LAND SUITABILITY ASSESSMENTSFOR SELECTED CROPS IN PAPUA NEW GUINEA

B.B. Trangmar, D.J. Giltrap, S.J. Burgham and T.J. Savage

PNGRIS Publication No. 8

prepared byLandcare Research New Zealand Ltd on contract toCommonwealth Scientific and Industrial Research Organisation for Australian Agency for International Development

May 1995

Published by Australian Agency for International Development, Canberra Australia 2601

© Commonwealth of Australia 1995

This work is copyright. Apart from any fan dealing for the purposes of study, research, criticism or review, as permitted under the Copyright Act, no part may be reproduced without written permission of the Australian Agency for International Development and the Papua New Guinea Government.

Bibliographic Reference:B.B. Trangmar, D.J. Giltrap, S.J. Burgham and T.J. Savage. 1995. Land Suitability Assessments for Selected Crops in Papua New Guinea. PNGRIS Publication No. 8 (AusAID: Canberra.) 84p.

National Library of Australia Cataloguing-in-Publication data:

Land suitability assessments for selected crops in Papua New Guinea.

Bibliography.ISBN 0 642 19609 5.

1. Crops and soils - Papua New Guinea - Data processing.2. Land capability for agriculture - Papua New Guinea - Data processmg. 3. Oil palm - Soils - Papua New Guinea.4. Coffee - Soils - Papua New Guinea. I. Trangmar, B. B. (Bruce Blair), 1952- . II. Australian Agency for International Development. III. Landcare Research New Zealand. IV. CSIRO. (Senes : PNGRIS publication ; no. 8).

631.49953

Cover photograph of coastal plam south of KietaEditing, design and typesetting byPenlnfo, Mooloolah Australia 4553Pnnted by Queensland Complete Printing Services, Nambour Australia 4560

ABSTRACT

Methodology for the application of computerized land suitability assessment systems with the Papua New Guinea Resource Information System (PNGRIS) is presented. These systems comprise a land suitability model for arabica coffee production (Harding et al. 1986), tree and cash crops described in the Papua New Guinea Land Evaluation System (PNGLES) (Venema and Daink 1992). The methodology is tested for arabica coffee production and oil palm in Papua New Guinea for the major environment classes in Papua New Guinea (PNG) and for resource mapping units defined in PNGRIS. Land suitability for arabica coffee and oil palm production in PNG is mapped. The computerized land evaluation system for arabica coffee production is described. Results of the land suitability assessments for arabica coffee production based on the Harding et al. (1986) and PNGLES systems are compared. Recommendations are made for further development and validation of the land evaluation systems used. Procedures for data transfer between PNGRIS and the land evaluation systems are described.

IV

ACKNOWLEDGEMENTS

Under an agreement between the Papua New Guinea Government and the Australian Agency for International Development (AusAID), the Commonwealth Scientific and Industrial Research Organisation (CSIRO) has contracted to further develop the Papua New Guinea Resource Information System (PNGRIS) and its applications. This publication is one of a series produced under that contract. The work reported here was conducted by Landcare Research New Zealand Ltd on subcontract to CSIRO

Francis Daink, Land Use Officer, Land Use Section, Department of Agriculture and Livestock, Papua New Guinea is thanked for his assistance in providing land evaluation models of the Papua New Guinea Land Evaluation System (PNGLES) for use in tins study. Dr. Clive Hackett is thanked for his advice in the application of PLANTGRO. Balthazar Wayi, Director. Research Division, Department of Agriculture and Livestock is thanked for his support and advice in developing the methods described in this report.

The assistance of Michael Siri and David Freyne in evaluating the land evaluation results are gratefully acknowledged.

ABBREVIATIONS

ALESAusAIDCSIRODALFAOLRULURLUT

PNGPNGLESPNGRISRMU

Automated Land Evaluation System Australian Agency for International DevelopmentCommonwealth Scientific and Industrial Research Organisation (Australia) Department of Agriculture and Livestock (PNG)Food and Agriculture Organization land resource unit land use requirements land utilisation type Papua New GuineaPapua New Guinea Land Evaluation System Papua New Guinea Resource Information System resource mapping unit

V

CONTENTS

ABSTRACT iii

ACKNOWLEDGEMENTS iv

ABBREVIATIONS iv

1. INTRODUCTION 1

1.1 Background and objectives 1

2. LAND EVALUATION SYSTEMS 2

2.1 Review of land evaluation in PNG 22.2 Arabica coffee land evaluation model 32.3 Papua New Guinea Land Evaluation System (PNGLES) 122.4 PLANTGRO ' 142.5 Other land evaluation systems used in PNG 16

3. METHODOLOGY 18

3.1 Classification of physical environments of PNG 183.2 Land evaluation procedures 203.3 Data transfer from PNGRIS to PLANTGRO 22

4. RESULTS 4

4.1 Physical environments of PNG 234.2 Land suitability for arabica coffee by environment subclass and RMU 264.3 Land suitability for oil palm 344.4 Land suitability for other crops 344.5 PLANTGRO ratings for Upper Ramu test area 36

5. DISCUSSION 38

5.1 Model validation requirements 385.2 Scale factors in land use suitability assessment 385.3 Adjustment of rating criteria in relation to management conditions 395.4 Application of land evaluation models 395.5 Future model development 39

6. CONCLUSIONS 40

REFERENCES 41

ANNEXES

1. Procedures for extracting soils data from PNGRIS for entry to PLANTGRO 432. Definitions of PNGRIS attributes used to define environment subclasses 473. Summary of crop suitability ratings for PNGRIS RMUs and median environment 48

subclasses according to PNGLES

VI

TABLES

1. Drainage/effective soil depth suitability classes for high input coffee production 42. Drainage/effective soil depth suitability classes for low input coffee production 43. Altitude/temperature suitability classes for coffee production 54. Mean annual rainfall suitability classes for coffee production 55. Factors and classes comprising the erosion hazard index 66. Erosion hazard index suitability classes 67. Soil fertility index factors, critical values and index points 78. Soil fertility index suitability classes 79. Suitability classes and points allocated to each diagnostic factor 810. Conversion of suitability points into a land suitability classification 811. Land characteristic classes which required translation from PNGRIS/RAMU 9

databases for use in the Harding et al. (1986) coffee model12. Erodibility classes of Soil Taxonomy great groups in PNG 1013. PNGLES land qualities and land characteristics 1314. Soil data inputs required for PLANTGRO 1515. Climatic data inputs required for PLANTGRO 1516. Environment types, subtypes and subclasses 1917. Environment types, subtypes, areas and number of RMUs 2318. Areas of suitability classes for arabica coffee production based on environment 26

subclasses19. Areas of suitability classes for low input arabica coffee production based on 28

PNGRIS RMUs20. Areas of suitability' classes for high input arabica coffee production based on 29

PNGRIS RMUs21. Comparison of suitability ratings for PNGRIS RMUs using the PNGLES 30

and Harding et al. (1986) low input arabica coffee models22. Comparison of suitability classes for high input arabica coffee production using 32

models of PNGLES and Harding et al. (1986)23. Comparison of suitability classes for low input arabica coffee production using 33

models of PNGLES and Harding et al. (1986)24. Areas of suitability classes for oil palm production based on PNGRIS RMUs 3525. PLANTGRO ratings and main limitations for selected crops and PNGRIS 36

RMUs, Upper Ramu catchment. Eastern Highlands Province

MAPS following 73

1. Major environment types, Papua New Guinea2. Suitability of environment subty pes for low input arabica coffee production (Harding

model)3. Suitability of environment subty pes for high input arabica coffee production (Harding

model)4. Suitability of PNGRIS RMUs for low input arabica coffee production (Harding model)5. Suitability of PNGRIS RMUs for high input arabica coffee production (Harding model)6. Suitability of PNGRIS RMUs for oil palm production

1. INTRODUCTION

1.1 Background and objectives

The Papua New Guinea Resource Information System (PNGRIS) is a micro-computer-based geo- referenced database containing information on natural resources, population distribution, rural land use, small-holder economic activity and land use potential (Bellamy 1986). It is compiled at 1:500 000 scale for approximately 5000 Resource Mapping Units (RMUs) covering the entire land area of Papua New Guinea (PNG).

Two potential applications of PNGRIS are:

1) identification of areas of PNG which, given certain criteria, would be suitable for particular types of land use, and

2) determination of the most suitable type(s) of land use for specific land areas.

The land resource data in PNGRIS is appropriate for broad-scale agricultural land use suitability assessment at national and provincial level. A number of studies have conducted general (Haantjens et al. 1967, Bleeker 1975, Hackett 1988) and crop-specific (Bleeker and Freyne 1981, Vcncma and Daink 1992) agricultural land suitability assessments for PNG. Of these studies, only Venema and Daink (1992) have applied PNGRIS for such purposes. Vcncma and Daink (1992) computed suitability ratings for 22 crops for all PNGRIS RMUs but the results were stored in a database and not mapped. As a result, no spatial assessment of crop suitability was presented.

The structure of most land evaluation systems partition land on the basis of physical and climatic characteristics. A similar partitioning of PNG's physical environment was done by McAlpine (unpub.) in 1987. McAlpine identified 235 discrete environmental combinations in PNG differentiated on the basis of landform, altitude, rainfall, slope, rock type. and inundation. Ideally, physical land suitability for specific crops should be broadly similar within the same environment class when considering land use suitability for different crops on a nation-wide scale. As a result, the environment classes defined by McAlpine should be useful in partitioning the physical environment of PNG for broad-scale agricultural development planning.

The objectives of this study were to:

1) Develop procedures for application of land resource data from PNGRIS with computerized land evaluation systems to assess crop suitability for PNGRIS RMUs and the major environmental types of PNG:

2) Compute land use suitability ratings for a range of crops using coffee as a detailed test crop.3) Prepare sample maps on suitability of major environments and PNGRIS RMUs for selected

cash crops.

This report describes the:

• land evaluation systems used in the study:• procedures used in applying PNGRIS data with these systems;• development and application of a computerized land evaluation system for arabica coffee

production:• results of the crop suitability analyses.

2

2. LAND EVALUATION SYSTEMS

2.1 Review of land evaluation in PNG

Land evaluation systems have been used with land resource survey data in PNG since the 1960’s to assess land suitability for agricultural development. Haantjcns (1963, 1969) developed the ‘"Agricultural Land Classification" for arable crops, tree crops, improved pastures and paddy rice. This system was developed during the CSIRO regional land resource surveys of the 1950's and early 1960's. It is based largely on physical properties of the land. Hartley et al. (1967) defined five potential land use classes for horticulture in a survey in West New Britain. Bleeker (1975) used the main elements of Haantjcns' system to produce national 1:1 000 000 scale maps of physical land limitations (9 classes) and agricultural land use potential for the four major land use types defined by Haantjcns.

These early land evaluation systems were developed for the major groups of agricultural crops and were not crop-specific. As the amount of resource data for PNG has increased and agricultural development has proceeded, a number of crop-specific land evaluation systems were developed. These included systems for cocoa production (Bleeker and Freyne 1981, Wavi 1987), sweet potato (Radcliffe 1983), and coffee (Harding et al. 1986). A crop eco-physical land evaluation system (ECOPFTES) (Hackett 1988) was also introduced as part of PNGRIS. However, ECOPHYS has not been widely used by staff of the Department of Agriculture and Livestock (DAL) Land Use Section and has not been widely tested in PNG.

The system currently used most widely by DAL staff is the Papua New Guinea Land Evaluation System (PNGLES) developed by Venema and Daink (1992). This system is based on the FAO Framework for Land Evaluation (FAO 1976). It provides a systematic land evaluation method for the production of 18 crops at two levels of management input. Decision trees for each of the 18 crops have been developed within the computerized framework provided by the Automated Land Evaluation System (ALES) (Rossitcr and Van Wambeke 1991) Crop suitability ratings for the above crops have been computed for each province in PNG using the land resource data in PNGRIS (1:500 000 scale). PNGLES is currently being tested in soil and land resource surveys at more detailed scale by DAL Land Use Section staff. The PNGLES decision tree models for citrus, potato and pineapple were used as part of the land evaluation process in the Upper Ramu land resource survey (Trangmar et al. 1995a).

