
kg
1
0.5
III
225
3605
Benomyl
B1619.9
Aliette-80PM
kg
1
0.8
III
0.07
7914
Fosetil Aluminium
B1610
Antracol
Stam-540
kg
1
0.7
III
1.00E06 1598
Propineb
B1619.901 Baycor
li
1
0.3
III
50
9275
Bitertanol
B1619.902 Bayleton-2500EC
li
1
0.25
II
225
10634
Triadimefon
B1611.002 Benlate
kg
1
0.5
III
225
7283
Benomyl
B1614.2
li
1
0.75
III
24
2627
Chlorothalonil
B1614.201 Daconil-2787W75
Daconil-500
kg
1
0.75
III
24
3529
Chlorothalonil
B1610.901 Difolatan
kg
0.5
0.8
Ia
1.00E06 -15
Captafol
B1610.001 Dithane-M-45
li
1
0.8
III
5
1152
Mancozeb
B1610.002 Dithane-F448
li
1
0.8
III
56
999
Maneb
B1610.005 Maneb-BO
li
0.92
0.8
III
56
867
Maneb
B1610.006 Manzate-200
kg
1
0.8
III
5
1217
Mancozeb
B1610.904 Orthocide
kg
1
0.5
II
3650
937
Captan
B1610.905 Poliram-combi
kg
1
0.8
III
0
1297
Metiram
B1619.911 Tilt-25%
li
1
0.25
II
96
9500
Propiconazole
V-2
Continuation:
B1629.201 Ambush-50CE
li
1
0.5
II
14
9126
Permetryn
B1621
kg
1
0.1
Ia
15
518
Terbufos
B1629.205 Decis
Counter
li
1
0.025
II
27
4758
Deltamethrin
B1620.001 Diazinon
li
1
0.6
II
23
1938
Diazinon
B1620.401 Dipterex
kg
1
0.95
III
19
2251
Trichlorfon
B1620.002 Folidol
li
1
0.48
Ia
49
1171
Parathion
B1620.302 Furadan
kg
1
0.1
Ib
37
928
Carbofuran
B1621.4
kg
1
0.9
Ib
6
10636
Methomyl
B1620.004 Lorsban
Lannate
li
1
0.04
II
89
2434
Chlorpyrifos
B1620.005 Malathion
li
1
0.57
III
30
1293
Malathion
B1620.007 Methil-parathion
li
1
0.46
Ia
19
2344
Parathion-methyl
B1621.001 Mocap
kg
1
0.05
Ia
32
954
Ethoprophos
B1620.008 Nemacur
kg
1
0.1
Ia
20
393
Fenamiphos
B1623.003 Orthene
kg
1
0.95
III
2
4536
Acephate
B1621.003 Perfektion
li
1
0.5
II
14
2578
Dimetoate
B1629.208 Pounce-38.4CE
li
1
0.5
II
-9
7279
Permetryn
B1623.004 Tamaron
li
1
0.6
Ib
3
2316
Methamidophos
B1623.4
Temik
kg
0.5
0.15
Ia
2
-15
Aldicarb
B1621.1
Thiodan
li
1
0.35
II
70
2524
Endosulfan
B1621.402 Vydate-L
li
1
0.24
Ib
18
4254
Oxamyl
B1691.002 Calixin
li
-9
0.75
II
14
4050
Tridemorph
B1691.001 Ethrel
li
1
0.3956
-9
1
8153
VI-1
Appendix VI: Prices of inputs and outputs
Prices of inputs based on the price level of 1996, while prices of outputs are based on an average
price for the years 1994, ’95 and ’96.
Input factor (unit)
variable name in
price
LUCTOR
multicultivator (col hr-1)
mul
2707
sowing equipment (col hr-1)
sow
2707
tra
4150
knap
30
spray
2707
drag
7500
fer
2707
saw
596
tractor (col
hr-1)
backpack spraying equipment (col hr-1)
tractor spraying equipment (col
dragline (col hr-1)
fertilizer distributor (col
hr-1)
motor saw (col hr-1)
hormones (col
kg-1)
hr-1)
prix_hor
6980
N (col kg-1)
prix_N
180
kg-1)
prix_P
360
K (col kg-1)
prix_K
128
prix_lab
1600
P (col
Price of labor (col
day-1)
VI-3
Continuation:
variable
actual
alternative act + alt.
name in
banana
banana
LUCTOR
Costs for soil, topographic and feasibility
studies (initial costs in col.
actual
alternative actual
pineapple pineapple palmheart palmheart plantain
alternative
plantain
(export)
(local)
66961
66961
0
0
33481
0
66961
cost_road 311380
311380
155690
0
31138
31138
0
155690
cost_hous 643905
643905
321952
0
0
0
64390
321952
cost_soil
66961
ha-1)
Costs for roads, drainage and cable materials
(initial costs in col.
act.+ alt.
ha-1)
Costs construction houses/processing
unit/administration (initial costs in col.
ha-1
)
Costs overhead labor and maintenance per
e
cost_lab
71709
71709
35854
0
0
7171
0
35854
cost_emp
287
287
287
0
0
0
0
0
Other costs (annual costs in col. ha-1)
cost_rest
97967
97967
48984
0
9797
9797
9797
48984
Annual costs for sigatoka (airplane, labor,
cost_air
141897
141897
0
0
0
0
0
0
year (annual costs in col.
ha-1)
Costs materials used in processing unit (col.
box-1)
materials ,but no costs fungicide in col ha-1)
Seed costs (col piece-1, for annuals col kg-1)
Price first class product (col kg-1, for teak and
melina col.
m-3
155
8
8
25
25
19
155
53
53
46
41
47
47
57
57
0
0
0
0
0
0
34
34
on stem)
Price second class product (col. kg-1, for teak
and melina col.
cost_plant 31
m-3
on stem)
VI-4
Continuation:
variable
act.+ alt.
act + alt.
act.+ alt.
act. + alt
act. + alt
name in
maize-
maize-cobs beans
cassave
teak
melina
LUCTOR
grain
cost_soil
0
0
0
0
0
0
cost_road
0
0
0
0
0
0
cost_house
0
0
0
0
0
0
cost_lab
0
0
0
0
0
0
cost_emp
0
0
0
0
0
0
Other costs (annual costs in col. ha-1)
cost_rest
0
0
0
0
0
0
Annual cost for sigatoka (airplane, labor,
cost_air
0
0
0
0
0
0
cost_plant
357
357
214
1
20
15
36
38
101
46
16263
3250
0
0
0
41
0
0
Costs for soil, topographic and feasibility
act. + alt.
studies (initial costs in col. ha-1)
Costs for roads, drainage and cable materials
(initial costs in col. ha-1)
Costs construction houses/processing
unit/administration (initial costs in col. ha-1 )
Costs overhead labor and maintenance per
year (annual costs in col. ha-1)
Costs materials used in processing unit (col.
box-1)
materials, but no costs fungicide in col
ha-1)
Seed costs (col piece-1, for annuals col kg-1)
Price first class product (col
kg-1,
for teak and
melina col. m-3 on stem)
Price second class product (col. kg-1, for teak
and melina col. m-3 on stem)