A crop physiological evaluation system (PLANTGRO) (Hackett 1991) is also currently being evaluated in PNG. PLANTGRO places more emphasis on crop physiological response to climate and soil factors. It is more appropriate for site-specific crop suitability assessments than the other systems listed above. The other systems have less rigorous climatic parameters and are easier to use with land resource survey data in which climatic variation is described in only a general way As such PLANTGRO complements the other systems for site-specific application.

In this study the arabica coffee model of Harding el al. (1986) was computerized using the ALES package and suitability ratings generated for each environment subclass and PNGRIS RMU. Ratings for all PNGLES models were computed for each environment subclass and PNGRIS RMUs. Procedures for applying PLANTGRO with PNGRIS were developed and tested in a pilot study area.

3

2.2 Arabica coffee land evaluation model

2.2.1 Introduction

Harding et al. (1986) developed a comprehensive system of land evaluation for rainfed arabica coffee production in PNG. The system is also based on the FAO (1976) framework and is a qualitative classification for two land utilization types (LUT). These are a high input system (coffee estates and managed blocks) and a low input system (small-holders).

Seven diagnostic criteria are recommended for evaluating the land suitability of individual sites, namely drainage/effective soil depth, air temperature, mean annual rainfall, erosion hazard, soil chemical fertility, site accessibility and present land use. Land resource data required to derive these criteria are:

Soil drainage class Effective rooting depthAltitude (surrogate for temperature) Mean annual rainfallErosion hazard (rainfall erosivity, soil erodibility, slope)Fertility (total N, P retention, Exch. K, Mg, CEC, pH)

The methodology enables evaluations to be made of individual (uniform) sites and also large areas of variable land, such as provinces or catchments. However, evaluations of large areas of variable land depend on the ability to map the land resource data used to derive the diagnostic criteria to run the model. The system has been validated at provincial scale using Western Highlands as a test area (Harding et al. 1987) and also applied in the Upper Ramu Catchment for land resource data collected at 1:50 000 scale (Trangmar et al. 1995a). In the latter study the classification system was computerized using the ALES framework (Rossiter and Van Wambeke 1991). The computerized version of the model has been installed on computers in DAL Land Use Section, Port Moresby.

Procedures for extracting the relevant land resource data for each resource mapping unit and transferring it into ALES to run the coffee model were similar to those used in the Upper Ramu study (Trangmar et al. 1995a). These procedures were adapted for use with PNGRIS in this study to generate coffee suitability ratings for PNGRIS RMUs and environment subclasses.

2.2.2 Diagnostic Criteria

The key land characteristics and their suitability classes required to run the Harding et al. (1986) coffee model are described briefly below.

Drainage/effective soil depth

The rating for this diagnostic criterion is detenmned from the effective soil depth, the soil depth after drainage. The depth of free-draining soil, the presence or absence of limiting layers, and the presence/absence of slowly permeable horizons are all used to determine the effective soil depth (Tables 1 and 2). These are modified versions of the respective tables in Harding et al (1986), and do not consider the possible limitation of the absence of drainage outlets, because this attribute cannot be compiled on a map unit basis.

In the evaluation for arabica coffee production, the soil is regarded as being unsuitable if the depth to which plant roots can readily penetrate is less than 50 cm (Harding et al. 1986). All soils with an impenetrable layer (e.g. rock, concretionary layer) at depths of less than 50 cm, are rated as unsuitable (R4). Soils with a depth of free-draining soil of less than 20 cm cannot be drained to attain an effective soil depth of more than 50 cm, and are also rated as unsuitable.

4

Soils without an impenetrable layer at depths of less than 50 cm, but with 20-50 cm of free-draining soil, are rated as suitable (R3), provided the soil can be drained to achieve an effective soil depth exceeding 50 cm. In most cases, this can be achieved by installing closely-spaced drams to a depth of 1 m. Drainage is possible in both low- and high-input production systems, so the soils are rated as suitable (Rating R3). However, if the soils have either low vertical- or low horizontal drainage, deep ploughing or mole draining respectively will be required. In high-input production systems, such amelioration techniques are possible. As a result, slowly permeable soils with 20-50 cm of free- draining soil are rated as suitable (R3). In low-input systems mechanized drainage techniques are too costly, and such soils are rated as unsuitable (R4).

Table 1: Drainage/effective soil depth suitability classes for high input coffee production (afterHarding et al. 1986)

Suitability rating Depth of free draining soil (cm)

R1 > 120

R2 50-120

R3 20-49

R4 <50over limiting layer

<20

Table 2: Dramage/cffcctivc soil depth suitability classes for low input coffee production(Source: Harding et al. 1986)

Suitability rating Depth of free draining soil (cm)

Presence of horizons with low vertical1 2 or horizontal“

permeability

R1 > 120 N/A

R2 50-120 N/A

R3 20-49 absent

R4 20-49 present

<50over limiting layer

N/A

<20 N/A

1 Low vertical permeability in any one horizon indicates that deep ploughing is necessary2 Low horizontal permeability indicates that mole draining will be necessary

5

Altitude

Arabica coffee production is directly related to temperature - both very low and very high temperatures can seriously affect production. In PNG a strong correlation exists between temperature and altitude. Altitude is therefore used as a diagnostic criterion (Table 3) for evaluation of suitability for coffee production (Harding et al. 1986).

Table 3: Altitude/temperature suitability classes for coffee production (after Harding et al.1986)

Suitabilityrating

Altitude range (metres a.s.l.)

Maximum annual temp, range

(°C) ^

Minimum annual temp, range

(°C) ^

High input LUT Low input LUT

Highinput

Lowinput

Highinput

Low'input

R1 1300-1699 1300-1699 26-24 26-24 15-13 15-13

R2 1100-1299 1100-1299 28-26 28-26 16-15 16-151700-1899 1700-1899 24-22 24-22 13-12 13-12

R3 1000-1099 600-1099 28-27 31-27 17-16 19-161900-2000 1900-2100 24-21 22-21 12-11 12-11

R4 <1000 <600 >28 >22 >16 >19>2000 >2100 <21 <21 <11 <11

Mean annual rainfall

Mean annual rainfall is an important climatic characteristic for arabica coffee production. Suitability ratings for rainfall classes are given in Table 4.

Table 4: Mean annual rainfall suitability classes for coffee production (Source: Harding et al.1986)

Suitability rating Mean amiual rainfall (mm)

R1 2000-2999

R3 1500-19993000-4000

R4 <1500>4000

6

Erosion hazard

In the humid tropics, rates of natural soil erosion are generally low due to the formation of a protective cover of vegetation. However if the vegetation is removed, accelerated soil erosion occurs, particularly in areas which receive high intensity rainfall (Harding et al. 1986).

In this evaluation, three factors that contribute to soil erosion by water: rainfall erosivity, soil erodibility, and slope, are all used to derive an erosion hazard index, by adding together the index points for each factor (Table 5). The suitability rating is determined from the numerical value of the erosion hazard index (Table 6). The critical values for each suitability rating are different for high-input and low-input coffee production.

Table 5: Factors and classes comprising the erosion hazard index (Source: Harding et al. 1986)

Factor and units Critical values and index points

1 2 3 4

Rainfall Erosivity (erosivity class0

Low Moderate High Very High

Soil erodibility (erodibility class2)

Very Low Low Moderate High

Slope (°) 0-8 9-16 17-32 >32

Rainfall erosivity is a function of maximum monthly precipitation and annual rainfall. Calculations of rainfall erosivity, using data from a network of existing rainfall stations across PNG have been made by Bleeker (1983). A map of rainfall erosivity classes for PNG is given in Harding et al. (1986).

Soil erodibility describes the susceptibility of the soil to erosion, and has been shown to be dependent on the texture, organic matter content, structure, and permeability of the soil. Erodibility classes for PNG soils are given in Table 12.

Table 6: Erosion hazard index suitability classes (after Harding et al. 1986)

Suitabilityrating

Erosion hazard index

High input LUT Low input LUT

R1 3-4 3-5

R2 5-7 6-8

R3 8-10 9-11

R4 11-121 12

l Or any land with slope >32°

7

Soil fertility

The inherent soil fertility is an important parameter for crop growth, particularly in low input management systems. Such systems are largely dependent on the soil for the supply of plant nutrients. In the arabica coffee model, total nitrogen, phosphate retention, exchangeable K, exchangeable Mg, cation exchange capacity, and pH are used to derive a chemical fertility index (Table 7). The importance of each of the above chemical properties to arabica coffee production, and their preferred range is given in Harding et al. (1986). The numerical value of the chemical fertility is used to determine the suitability ratings, the critical values being different for low-input and high-mput production systems (Table 8).

Table 7: Soil fertility index factors, critical values and index points (Source: Harding et al.1986)

Factor and units Critical values and index points

1 2 3 4

Total N (%) <0.10 0.10-0.19 0.20-0.50 >0.50

P retention (%) >95 85-95 60-84 >60

Exch. K (meq %) <0.10 0.10-0.19 0.20-0.60 >0.60

Exch. Mg (meq %) <0.30 0.30-0.59 0.60-1.00 >1.00

ECEC (meq %) <2 2-5 6-10 >10

or CEC (meq %) <5 5-10 11-25 >25

pH <4.5 4.5-4.9 5.0-5.4 5.5-6.5>7.5 7.1-7.5 6.6-7.0

Table 8: Soil fertility index suitability classes (Source: Harding et al. 1986)

Suitability rating Soil fertility index points

High input LUT Low input LUT

R1 >18 >20

R2 13-18 15-20

R3 8-12 10-14

R4 <8 <10

2.2.3 Land Suitability Class

For each of the diagnostic criteria described above, suitability points are assigned to each of suitability7 ratings, according to the relative importance of each (Table 9). The suitability points of a particular land unit are then added together to determine the suitability class (Table 10).

8

Table 9: Suitability classes and points allocated to each diagnostic factor (Source: Harding etal. 1986)

Diagnostic factor Suitability rating and points

R1 R2 R3 R4

Drainage/effective soil depth 12 9 6 0

Altitude 8 6 4 0

Mean annual rainfall 4 n/a 2 0

Erosion hazard 4 3 2 0

Soil fertility 4 3 2 1

Table 10: Conversion of suitability points into a land suitability classification (after Harding etal. 1986)

Suitability points Land suitability class

>27 1 (Highly Suitable)

23-27 2 (Moderately Suitable)

17-22 3 (Marginally Suitable)

<17 or any suitability rating 4 (Not Suitable)scoring zero

2.2.4 Development of the model for arabica coffee production in ALES

A computerized decision tree model of the Harding et al. (1986) arabica coffee land evaluation system was developed using the ALES framework.

The land and soil attributes required to determine the suitability ratings for the diagnostic criteria, and thus the Land Suitability Class of the Harding et al. (1986) coffee model, were set up as Land Characteristics in ALES. However, the corresponding data in the PNGRIS/Upper Ramu databases did not always have the same number of classes and/or the same critical limits as those required by the Harding et al. (1986) model. Intermediate Land Characteristics corresponding exactly to those in the PNGRIS/Upper Ramu databases were set up. Decision trees were also set up that would translate the classes of these Land Characteristics to those of the Land Characteristics required by the ALES model. This was done using “Lc -> LC’ decision trees, where decision trees are set up for the each Land Characteristic required by the Harding model that did not have matching classes in the PNGRIS/Upper Ramu databases.

9

The classes of attributes in the PNGRIS/RAMU data set that required translation are shown in Table 11 below.

Table 11: Land characteristic classes which required translation from the PNGRIS/RAMUdatabases for use in the Harding et al. (1986) coffee model

Land characteristic Land characteristic class in PNGRIS/Upper Ramu databases

Land characteristic class of Harding et al. coffee model

ECEC (meq/100g) <10 2-5

Altitude (m) 600-1200 600-999

1200-1800 1300-1699

1800-2400 2000-2099

1800-2100 1900-1999

Slope (°) 5-10 0-8

10-20 9-16

20-30 17-32

>30 >32

Total Nitrogen (%) <0.2 0.10-0.19

P Retention (%) 60-90 60-84

>90 >95

Exchangeable K (meq/100g) <0.2 0.10-0.19

pH 6-7 6.6-7.0

5-6 5.0-5.4

<5 <4.5

7-8 7.1-7.5

>8 >7.5

The Land Characteristic “depth of free draining soil'5 was derived from the data attribute “drainage class'5 in the PNGRIS/Upper Ramu database as follows:

Drainage Class (PNGRIS/Upper Ramu) Depth of free draining soil

well drained > 120cmimperfectly drained 5 0-120cmpoorly to very poorly drained 20-49cmwaterlogged (swampy) <29cm

Soil erodibility was determined from the Soil Taxonomy great group, using Table A6.1 of Harding et al. (1986). A number of additional correlations were made (Table 12) where the great groups of PNGRIS were not listed by Harding et al. (1986).

10

Table 12: Erodibility classes of Soil Taxonomy great groups in PNG (after Bleeker 1983)

Code Class Soil Taxonomy Descnptiongreat group

1 Very low Histosols Humitropepts Soils with high to very highAndisols organic matter content and

moderate to rapid permeabilities. Granular to fine crumby surface horizons.

2 Low Argiudolls Crvumbrepts Soils with moderate organicHapludolls Rhodudalfs matter content and moderateRendolls Rhodustalfs permeabilities.Argiaquolls PsammaquentsEndoaquolls TropopsammentsEpiaquolls UstipsammentsArgiustolls HaplohumultsCalciustolls PalehumultsHaplustolls Plinthohumults

3 Moderate Cryaquepts Sulfaquents Generally slowly permeableTropaquepts Tropofluvents soils with moderate organicCryochrepts Ustifluvents matter content; the alluvialDystropepts Cryorthents Entisols have low to moderateEutropepts Troporthents organic matter content, areUstropepts Ustorthents massive and may have moderateCryaquents Endoaqualfs very fine sand and silt contentEndoaquents EpiaqualfsEpiaquents HapludalfsFluvaquents HaplustalfsHydraquents Rhodudults

4 High Endoaquerts Albaquults Endoaquerts: Very slowlyHapluderts Endoaquults permeable, often subject toHaplusterts Epiaquults surface scaling and haveHalaquepts Paleaquults prismatic or coarse blockyPlinthaquepts Plinthaquults structures, but moderate organicAlbaqualfs Hapludults matter content.Natrudalfs Paleudults Hapludults and Epiaqualfs:Natrustalfs Plinthudults generally relatively low organicPlinthaqualfs Acrudox matter content and relativelyNatrustolls Hapludox high very fine sand and silt

content. Poorly structured topsoils.

1 1

2.2.5 Land use requirements

The diagnostic criteria (rainfall, altitude, erosion hazard index, chemical fertility index, and drainage/effective soil depth) were established in ALES as Land Use Requirements (LUR) for coffee. The decision trees to determine suitability ratings (severity levels in ALES) followed the tables of Harding et al. (1986) described above. The soil chemical fertility index LUR required multiple land characteristics to be set up as Lc -> Lc decision trees to avoid complex decision trees for the LURs.

In the Upper Ramu study, the severity levels for erosion hazard index of Harding et al. (1986) were adjusted so that slopes exceeding 16 degrees were unsuitable for coffee production in the low input model, and slopes greater than 8 degrees were unsuitable in the high input model (D.F. Freyne pers. comm.). In the present studv the seventy levels for erosion hazard were those described in Harding et al. (1986)

2.2.6 Physical suitability subclass

The physical suitability class (1-4) and subclass (abbreviations of the LURs/diagnostic criteria) were derived by means of a decision tree, using all the possible permutations of the five LURs/diagnostic criteria, in combination with the ‘"maximum limitation" method. The suitability classes 1-4 corresponded to the classes SI (Highly Suitable), S2 (Moderately Suitable), S3 (Marginally Suitable), and S4 (Unsuitable) respectively of Harding et al. (1986).

In the decision tree, a suitability class was allocated to each permutation of the five LURs, using the suitability points defined in Tables 9 and 10. Branching of the decision tree was stopped when any one of the diagnostic criteria was maximally limiting (i.e. had a R4 rating and zero suitability points). This was done to reduce the number of possible permutations and the size of the decision tree. When any one of the diagnostic criteria had a R4 rating, the suitability class allocated was “4”, and the subclass corresponded to the abbreviation of the maximally limiting LUR. Where more than one LUR was maximally limiting, the subclass only included the first maximally limiting LUR and would not include all the correct abbreviations. As a result, the "'decision tree'1 method was combined with the ‘"maximum limitation” method. This was done so that all the LURs which are potentially maximally limiting (i.e. drainage/effective soil depth, erosion hazard, altitude, and rainfall) were identified in the final physical suitability class allocated to each RMU.

2.2.7 Suitability ratings for compound RMUs

Many of the land units of both PNGRIS and the Upper Ramu catchment contain more than one soil great group. Each soil great group is unique, having a discrete set of attributes. This can result in different suitability ratings amongst soil great groups in the same RMU Each soil great group is estimated to occupy a certain area percentage of the RMU in which it occurs. In RMUs containing three different soil great groups it was assumed that the percentage area of the RMU occupied by each great group was 40, 30, 30, respectively. This assumption was made to simplify- the decision tree calculations in ALES. Similarly for those land units with only two soil great groups, great groups were assumed to occupy 60% and 40% of the total area respectively. In each case it was assumed that the dominant great group was listed first in the PNGRIS database.

Once the arabica coffee decision tree models were set up in ALES appropriate data for each land characteristic for each RMU was extracted from PNGRIS (Upper Ramu database in the case of the Upper Ramu study). A number of additional attributes required by the model (e.g. exchangeable Mg)

12

were added. This database was then transferred to FoxPro, whereupon ASCII files of RMU definitions and resource attributes required by the ALES system were compiled. In the ALES framework, evaluations of suitability for arabica coffee production in both low-input and high-input production systems were generated. The resultant suitability classes for each land unit were transferred to a spatial database in Maplnfo from which maps of suitability for arabica coffee production were generated.

The computerized Harding et al. (1986) arabica coffee model was originally developed for the Upper Ramu study (Trangmar et al. 1995a). It was then applied in this study to PNGR1S and used to generate the ratings shown in Maps 1 -5.

2.3 Papua New Guinea Land Evaluation System (PNGLES)

DAL Land Use Section currently uses the Papua New Guinea Land Evaluation System (PNGLES) either manually or through the Automated Land Evaluation System (ALES) package to assess land suitability for crop production. The mam concepts of PNGLES are summarized here. Full details of PNGLES are given in Venema and Daink (1992).

2.3.1 PNGLES concepts

PNGLES is based on the FAO concepts of matching land qualities with land use requirements of a specific land utilization type (LUT) (FAO 1976) to derive suitability ratings. The matching process is conducted using a decision tree framework established for each LUT within the Automated Land Evaluation System (ALES) of Rossiter and Van Wambeke (1991). Decision trees are given for small­holder production of 18 crops (cardamom, cashew, chillies, citrus, cocoa, coconut, coffee arabica and robusta, maize, mango, oil palm, pepper, pineapple, potato, pyrethrum, rubber, upland rice, and vanilla) under low input conditions and 8 crops (cocoa, coffee arabica and robusta, maize, oil palm, potato, upland rice, rubber) under medium to high input, smallholder production conditions. Suitability ratings of each land quality in relation to that required for optimum production of each crop are determined using the decision trees. The suitability rating for most limiting land quality is used to determine the overall suitability of the mapping unit for the specified land utilization type. All land utilization types, crop requirements, and decision trees are given in Venema and Daink (1992).

PNGLES was initially set up to utilize the land resources data stored in PNGRIS, but can be applied at more detailed scales (Trangmar et al. 1995a). It currently uses eight land qualities derived from 26 land characteristics (Table 13).

2.3.2 Automated Land Evaluation System (ALES)

The ALES framework provides a systematic structure and process through which to derive the land evaluation ratings for each LUT. A particular strength of ALES is that provides a user-friendly query system through which the user can ask questions about why particular ratings were derived. ALES also has the potential capability to determine gross margins for crop production if production costs, prices, and yield data are known or can be derived for each LRU. Full details of using ALES are given in the user manual (Rossiter and Van Wambeke 1991) and are not explained here. The yield and gross margins components of ALES and PNGLES have yet to be tested in PNG.

13

Table 13: PNGLES land qualities and land characteristics

Land qualities Land characteristics/attributes

Temperature Mean max. temperature Mean min. temperature

Erosion hazard SlopeSoil erodibility

Moisture conditions Mean annual rainfall Rainfall seasonality Soil texture (topsoil, subsoil) Landform

Nutrient availability pHMineral reserve Anion fixation

Potential for mechanization

Stonmess/rockinessSlopeTopsoil texture

Rooting conditions Effective soil depthSoil texture (topsoil, subsoil)Stonmess/rockiness

Nutrient availability/ retention capacity

pHCECTopsoil texture Anion fixation

Oxygen availability Soil drainage

Soil toxicities Acid sulphate Calcium carbonate

Excess of salts SalinitySodicity

In addition to the existing decision tree models for the crops developed in PNGLES by Venema and Daink (1992) ALES provides the framework to develop such models for other crops not presently included in PNGLES.

2.3.3 Data transfer between land resource databases and ALES/PNGLES Models

The relevant land characteristics for each RMU can be readily extracted from PNGRIS database and exported to ALES to conduct the crop suitability ratings using the PNGLES models. The crop suitability ratings were computed for each RMU in PNGRIS (Venema and Daink 1992). The ratings can then be reimported back to FoxPro and Maplnfo for map plotting or spatial analyses with the other land resource attributes recorded.

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2.4 PLANTGRO

2.4.1 Concepts

PLANTGRO is crop physiology-based computerized system of assessing land suitability for crop growth (Hackett 1991). It operates by matching the environmental requirements for optimum growth of specific crops with the climatic and soil conditions of the site being evaluated. It uses empirical qualitative crop growth response curves for each climatic and soil factor and matches them with the climatic and soils conditions for the site being evaluated. Limitations of each climatic and soil parameter of the site is assessed for the crop on a 0-9 scale (0 representing optimum growth, 9 causing plant death) In assessing the overall rating for the site PLANTGRO uses the maximum limitation method in which crop growth is constrained to the level or rating imposed by the most limiting factor. The overall ratings are presented in terms of maximum limitation rating tables or graphs.

2.4.2 Crop growth response functions in PLANTGRO

PLANTGRO contains crop growth response functions for the following climate and soil/site parameters:

Climate: Soils/Site:

Daylength, solar radiation Tolerance to brief and extended cold Tolerance to heat damage Thermal units Crop development units Water availability Seasonal water logging FloodingProneness to wind damage

Aeration Base saturation Cation exchange capacity Effective rooting depth pHN, P. K Salinity SlopeSoil texture

PLANTGRO currently contains these growth functions for over 100 crops. The crop growth response files have been developed using documented information and expert consultation throughout the world Growth functions can be developed for additional crops quite easily once their response to each climate and soil factor is known. Existing functions can be easily edited to take into account cultivar or varietal differences.

2.4.3 Soil data input to PLANTGRO

Soils data can be entered directly to PLANTGRO or existing generalized soil files for particular types of soils (e.g. sandy, clay soils) can be used if the soil type at a particular site or area is not known. It is recommended that actual soil data be used wherever possible. Soil data required by PLANTGRO is given in Table 14. Soil chemical properties listed in Table 14 apply to the topsoil layer. All of the soils data can be derived from PNGRIS usmg the procedures given in Annex 1.

15

Table 14: Soil data inputs required for PLANTGRO

Soil chemical properties (Topsoil)

Soil physical/site properties

Base saturation (%) Aeration (Drainage Class)

Cation exchange capacity (meq/100g) Effective rooting depth (cm)

Nitrogen (%) Slope (degrees)

Available phosphorus (ppm) Depth (Layers A, B, C) (cm)

Exchangeable potassium (meq/100g) Texture (Layers A, B, C) (8 classes)

Salinity (dS/m) Available water holding capacity (Layers A, B) (cm/m)

Drainable water holding capacity (Layers A, B, C) (cm/m)

2.4.4 Climatic data inputs to PLANTGRO

Climatic data used in PLANTGRO (Table 15) should be derived from weather stations with more than 15 years of established record. Climatic data can be entered and crop growth analyses conducted for monthly, 10-daily, or weekly periods. Monthly data is most commonly used and is most relevant to crop suitability assessments for land resource surveys. 10-daily or weekly data may be used where particular crop physiological responses to specific weather conditions are to be assessed. Average data over the years of record or actual data for specific monthly, 10-daily or weekly periods can be entered.

Climatic data for PNG stations can be obtained from McAlpine et al. (1975) and entered to PLANTGRO. In running PLANTGRO for a particular survey area, data for the nearest or most relevant climate station is selected from the climate data menu.

Table 15: Climatic data inputs required for PLANTGRO

Data Type Data Type

Rainfall (mm)

Irrigation water applied (mm)

Daylength - average daily (hrs)

Temperature - mean daily max (°C)

Temperature - Brief cold - (e.g. mm. less 6°C)

Wind - extreme (km/hr)

Evaporation (mm)

Flooding risk value (1 or 0)

Solar radiation - average daily (Mj/m2/day)

Temperature - mean daily min. (°C)

Wind - average (km/hr)

16

2.4.5 Making crop growth predictions using PLANTGRO

Single or multiple crop suitability predictions can be computed at any one time for any combinations of crops, soils and climates. As a result, PLANTGRO is versatile in that many different crops can be compared at the same time for a particular soil and climate combination; the suitability of a particular crop can be evaluated for several different climate zones at the same time; it can also be used to assess appropriate planting times for particular crops during the year and assess crop rotation sequences for different crops under a given set of soil and climate data. PLANTGRO can also be used to assess development stages of the crop growth in response to environmental conditions. It also enables crop suitability assessments to be made for several soils and crops within a land unit.

PLANTGRO output is in the form of tables of limitation ratings for each soil and climate factor for each crop, and an overall limitation rating based on the maximum limiting rating for the individual factors. This can also be displayed graphically onscreen and results saved to a file for hard copy output. PLANTGRO also has a capability for output files to be converted to GIS format although this has not been attempted in PNG as yet.

Full details of operating PLANTGRO are given in the manual by Hackett (1991).

At the present time in PNG, PLANTGRO is best used for making crop growth predictions for particular sites or for land resource surveys with a small number of LRUs. As a result, it is quite complementary to the PNGLES suite of land evaluation models which were designed to be used with large land resource survey data sets through use of the ALES framework.

In contrast to ALES and PNGLES, PLANTGRO does not readily take management conditions into account although these can be simulated by adjusting the soil input data to match different management input levels (e.g. fertilizer, drainage, irrigation). It does not contain economic analysis modules.

PLANTGRO is not well structured to access a large land resource survey database. Its current output formats require much editing before it can be directly incorporated with a relational database for subsequent plotting with a GIS package.

2.5 Other land evaluation systems used in PNG

Land evaluation systems for a number of specific crops (including cocoa, sweet potato, coconuts) have been developed for use with land resource data in PNG. Several of these are briefly described below'. Full details of each system are given in the cited references which the reader is encouraged to consult.

2.5.1 Cocoa

Bleeker and Freyne (1981) developed a land evaluation system for cocoa based on the relative severity of nine types of physical limitations. They used this system to map areas of PNG which are suitable for cocoa production. Wayi (1987) also proposed a system of land evaluation for cocoa production at three levels of management based on the FAO (1976) framework.

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2.5.2 Sweet Potato

Radcliffe (1983) also applied the FAO framework for land evaluation to assess contrasting land types in Southern Highlands Province for sweet potato production under the traditional Mendi cultivation system. Radcliffe’s sweet potato evaluation system could probably adapted for application in other parts of PNG.

2.5.3 Coconuts

Marques (1994) developed a land evaluation system for small-holder coconut production in PNG using the FAO framework. ALES was used as the framework within which to develop the system. The system was applied to Madang, Gulf and North Solomons Provinces as test areas using land resource data from PNGRIS. Maps of coconut suitability classes were plotted using Maplnfo and areas of different suitability classes calculated (Marques 1994). The ALES model for coconut production has been installed on computers in DAL Land Use Section in Port Moresby.

2.5.4 Other Crops

Description of environmental requirements for specific crops is beyond the scope of this handbook. A bibliography of information on 26 crops is given in Venema and Daink (1992). This and the plant growth response functions given in PLANTGRO (Hackett 1991) should be consulted if new land evaluation systems for particular crops are to be developed or existing ones improved on.

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3. METHODOLOGY

3.1 Classification of physical environments of PNG

The RMUs of the PNGRIS database were classified into the major environment subclasses based on the criteria defined by McAlpine (unpub.).

The classification includes 14 major environment types separated initially on the basis of major landform type (Table 16). These environment types were then subdivided according to altitude and rainfall classes. These subtypes are:

Altitude class: Rainfall class:

low (0-600m)mid (600-1200m)high (1200-2400m)very high (>2400m)

dry (1000-2000mm)wet (2000-4000mm)very wet (>4000mm)

It should be noted that not all environment types contain subtypes of each altitude and/or rainfall class. For example, mangrove swamp environments only occur in the low altitude class, but occur in all three rainfall classes. As a result, there are three environment subtypes:

Mangrove Swamps:Low altitude (0-600 m), dry (1000-2000 mm)Low altitude (0-600 m), wet (2000-4000 mm)Low altitude (0-600 m), very wet (> 4000 mm).

Each environment subtype has been further subdivided into a number of environment subclasses, based either on inundation type, slope, rock type, or dominant rock type grouping (Table 16). The environment subclasses are defined according to the PNGRIS classes for each of these land resource factors (Annex 2).

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Table 16: Environment types, subtypes and subclasses

Environment type Subtypes Subclasses Maximum number of

subclasses1 per subtype

Altitude Rainfall

Mangrove Swamps Low DryWetVery wet

- -

Estuaries and Coastal Plains

Low DryWetVery wet

Inundationtype

4

Beach Ridges and Plains

Low DryWet

- -

Raised Coral Reefs Low DryWetVery wet

Slope 3

Recent Alluvial Plains Low High Very high

Dry (L,H alt.)Wet (L,H,VH alt.) Very wet (L alt.)

Inundationtype

4

Relict Alluvial Plains Low DryWetVery wet

Inundationtype

3

Alluvial Floodplains Low DryWetVery wet

Inundationtype

3

Swamps Low Mid High Very high

Dry (L alt.)Wet (L,H,VH alt.) Very wet (L,M alt.)

Inundationtype

4

Alluvial Fans Low Mid High Very high

Dry (L,M,H alt.)Wet (L,M,H,VH alt.) Very wet (L,M,H alt.)

Rock type 3

Volcanic Plains and Fans

Low Mid High Very high

Dry (L alt.)Wet (L,M,H,VH alt.) Very wet (L,M,H alt.)

Slope 3

Volcanic Plateaux and Cones

LowMidHighVery high

Wet (L,M,H,VH alt.) Very wet (L,M,H alt.)

Slope 3

(continued)

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Environment type

Altitude

Subtypes

Rainfall

Subclasses Maximum number of

subclasses1 per subtype

Steeplands - Karst LowMidHighVery high

Wet (L,M,H alt.) Very wet (L,M,H.VH alt.)

Slope 3

Hills LowMidHighVery high

Dry (L,M,H alt.)Wet (L,M,H,VH alt.) Very7 wet (L,M,H alt.)

Dominant rock type grouping

62

Mountains LowMidHighVery high

Dry7 (L,M,H,VH alt.) Wet (L,M,H,VH alt.) Very wet (L,M,H,VH alt.)

Dominant rock type grouping

5

Not all possible subclasses occur within each subtype.

Includes additional subclass not previously defined, for those RMUs which did not fit any of the subclass criteria

A FoxPro database file (RMUENV.DBF) containing all the RMUs from the PNGRIS database classified to environment subclass level has been installed on computers in the Land Use Section, Department of Agriculture and Livestock (DAL).

3.2 Land evaluation procedures

3.2.1 Evaluation models

Crop suitability ratings for all PNGRIS RMUs and the environment subclasses of McAlpine (unpub.) were evaluated using the PNGLES models (Venema and Daink 1992), except for arabica coffee for which the Harding et al. (1986) model was used.

The decision trees of the PNGLES models were used without modification, except for the vanilla model (low input). This model produced nil ratings for three RMUs in Central Province. The decision tree for the Tooting conditions' LUR caused the nil ratings for the particular combination of soils for these RMUs. The particular combination in question was soils with an effective soil depth of 50-100 cm, and a subsoil texture of class 6 (rock). The decision tree was altered such that this combination gave a class 2 rating for 'rooting conditions', the lowest rating elsewhere in the decision tree for all other subsoil textures and an effective soil depth of 50-100 cm.

For the Harding et al. (1986) coffee model only the decision tree for erosion hazard index (for low input production) was altered from that of the original model. In the decision tree of the original model an R4 rating is achieved only when a land unit has slopes >32 degrees, high rainfall erosivity and high

21

soil erodibility. The model was adjusted such that all units with slopes >32 degrees were given a R4 rating, including those with a higher rating for rainfall erosivity and soil erodibility.

3.2.2 Missing data from PNGRIS

Data for the rainfall erosivity (a required PNGLES land characteristic) is not part of the PNGRIS data set. Rainfall erosivity for each RMU was derived by overlaying the rainfall erosivity map given in Harding et al. (1986) onto an RMU map of each province. Maplnfo and FOXPRO were then used to compile rainfall erosivity classes for each RMU into a database which was then linked to the PNGRIS database.

3.2.3 Suitability determinations for RMUs containing more than one soil great group

For crops evaluated using the PNGLES models, suitability ratings for RMUs containing more than one soil great group were derived using the method described in Venema and Daink (1992). For the arabica coffee evaluation, the final rating of RMUs with more than one soil great group was based on the dominant soil in the RMU.

3.2.4 Calculation of median environment subclass ratings

All RMUs were classified according to the 'Natural Resource Classification from PNGRIS’ (McAlpine unpub.) down to environment subclass, altitude subclass and rainfall subclass level. The median crop suitability rating for all RMUs occurring in a particular environment subclass within a particular province was then calculated for each of a set of unique classification identifiers that occurred in each province. The median rating was then attached back to the RMUs using the unique classification identifier.

3.2.5 Data transfer and crop evaluations

The Harding et al. (1986) arabica coffee model required land unit definitions to be set up in the ALES database. These were created in FoxPro and exported to ALES using ASCII comma-delimited files. The attributes from PNGRIS required by the Harding arabica coffee model were also transferred from PNGRIS (FoxPro database files) to ALES using ASCII comma-delimited text files. The evaluations for all crops were then run and the results exported to an ASCII file, using the commands “File Evaluation Results”, ‘'Suitabilities” in the ‘'Evaluations” sub-menu (Rossiter and Van Wambeke 1991).

The PNGLES models already contained a complete attribute database for all RMUs, and so did not require attributes to be transferred from PNGRIS. The crop evaluations were run for these models, and results exported as above.

The files containing the evaluation results for the Harding arabica coffee model and the PNGLES models, were imported into FoxPro and database files created. The database files for the Harding arabica coffee model and the PNGLES models were processed differently to attach individual ratings to each of the provincial RMUs. The database files for the Harding model were filtered to remove the results for RMU constituents. The ALES evaluation results file contains an evaluation rating (integer physical suitability subclass) for each RMU that corresponds to that of the first-named soil constituent (which by PNGRIS definitions occupies 40% of each RMU). It is this rating which was then attached to each provincial RMU, in individual database files for each province/crop combination.

22

In the case of the results files from the PNGLES models the evaluation ratings for the individual soil constituents were used in combination to derive the rating for the RMU. This was done according to the method of Venema and Daink (1992). A macro was written in FoxPro to do this. The resultant whole-RMU rating was stored as a single field in individual database files for each province/crop combination.

The individual files containing evaluation ratings for each province were then combined to create a single file containing all the RMUs for each province in PNG for each crop. These files were then transferred to Maplnfo for map plotting.

3.3 Data transfer from PNGRIS to PLANTGRO

The PLANTGRO crop evaluation package (Hackett 1991) has not been evaluated for use with PNGRIS to date. The data input structures for PLANTGRO are not readily suited for application with large resource survey databases. These difficulties largely relate to extracting soils data from multi­factor resource databases and adapting them to PLANTGRO's input fonnats. Procedures w7ere developed to extract soils data from PNGRIS for application with PLANTGRO. These procedures are described in full in Annex 1. The procedures were tested for 19 PNGRIS RMUs in the Upper Ramu catchment in Eastern Highlands Province. PLANTGRO analyses for these RMUs were conducted for arabica coffee, sweet orange, pineapple, Irish potato and yam. Climate data from Aiyura (McAlpine et al. 1975) was used for the climatic parameters required by PLANTGRO for the 19 test RMUs.

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4. RESULTS

4.1 Physical environments of PNG

The distribution of the 14 major physical environment types in PNG are shown in Map 1. The enviromnent types were further subdivided into a total of 235 subclasses (McAlpine unpub.) whose distribution is too complex to be shown at the map scale (1:2 000 000) used in Map 1. A data file classifying each PNGRIS RMU into one of the 235 enviromnent subclasses has been installed on the computers in the Land Use Section, DAL.

The areas of major environment types and subtypes are summarized in Table 17. Results indicate that mountains (41%), relict alluvial plains (13%), swamps (8%) and hills (7%) are the most common environment types in PNG.

Table 17: Environment types, subtypes, areas and number of RMUs

E n v ir o n m e n t T o ta l a r e a

( k m 2)

N u m b e r o f

R M U s

M a n g r o v e s w a m p sL o w a l t i tu d e ( 0 - 6 0 0 m ) , d ry (1 0 0 0 - 2 0 0 0 m m ) 1861 35

L o w a l t i tu d e (0 - 6 0 0 m ) , w e t ( 2 0 0 0 4 0 0 0 m m ) 2 3 2 6 53

L o w a l t i tu d e ( 0 - 6 0 0 m ) , v e ry w e t ( > 4 0 0 0 m m ) 1511 1 0

T o ta l - M a n g ro v e s w a m p s 5 6 9 8 9 8

E s tu a r ie s a n d C o a s ta l P la in s

L o w a l t i tu d e ( 0 - 6 0 0 m ) , d ry ( 1 0 0 0 - 2 0 0 0 m m ) 1001 16

L o w a l t i tu d e (0 -6 0 0 m ) , w e t ( 2 0 0 0 - 4 0 0 0 m m ) 1059 4 2

L o w a l t i tu d e ( 0 -6 0 0 m ) , v e ry w e t ( > 4 0 0 0 m m ) 3 8 4 _9

T o ta l - E s tu a r ie s a n d c o a s ta l p la in s 2 4 4 4 6 7

B e a c h R id g e s a n d P la in sL o w a l t i tu d e (O -öO O m j, d ry ( 1 0 0 0 - 2 0 0 0 m m j 7 6 8 17

L o w a l t i tu d e (0 - 6 0 0 m ) , w e t ( 2 0 0 0 - 4 0 0 0 m m ) 1 1 2 3 52

T o ta l - B e a c h r id g e s a n d p la in s 1891 6 9

R a is e d C o ra l R e e f sL o w a l t i tu d e ( 0 - 6 0 0 m ) , d ry (1 0 0 0 - 2 0 0 0 m m ) 95 7

L o w a l t i tu d e (0 - 6 0 0 m ) , w e t ( 2 0 0 0 4 0 0 0 m m ) 5 3 1 2 156

L o w a l t i tu d e (0 - 6 0 0 m ) , v e ry w e t ( > 4 0 0 0 m m ) 2 1 6 3 _36

T o ta l - R a is e d c o ra l r e e f s 7 5 7 0 199

R e c e n t A l lu v ia l P la in s

L o w a l t i tu d e (0 -6 0 0 m ) , d ry (1 0 0 0 - 2 0 0 0 m m ) 3 2 1 7 4 4

L o w a l t i tu d e ( 0 -6 0 0 m ) , w e t (2 0 0 0 - 4 0 0 0 m m ) 14191 150

L o w a l t i tu d e ( 0 -6 0 0 m ) , v e ry w e t (> 4 0 0 0 m m ) 2 6 7 6 4 5

H ig h a l t i tu d e (1 2 0 0 - 2 4 0 0 m ) , d ry (1 0 0 0 - 2 0 0 0 m m ) 102 9

H ig h a l t i tu d e (1 2 0 0 - 2 4 0 0 m ) , w e t ( 2 0 0 0 - 4 0 0 0 m m ) 6 6 5 11

V e ry h ig h a l t i tu d e ( > 2 4 0 0 m ) , w e t ( 2 0 0 0 4 0 0 0 m m ) 131 _ 9

T o ta l - R e c e n t a l lu v ia l p la in s 2 0 9 2 8 2 6 8

R e l ic t A l lu v ia l P la in s

L o w a l t i tu d e ( 0 -6 0 0 m ) , d ry (1 0 0 0 - 2 0 0 0 m m ) 2 2 9 6 0 2 8

L o w a l t i tu d e (0 -6 0 0 m ) , w e t ( 2 0 0 0 4 0 0 0 m m ) 2 1 4 9 1 2 0

L o w a l t i tu d e ( 0 -6 0 0 m ) , v e ry w e t ( > 4 0 0 0 m m ) 1 5 3 6 3 U

T o ta l - R e l ic t a l lu v ia l p la in s 5 9 8 1 4 59

( c o n t in u e d )

24

E n v iro n m e n t T o ta l a rea (k m 2)

N u m b e r o f R M U s

A llu v ia l F lo o d p la in sL o w a ltitu d e (0 -6 0 0 m ), d ry (1 0 0 0 -2 0 0 0 n u n ) 3364 27L o w a ltitu d e (0 -6 0 0 m ), w e t (2 0 0 0 -4 0 0 0 n u n ) 19582 89L o w a ltitu d e (0 -6 0 0 in ), very w e t (> 4 0 0 0 m m ) 4 4 9 9 _24T o ta l - A llu v ia l flo o d p la in s 27445 140

S w am p sL o w a ltitu d e (0 -6 0 0 m ), d ry (1 0 0 0 -2 0 0 0 m m ) 4 5 6 9 33L o w a ltitu d e (0 -6 0 0 m ), w e t (2 0 0 0 -4 0 0 0 n u n ) 2 5995 168L o w a ltitu d e (0 -6 0 0 m ), very w e t (> 4 0 0 0 m m ) 4 5 2 9 21M id a lti tu d e (6 0 0 -1 2 0 0 m ), very w e t (> 4 0 0 0 m m ) 128 5H ig h a lti tu d e (1 2 0 0 -2 4 0 0 m ), w e t (2 0 0 0 -4 0 0 0 m m ) 13 1V ery h ig h a lti tu d e (> 2 4 0 0 m ), w e t (2 0 0 0 -4 0 0 0 m m ) 14 J _T o ta l - S w am p s 35248 229

A llu v ia l F an sL ow a ltitu d e (0 -6 0 0 m ), d ry (1 0 0 0 -2 0 0 0 m m ) 4 4 5 8 41L o w a ltitu d e (0 -6 0 0 m ), w e t (2 0 0 0 -4 0 0 0 n u n ) 7964 111L o w a ltitu d e (0 -6 0 0 m ), very w e t (> 4 0 0 0 m m ) 907 11M id a lti tu d e (6 0 0 -1 2 0 0 m ), d ry (1 0 0 0 -2 0 0 0 m m ) 89 3M id a lti tu d e (6 0 0 -1 2 0 0 m ), w e t (2 0 0 0 -4 0 0 0 m m ) 539 14M id a lti tu d e (6 0 0 -1 2 0 0 m ), very w e t (> 4 0 0 0 m m ) 86 3H ig h a lti tu d e (1 2 0 0 -2 4 0 0 m ), d ry (1 0 0 0 -2 0 0 0 m m ) 181 6H ig h a lti tu d e (1 2 0 0 -2 4 0 0 m ), w e t ( 2 0 0 0 4 0 0 0 m m ) 1542 39H ig h a lti tu d e (1 2 0 0 -2 4 0 0 m ), very w e t (> 4 0 0 0 m m ) 114 3Very' h ig h a lti tu d e (> 2 4 0 0 m ), w e t (2 0 0 0 -4 0 0 0 m m ) 76 _ 3T o ta l - A llu v ia l fans 15956 234

V olcan ic P la in s a n d F an sL ow a ltitu d e (0 -6 0 0 m ), d ry (1 0 0 0 -2 0 0 0 n u n ) 47 1L ow a ltitu d e (0 -6 0 0 m ), w e t (2 0 0 0 4 0 0 0 m m ) 10197 88L ow a ltitu d e (0 -6 0 0 m ), very' w e t (> 4 0 0 0 m m ) 7761 29M id a lti tu d e (6 0 0 -1 2 0 0 m ), w e t ( 2 0 0 0 4 0 0 0 m m ) 1167 19M id a lti tu d e (6 0 0 -1 2 0 0 m ), very w e t (> 4 0 0 0 n u n ) 1710 18H ig h a lti tu d e (1 2 0 0 -2 4 0 0 m ), w e t ( 2 0 0 0 4 0 0 0 m m ) 3286 38H igh a lti tu d e (1 2 0 0 -2 4 0 0 m ), very w e t (> 4 0 0 0 m m ) 452 8V ery h ig h a lti tu d e (> 2 4 0 0 m ), w e t ( 2 0 0 0 4 0 0 0 n m i) 711 _ 9T o ta l - V o lcan ic p la in s an d fans 25331 212

V olcan ic P la te a u x an d C o n esL o w a ltitu d e (0 -6 0 0 m ), w e t ( 2 0 0 0 4 0 0 0 m m ) 3618 77L o w a ltitu d e (0 -6 0 0 m ), very w e t (> 4 0 0 0 n u n ) 40 4 18M id a lti tu d e (6 0 0 -1 2 0 0 m ), w e t ( 2 0 0 0 4 0 0 0 m m ) 2665 36M id a lti tu d e (6 0 0 -1 2 0 0 m ), very w e t (> 4 0 0 0 m m ) 802 11H ig h a lti tu d e (1 2 0 0 -2 4 0 0 m ), w e t ( 2 0 0 0 4 0 0 0 m m ) 2 084 36H ig h a lti tu d e (1 2 0 0 -2 4 0 0 m ), very w e t (> 4 0 0 0 m m ) 836 16V ery h ig h a lti tu d e (> 2 4 0 0 m ), w e t ( 2 0 0 0 4 0 0 0 m m ) 994 _ L ZT o ta l - V o lcan ic p la te a u x a n d cones 11403 211

(c o n tin u ed )

25

E n v iro n m e n t T o ta l a rea N u m b e r o f(k m 2) R M U s

S te e p la n d s , K a rs tL ow a ltitu d e (0 -6 0 0 m ), w e t ( 2 0 0 0 4 0 0 0 m m ) 169 28L o w a ltitu d e (0 -6 0 0 m ), very w e t (> 4 0 0 0 m m ) 7 518 25M id a lti tu d e (6 0 0 -1 2 0 0 m ), w e t (2 0 0 0 -4 0 0 0 m m ) 4 0 7 6 21M id a lti tu d e (6 0 0 -1 2 0 0 m ), very w e t (> 4 0 0 0 m m ) 3 897 21H ig h a lti tu d e (1 2 0 0 -2 4 0 0 m ), w e t (2 0 0 0 -4 0 0 0 m m ) 3697 26H ig h a lti tu d e (1 2 0 0 -2 4 0 0 m ), very w e t (> 4 0 0 0 m m ) 1524 21V ery h ig h a lti tu d e (> 2 4 0 0 m ), w e t (2 0 0 0 4 0 0 0 m m ) 1447 14V ery h ig h a lti tu d e (> 2 4 0 0 m ), w e t (2 0 0 0 4 0 0 0 m m ) 573 J JT o ta l - S te ep la n d s , k a rs t 2731 25463 169

H illsL o w a ltitu d e (0 -6 0 0 m ), d ry (1 0 0 0 -2 0 0 0 m m ) 5796 58L o w a ltitu d e (0 -6 0 0 m ), w e t (2 0 0 0 4 0 0 0 m m ) 17929 192L ow a ltitu d e (0 -6 0 0 m ), very w e t (> 4 0 0 0 m m ) 4 3 0 8 30M id a lti tu d e (6 0 0 -1 2 0 0 m ), dry (1 0 0 0 -2 0 0 0 m m ) 323 3M id a lti tu d e (6 0 0 -1 2 0 0 m ), w e t ( 2 0 0 0 4 0 0 0 m m ) 49 9 16M id a lti tu d e (6 0 0 -1 2 0 0 m ), very w e t (> 4 0 0 0 m m ) 4 70 10H ig h a lti tu d e (1 2 0 0 -2 4 0 0 m ), d ry (1 0 0 0 -2 0 0 0 m m ) 264 5H ig h a lti tu d e (1 2 0 0 -2 4 0 0 m ), w e t ( 2 0 0 0 4 0 0 0 m m ) 2 334 44H ig h a lti tu d e (1 2 0 0 -2 4 0 0 m ), very w e t (> 4 0 0 0 m m ) 2 633 1V ery h ig h a lti tu d e (> 2 4 0 0 m ), w e t ( 2 0 0 0 4 0 0 0 m m ) 425 _ioT o ta l - H ills 32383 269

M o u n ta in sL o w a ltitu d e (0 -6 0 0 m ), d ry (1 0 0 0 -2 0 0 0 m m ) 4 7 8 7 36L o w a ltitu d e (0 -6 0 0 in ), w e t ( 2 0 0 0 4 0 0 0 m m ) 43911 365L o w a ltitu d e (0 -6 0 0 m ), very w e t (> 4 0 0 0 m m ) 19886 157M id a lti tu d e (6 0 0 -1 2 0 0 m ), dry (1 0 0 0 -2 0 0 0 m m ) 567 11M id a lti tu d e (6 0 0 -1 2 0 0 m ), w e t ( 2 0 0 0 4 0 0 0 m m ) 36433 302M id a lti tu d e (6 0 0 -1 2 0 0 m ), very w e t (> 4 0 0 0 m m ) 11657 123H ig h a lti tu d e (1 2 0 0 -2 4 0 0 m ), d ry (1 0 0 0 -2 0 0 0 m m ) 2 180 24H ig h a lti tu d e (1 2 0 0 -2 4 0 0 m ), w e t ( 2 0 0 0 4 0 0 0 m m ) 4 7 9 7 2 482H ig h a lti tu d e (1 2 0 0 -2 4 0 0 m ), very w e t (> 4 0 0 0 m m ) 6725 121V ery h ig h a lti tu d e (> 2 4 0 0 m ), dry (1 0 0 0 -2 0 0 0 m m ) 99 2V ery h ig h a lti tu d e (> 2 4 0 0 m ), w e t (2 0 0 0 4 0 0 0 m m ) 12311 187V ery h ig h a lti tu d e (> 2 4 0 0 m ), very w e t (> 4 0 0 0 m m ) 47 9 17T o ta l - M o u n ta in s 187007 1828

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4.2 Land suitability for arabica coffee by environment subclass and RMU

4.2.1 Suitability for arabica coffee based on environment subclass

The suitability of arabica coffee production (Harding et al. 1986) based on analysis of median ratings for each environment subclass are shown in Maps 2 (low input) and 3 (high input). Map 2 indicates that small areas of highly suitable land occur near Goroka in Eastern Highlands Province, Mt Hagen in Western Highlands Province and in Southern Highlands Province. Large areas with moderate suitability occur along the main axial ranges of the PNG mainland with small areas at higher elevations in East New Britain, New Ireland and the islands in Milne Bay Province.

Map 3 also indicates that the same areas which are highly suited for low input coffee production are also well suited for high input production. However, a much smaller area is moderately suited and is restricted to parts of the Southern Highlands, Enga, Western Highlands, Eastern Highlands and Morobe Provinces with small areas in Central and Northern Provinces. The total areas of suitability class based on analysis of environment subclasses are given in Table 18.

Table 18: Areas of suitability classes for arabica coffee production based on environmentsubclasses

Input level Suitability class Total

High Moderate Marginal Not suitable

Low (km2) 2909 61370 22068 372288 458635

Low (%) 0.6 13.4 4.8 81.2 100

High (km2) 3131 10316 732 444456 458635

High (%) 0.7 2.2 0.2 96.9 100

4.2.2 Suitability for arabica coffee based on RMUs

The suitability of arabica coffee production based on application of the Harding et al. (1986) model for each of the PNGRJS RMUs is shown in Maps 4 (low input) and 5 (high input). The suitability ratings determined on analysis of each RMU are summarized in Tables 19 (low input) and 20 (high input) for each province. About 4% of PNG has high physical suitability under either low or high input management (Tables 19, 20). These areas occur mainly in the 1300-1700 altitude range in rolling terrain in West Sepik, Southern Highlands, Enga, Western Highlands, parts of Chimbu, Eastern Highlands, Morobe, Central and Milne Bay Provinces with small areas in Madang and Northern Provinces (Maps 4 and 5).

About 8% of PNG is moderately suited for low input arabica coffee production compared to about 1% under high input levels. Areas of moderate suitability for low input production are generally adjacent to the highly suitable areas listed above (Map 4). These are generally areas which are less suitable because of soil constraints (effective rooting depth, soil fertility), steeper slopes, suboptimal altitude/temperature range, or soil erodibility. Areas which are marginal for low input production occur on higher parts of New Britain, New Ireland and Bougainville with constraints largely caused by warmer temperatures and moist climate.

27

Results indicate a wider distribution of areas of high suitability based on RMU analysis compared to the values determined using the median rating for each environment subclass (Maps 2 and 3). In the environment subclass approach, values of land attributes are averaged across RMUs within each contiguous environment subclass. This may result in the relatively lower suitability ratings of many areas using the environment subclass median values compared to analysis based on each RMU.

Soil factors were not used as criteria for separating environment subclasses. They were not used because of the complexity of the soil pattern and difficulties of delineating homogeneous units at 1:500 000 scale in PNGRIS.

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Table 19: Areas of suitability classes for low input arabica coffee production based on PNGRISRMUs

P rov ince S u itab ility c lass and areaT otalarea

H igh M oderate M arg ina l U nsu itab le

(k m 2) (%) (k m 2) (% ) (k n r ) (% ) (k m 2) (% ) (k n r )

W este rn 16 <0.1 10 <0.1 0 0 96260 99.9 96286

G u lf 0 0 60 0.2 0 0 33787 99.8 33847

C en tra l 2233 7.5 4899 16.4 39 0.1 22747 76.0 29918

M iln e Bay 587 4.2 1513 10.7 4 <0.1 12021 85.1 14125

N o rth e rn 694 3.1 3071 13.6 0 0 18745 83.3 22510

S ou th ernH ig h lan d s 2444 9.5 3796 14.8 0 0 19400 75.7 25640

E n g a 1392 11.8 2119 17.9 81 0.7 82470 69.6 11839

W este rnH ig h lan d s 2080 23.4 3183 35.7 0 0 3634 40.9 8897

C h im bu 373 6.2 996 16.5 0 0 4653 77.3 6022

E as te rnH ig h lan d s 3607 32.8 3340 30.4 292 2.6 3767 34.2 11006

M orobe 3693 11.0 6413 19.1 331 1.0 23074 68.9 33511

M ad an g 371 1.3 851 3.0 84 0.3 27355 95.4 28661

E as t Sepik 19 <0.1 378 0.9 99 0.2 43020 98.9 43516

W est Sepik 1527 4.2 3419 9.5 11 <0.1 31053 86.2 36010

M an u s 0 0 0 0 0 0 2098 100 2098

N ew Ire lan d 0 0 458 4.8 1922 20.0 7235 75.2 9615

E as t N ew B rita in 0 0 427 2.8 2357 15.6 12325 81.6 15109

W est N ew B rita in 0 0 29 0.1 1800 8.7 18867 91.2 20696

N o rthS o lom ons 0 0 100 1.1 1045 11.2 8184 87.7 9329

T O T A L 19036 4.2 35062 7.6 8065 1.8 396472 86.4 458635

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Table 20: Areas of suitability classes for high input arabica coffee production based on PNGRISRMUs

Province Suitability class and areaTotalarea

High Moderate Marginal Unsuitable

(km2) (%) (km:) (%) (km2) (%) (km2) (%) (km2)

Western 16 <0.1 10 <0.1 0 0 96260 99.9 96286

Gulf 0 0 0 0 0 0 33847 100 33847

Central 2179 7.3 162 0.5 39 0.1 27538 92.1 29918

Milne Bay 587 4.2 25 0.2 4 <0.1 13509 96.8 14125

Northern 694 3.1 14 0.1 0 0 21802 96.8 22510

SouthernHighlands 2549 9.9 867 3.4 0 0 22224 86.7 25640

Enga 1392 11.8 337 2.8 0 0 10110 85.4 11839

WesternHighlands 2226 25.0 777 8.7 0 0 5894 66.3 8897

Chimbu 491 8.2 113 1.9 0 0 5418 89.9 6022

EasternHighlands 3607 32.8 650 5.9 93 0.8 6656 60.5 11006

Morobe 3719 11.1 135 0.4 0 0 29657 88.5 33511

Madang 251 0.9 203 0.7 0 0 28207 98.4 28661

East Sepik 19 <0.1 0 0 0 0 43497 99.9 43516

West Sepik 1544 4.3 220 0.6 11 <0.1 34235 95.1 36010

Manus 0 0 0 0 0 0 2098 100 2098

New Ireland 0 0 0 0 483 5.0 9132 95.0 9615

East New' Britain 0 0 433 2.9 42 0.3 14634 96.8 15109

West New7 Britain 0 0 39 0.2 0 0 20657 99 8 20696

NorthSolomons 0 0 100 1.1 0 0 9229 98.9 9329

TOTAL 19274 4.2 4085 0.9 672 0.2 434604 94.7 458635

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4.2.3 Comparison of PNGLES and Harding et al. (1986) arabica coffee models

Suitability ratings determined by the PNGLES (Venema and Daink 1992) and Harding et al. (1986) low input arabica coffee land evaluation models were compared at two different spatial scales:

• 1: 500 000 scale based on the PNGRIS RMUs, and• 1: 50 000 scale for 598 Land Resource Units (LRUs) mapped in the Upper Ramu catchment

(Trangmar et al. 1995a) in Eastern Highlands Province.

Results for the comparison between models (Table 21) show that the Harding et al. (1986) model rates about 11% of PNG as being of high or moderate suitability for low input arabica coffee production. In contrast, the PNGLES model rates only about 4% as being moderately suitable, with none being highly suited.

Table 21: Comparison of suitability ratings for PNGRIS RMUs using the PNGLES and Hardinget al. (1986) low input arabica coffee models

Rating class PNGLES Harding et al. (1986)

Area (km2) % Area (km2) %

1 0 0 19036 4.2

2 17325 3.8 35062 7.6

3 281339 61.3 8065 1.8

4 150642 32.8 396472 86.4

458635 100 458635 100

In the comparison made at 1:50 000 scale, the Harding et al. (1986) model for high input arabica coffee production produced a more severe rating than the PNGLES model for 31% of LRUs (Table 22). In 18% of LRUs the Harding et al. (1986) model produced a less severe rating than the PNGLES model. The remaining 50% of LRUs had the same rating in each system. For low input production, the Harding et al. (1986) model produced a more severe rating than the PNGLES model (Table 23) for 48% of the LRUs. In 21% of LRUs the Harding et al. (1986) model produced a more severe rating the PNGLES. The remaining 31% of LRUs had the same rating in each system.

Many of the discrepancies between the two systems are caused by differences in the decision trees and class boundaries for altitude (temperature) and rainfall land use requirements. The Harding et al. (1986) model treats vanation in altitude outside the optimal range of 1300-1699 m more severely than the PNGLES model. Similarly, the Harding et al. (1986) model downgrades variation in rainfall outside the 2000-2999 mm optimal range more severely than the PNGLES models.

For example, in the Harding et al. (1986) model, a rainfall of less than 1500 mm yields a severity level for the rainfall land use requirement of “4”. The suitability class is therefore Class 4, indicating that this LRU is unsuitable for both high and low input arabica coffee production. However h the PNGLES model, the land use requirement decision tree yields a severity level of “2”, because i: also considers the texture of the subsoil in this decision tree.

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Therefore rainfall is rated more severely in the Harding model. In the case of the Upper Ramu, this has a significant effect on the ratings produced with 276 LRUs in the low input model, and 186 LRUs in the high input model being affected by the more severe rainfall rating.

In cases where the PNGLES models produced more severe ratings, this was largely due to differences in assessment of soil aeration or wetness. In PNGLES aeration status is solely derived from soil drainage class. For poorly or very poorly drained soils, the land use requirement rating is c4’ (i.e. unsuitable for coffee production). However, for the corresponding land use requirement (drainage/effective soil depth) in Harding et al. (1986) such soils are rated as 3. Where drainage is mitigated by other land use requirements the final rating for such soils in Harding et al. (1986) may be 2 .

4.2.4 Interpretation of land evaluation results

The differences in evaluation results of the two models for both the PNGR1S RMUs and the Upper Ramu study indicate the need for users of the land evaluation systems to be fully aware of which models and criteria were used. They should also be aware of the limitations of the models used.

The results also indicate that the PNGLES and Harding et al. (1986) models require further validation before they are widely applied in PNG.

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Table 22: Comparison of suitability classes for high input arabica coffee production usingmodels of PNGLES and Harding et al. (1986)

Rating class, Harding model

Rating class, PNGLES model

No. of LRUs

Example Harding model subclass1

Example PNGLES model subclass2

Harding model more severe:

4 3 46 4r/e 3e

4 2 137 4r 2m/t

3 2 3 3a/r 2c/m/w

3 1 - - -

2 1 - - -

Total LRUs 186(31.1%)

Venema model more severe:

3 4 7 3d/a/r 4w

2 4 42 2d/r 4w

2 3 - - -

1 3 - - -

1 2 63 1 2m/t

Total LRUs 112(18 7%)

No difference:

4 4 260 4r/e 4e

3 3 - - -

2 2 40 2r 2mAv

1 1 - - -

Total LRUs 300(50.2%)

1 Subclass abbreviations for the Harding models as follows:a altitude d drainage/effective soil depthe erosion hazard r rainfall

Subclass abbreviations for the Venema models as follows: c temperature m moisture conditionst nutrient availability/retention capacity w oxygen availability (wetness)

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Table 23: Comparison of suitability classes for low input arabica coffee production using modelsof PNGLES and Harding et al. (1986)

Rating class, Harding model

Rating class, PNGLES model

No. of LRUs

Example Harding model subclass1

Example PNGLES model subclass2

Harding model more severe:

4 3 226 4r/e 3e

4 2 50 4r 2m/n

3 2 8 3a/r 2c/e/m/w

3 1 - - -

2 1 - - -

Total LRUs 284(47.5%)

Venema model more severe:

3 4 28 3d/r 4w

2 4 - - -

2 3 - - -

1 3 - - -

1 2 99 1 2e/m

Total LRUs 127(21.2%)

No difference:

4 4 101 4r/e 4e

3 3 - - -

2 2 86 2r 2e/m/r/w

1 1 - - -

Total LRUs 187(31.3%)

1 Subclass abbreviations for the Harding models as follows:a altitude d drainage/effective soil depthe erosion hazard r rainfall

2 Subclass abbreviations for the Venema models as follows:c temperature m moisture conditionst nutrient availability/retention capacity w oxygen availability (wetness)

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4.3 Land suitability for oil palm

The suitability of land for high input oil palm production based on application of the PNGLES model (Venema and Daink 1992) for each of the PNGRIS RMUs is shown in Map 6. The suitability ratings determined on analysis of each RMU are summarized in Table 27 for each province. Results indicate that about 4% of PNG has high physical suitability for oil palm. These areas occur mainly in Madang, Morobe, Northern, Central, Milne Bay, West New Britain, New Ireland and North Solomons Provinces. About 6% of PNG is moderately suited, 24% is marginal and 66% is unsuitable (Table 24, Map 6).

Areas of moderate suitability occur in East and West Sepik, the Gogol Valley in Madang, parts of Western Province, Gulf, Central, Northern, New Britain, and small areas in Morobe, Milne Bay, North Solomons, and New Ireland.

4.4 Land suitability for other crops

Land suitability assessments were conducted using the PNGLES models for low input, small-holder production of 14 other crops (cardamom, cashew, chillies, citrus, cocoa, coconut, coffee robusta, maize, mango, oil palm, pepper, pineapple, potato, pyrethrum, rubber, upland rice, and vanilla) under low input conditions and 6 other crops (cocoa, coffee robusta, maize, potato, upland rice, rubber) under medium to high input, smallholder production conditions.

For each of the above crops, land suitability ratings for each PNGRIS RMU were transferred from ALES and linked to the PNGRIS database for mapping using Maplnfo. These crop suitability map files have been installed on the computers in the DAL Land Use Section, Port Moresby from which hard copy maps can be readily produced. The maps are too numerous to be presented in this report. The example of oil palm is given m Map 6.

In addition, suitability ratings were computed for each median environment subclass for all 18 of the PNGLES crops (Venema and Daink 1992). Results for each province have been installed on the DAL Land Use Section computers. Area summaries (km“ and per cent of total province area) of suitability ratings by crop and province are given in Annex 3 for the PNGLES models calculated on an RMU basis and by median environment subclass.

35

Table 24: Areas of suitability classes for oil palm production based on PNGRIS RMUs

P rov ince S uitab ility class an d areaT ota larea

H igh M oderate M arg in a l U nsuitab le

(k m 2) (% ) (k m 2) (% ) (k m 2) (% ) (k m 2) (% ) (km 2)

W este rn 0 0 5384 5.6 51457 53.4 39445 41.0 96286

G u lf 698 2.1 3333 9.9 1115 3.3 28701 84.7 33847

C en tra l 1127 3.8 1816 6.1 5297 17.7 21678 72.4 29918

M iln e Bay 1975 14.0 1207 8.6 1727 12.2 9216 65.2 14125

N o rth e rn 2925 13.0 1927 8.6 3827 17.0 13831 61.4 22510

S ou thernH ig h lan d s 0 0 529 2.1 145 0.6 24966 97.3 25640

E n g a 68 0.6 54 0.5 0 0 11717 98.9 11839

W este rnH ig h lan d s 257 2.9 172 1.9 726 8.2 7742 87.0 8897

C h im bu 0 0 40 0.7 1081 18.0 4901 81.3 6022

E aste rnH ig h lan d s 0 0 116 1.0 98 0.9 10792 98.1 11006

M orobe 1107 3.3 1021 3.1 4541 13.6 26842 80.0 33511

M ad an g 4069 14.2 2132 7.4 3607 12.6 18853 65.8 28661

E a s t Sepik 0 0 981 2.2 10524 24.2 32011 73.6 43516

W est Sepik 0 0 2676 7.4 13254 36.8 20080 55.8 36010

M anus 193 9.2 662 31.6 260 12.4 983 46.8 2098

N ew Ire lan d 1448 15.1 1319 13.7 2384 24.8 4464 46.4 9615

E ast N ew B rita in 816 5.4 1737 11.5 0 0 12556 83.1 15109

W est N ew B rita in 2551 12.3 3782 18.3 3871 18.7 10492 50.7 20696

N o rthSolom ons 2185 23.4 685 7.3 4305 46.2 2154 23.1 9329

T O T A L 19419 4.2 29573 6.5 108219 23.6 301424 65.7 458635

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4.5 PLANTGRO ratings for Upper Ramu test area

Crop suitability ratings and dominant constraints for selected PNGR1S RMUs from the Upper Ramu test application of PLANTGRO are summarized in Table 25. PLANTGRO uses growth limitation ratings (GLRs) imposed by soil or climatic constraints to assess potential crop performance. GLRs are rated (Hackett 1991) as:

0-2 High potential crop performance (Minimal physical limitations to crop growth)3-4 Moderate potential crop performance (Moderate limitations to crop growth)5-6 Low potential crop performance (Significant limitations to crop growth)7-8 Nil potential (Severe limitations to crop growth)9 Rapid death (Very severe limitations to crop growth).

The PLANTGRO ratings correspond to the high, moderate, marginal and unsuitable classes used in this report for ratings derived from PNGLES and Harding et al. (1986)

Table 25: PLANTGRO ratings and mam limitations for selected crops and PNGRIS RMUs,Upper Ramu catchment, Eastern Highlands Province. Comparative ratings for Harding et al. (1986) for arabica coffee and PNGLES for other crops (except yam) in parentheses.

RMU No. PLANTGRO crop performance ratings and main limitations

Arabica coffee Sweet orange Pineapple Irish potato Yam

171 9(4) 6(3) 5(3) 5(3) 6Slope Soil texture Fertility Slope Temp.

441 3(2) 3(2) 4 (2) Solar 3(2) 6Fertility Temperature radiation Fertility Temp.

442 4(2) 6(3) 5(3) 5(3) 6(3)Aeration Aeration,

textureFertility Fertility Temp.

452 6(3) 7(4) 7(4) 7(4) 6(3)Aeration Aeration Aeration Aeration Temp.

60 4(2) 4(2) 5(3) 5(3) 6(3)Slope Temperature Fertility Fertility Temp.

64 2(1) 4(2) 4(2) 2(1) 6(3)Nil Temperature Solar

radiationNil Temp.

For the RMUs listed in Table 25 ratings based on PLANTGRO correlated closely with those for the other evaluation systems used. As with the ALES-based systems (PNGLES and Harding et al. (1986), the limitation ratmgs of each land characteristic are given in the PLANTGRO output. An additional advantage of the PLANTGRO output is that climatic limitations at various parts of the grow ing season

37

can be identified for each crop. This capability is not present in PNGLES or the Harding et al. (1986) coffee models.

The land use requirements in PLANTGRO are more physiologically-based than PNGLES or the Harding et al. (1986) coffee model. As a result, PLANTGRO is more sensitive to climatic parameters than the other two models. For example, Table 25 indicates that while there are basically no soil constraints to growth of pineapple in RMUs 441 and 64, solar radiation is the major limiting factor to achieving optimum growth. Factors such as solar radiation are not included in the other models.

The structure of PLANTGRO enables rapid comparison of site suitability for a range of crops, comparison of sites (different soils, and/or climates) for a specific crop(s), crop rotations, and assessment of the phasic development of some crops. It is also flexible in climate data entry which can include weekly or mean monthly records, or actual monthly records for a specific year. As a result, it complements the relatively inflexible decision tree structure of the ALES-based models. However, PLANTGROs current input/output formats are not easily linked with relational databases or computer mapping packages. This constraint limits its widespread application with large resource databases.

38

5. DISCUSSION

5.1 Model validation requirements

The analysis has indicated a need for wider validation of the PNGLES models (Venema and Daink 1992) and the Harding et al. (1986) arabica coffee models. The PNGLES models have been applied for several small areas by DAL Land Use Section staff (L. Dainkpers comm.). They were also used to assess land suitability for citrus and Irish potato at 1:50 000 scale in the Upper Ramu catchment (Trangmar et al. 1995a). In the latter study, the suitability ratings produced by the models for specific areas were compared with local experience of crop production as a reliability check. However, no systematic quantitative testing of models in relation to yield and specific management inputs in different locations has been conducted to date.

The arabica coffee models of Harding et al. (1986) have been applied and tested in the Western Highlands (Harding et al. 1987). They were also applied at 1:50 000 scale in the Upper Ramu catchment (Trangmar et al. 1995a). Results were generally consistent with local experience.

Both the PNGLES and Harding et al. (1986) arabica coffee models require further field testing in areas with different land characteristics and management regimes. Validation should include comparison of model ratings with crop yield results under known management inputs. This should be done in a range of environments that cover a range of rating classes so that the boundaries between rating classes can be more reliably defined.

As the systems become used and tested in different areas, they should be refined and updated by DAL Land Use Section staff. This should be an ongoing activity as DAL experience in application of the various systems increases.

In addition, comparisons of different land evaluation systems which exist in PNG for some crops (e g. cocoa) should be systematically compared so DAL staff can select the most appropriate model for each crop. For example, the results produced by the cocoa model of Bleeker and Freyne (1981) should be compared with those of PNGLES (Venema and Daink 1992) and PLANTGRO (Hackett 1991) to assess which best match local experience.

5.2 Scale factors in land use suitability assessment

The land evaluation systems discussed in this report can be readily applied at different map scales. However, it is essential that interpretation of the results is done in context of the map scale at which the land characteristics used in the land evaluation systems are collated and delineated.

Land suitability assessments made using data from PNGRIS are only relevant for planning at national or provincial level. The land resource data in PNGRIS was compiled at 1:500 000 scale and is of insufficient detail for planning agricultural development at district, subdistrict or catchment levels. Land evaluation for planning at these levels should be based on resource data mapped at scales of 1:50 000 or 1:25 000, respectively (Basher et al. 1995). Land suitability assessment for village development or site-specific agricultural projects requires land resource data mapped at scales of 1:10 000 or larger.

39

5.3 Adjustment of rating criteria in relation to management conditions

Adaptations to the models may be required at different spatial scales and under different input management systems. At increasing levels of spatial detail, the assumed management inputs levels (or Land Utilization Type in the PNGLES models) will require increasing refinement for local crop production systems and management inputs. In applying the land evaluation systems, care should be taken in defining the management systems to which the systems are applied to ensure relevance of the suitability ratings generated.

5.4 Application of land evaluation models

The ALES-based models (PNGLES, Harding et al. (1986)) should be used in conjunction with appropriate land resource data to initially screen land suitability at national and provincial level using PNGRIS. They should then be used in conjunction with more detailed land resource data (e.g. 1:50 000) collected in areas of high potential to identify specific areas of development potential. Once such areas have been identified detailed site investigations of land suitability should be done in conjunction with other relevant factors such as availability of markets, investment, infrastructure, and transport. PLANTGRO is perhaps best used to complement the ALES-based models at this detailed, site-specific stage of the agricultural development process to assess specific soils, crop species and combinations under a particular set of well documented climatic conditions.

5.5 Future model development

Once the existing models have been more widely validated and applied, additional parameters of importance for production of specific crops could be added to improve their predictive ability. For example, climatic criteria (e.g. rainfall, rainfall seasonality, humidity, temperature) could be used to predict general likelihood of pest and disease occurrence for specific crops.

Addition of new parameters and their testing should be done in conjunction with agronomists and crop specialists with experience of specific crops in different parts of PNG.

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6. CONCLUSIONS

The framework for computerized land evaluation systems in PNG have been established by the development of PNGLES (Venema and Daink 1986), the arabica coffee model of Harding et al. (1986), and the test application of PLANTGRO (Hackett 1991) in conjunction with PNGRIS. However, the systems applied during this study require ongoing validation in different parts of PNG. This should be done by comparison of crop yield data with suitability ratings generated by the land evaluation systems. This should be done in collaboration with local agronomists and crop production specialists.

Results of the analyses for arabica coffee production indicate that use of median ratings for environment subclasses downgrade suitability ratings compared to analysis on an RMU-by-RMU basis. This is caused by use of the median rating for each environment subclass whose value reflects the generally more common lower suitability classes in an environment unit and downgrades the RMUs which (on an individual basis) have higher suitability ratings.

The aggregation of ratings using the environment class approach are useful for very broad scale analysis of crop suitability. However, calculation of ratings on an RMU basis provide a more reliable estimate of crop suitability because the ratings are directly related to the land resource data in each RMU rather than averaged for all RMUs in a particular environment subclass.

Wider correlation of the PNGLES and Harding et al. (1986) arabica coffee models should be conducted. Appropriate adjustments should be made to each model to ensure improved consistency between the ratings produced by each model.

The development of PNGLES and the Harding et al. (1986) coffee model in the ALES framework makes them well suited to land evaluation in association with computerized land resource databases such as PNGRIS and the Upper Ramu database (Trangmar et al. 1995b). The work done in this study of developing procedures to extract soil data from PNGRIS for use in PLANTGRO should be expanded on to make PLANTGRO more widely applicable with PNGRIS. This will require revision of data input/output formats in PLANTGRO to make it more compatible with relational databases and applicable with land resource survey data.

The crop suitability ratings based on PNGRIS in this study should only be used for agricultural development planning at national scale. Investigations for agricultural development at provincial, district, subdistrict and site-specific levels should be conducted at more detailed scales.

41

REFERENCES

Bellamy, J.A. (compiler). 1986. Papua New Guinea inventory o f natural resources, population distribution and land use handbook. CSIRO Division of Water and Land Resources, Natural Resources Series No. 6 (CSIRO: Melbourne).

Bleeker, P. 1975. Explanatory notes to the land limitation and agricultural land use potential map o f Papua New Guinea. CSIRO Australian Land Research Series 36.

Bleeker, P. 1983. Soils o f Papua New Guinea. CSIRO and ANU Press, Canberra.

Bleeker, P., and Freyne, D.F. 1981. Areas suitable for cocoa production in Papua New Guinea. CSIRO, Australia and Dept. Primary Industry, Papua New Guinea.

FAO (Food and Agriculture Organization). 1976. A framework for land evaluation. Soils Bulletin 32, Rome.

Haantjens, H.A. 1963. Land capability classification in reconnaissance surveys in Papua New Guinea. J. Aust. Inst. ofAgric. Sei. 29:104-107.

Haantjens, H.A. 1969. Agricultural land classification for New Guinea land resources surveys. Second revised edition. CSIRO Division of Land Research Tech. Mem. 86/4. Canberra.

Haantjens, H.A., Reynders, J.J., Mouthaan, W.L.P.J., and van Baren, F.A. 1967. Major soil groups o f New Guinea and their distribution. Communication No. 55, Department of Agriculture Research, Royal Tropical Institute, Amsterdam.

Hackett, C. 1988. Matching plants and land: Development o f a general broadscale system from a crop project for Papua New Guinea. CSIRO Division of Water and Land Resources, Natural Resources Series No. 11, Melbourne.

Hackett, C. 1991. PLANTGRO - A software package for the coarse prediction o f plant growth. CSIRO Publications, Melbourne. 242 p.

Harding, P.E., Bleeker, P. and Freyne, D.F. 1986. A handbook for land suitability evaluation for rainfed arabica coffee production in Papua New Guinea. Coffee Research Report 3. PNG Coffee Research Institute. 134 p.

Harding, P.E., Bleeker, P. and Freyne, D.F. 1987. Land suitability evaluation for rainfed arabicacoffee production: Western Highlands Province, Papua New Guinea. Coffee Research Report 4. PNG Coffee Research Institute.

Hartley, A.C., Aland, F.P., and Searle, P.G.E. 1967. Soil survey o f West New Guinea, the Balima- Tiauru area. Soil Survey Report No 1, DASF, Port Moresby.

McAlpine, J.R., Keig, G., and Short, K. 1975. Climatic tables for Papua New Guinea. CSIRO Division of Land Use Research Technical Paper 37. Canberra, Australia.

42

McAlpine, J.R. Unpub. Environments o f Papua New Guinea and their current use. Unpublished report.

Marques, M.R. 1994. Land suitability classification for small-holder coconut production in Papua New Guinea. M.Sc Thesis, University of Queensland. 191 p.

Radcliffe, D.J. 1983. Land evaluation for food crop production in Papua New Guinea. Paper presented at the 1st PNG Food and Nutrition Conference, Goroka, AFTSEMU Technical Report No. 2. Mendi.

Rossiter, D.G., and Van Wambeke, A.R. 1991. ALES Version 3 User’s Manual. Cornell University, Ithaca, New York.

Trangmar, B.B., Basher, L.R., Rijkse, W.C., and Jackson, R.J. 1995a. Land resource survey, Upper Ramu Catchment, Papua New Guinea. (AIDAB:CSIRO), PNGRIS Publication 3.

Trangmar, B.B., Basher, L.R., and Rijkse, W.C. 1995b. Land resource survey, Upper Ramu Catchment, Papua New Guinea: Database attributes. (AIDAB:CSIRO), PNGRIS Report 3.

Venema, J.H., and Daink, F. 1992. Papua New Guinea Land Evaluation System (PNGLES). AG:TCP/PNG/0152 Field Document 1. PNG Dept. Agriculture and Livestock, and Food and Agriculture Organization. 152 p.

Wayi, B.M. 1987. Development o f a land suitability classification system for cocoa in Papua New Guinea. M.S. Thesis. State University of Ghent, Belgium.

43

ANNEX 1: Procedures for extracting soils data from PNGRIS for entry to PLANTGRO

1. Introduction

PLANTGRO (Hackett 1991) is a computer program which generates semi-quantitative predictions of plant performance based on soil, climate and plant characteristic files. Linking PLANTGRO to PNGRIS data should allow prediction of physical crop suitabilities for many crops across many RMUs. However, constraints to doing this are:

• limitations in PLANTGRO input and output formats• PNGRIS does not contain all the data required to run PLANTGRO• inconsistencies in data definitions between PNGRIS and PLANTGRO

Any omissions from the PNGRIS database or the accuracy of the PLANTGRO models are not considered here. Such factors impact on the reliability of predictions obtained by linking PLANTGRO and PNGRIS, but not on the procedures of generating such predictions.

2. PLANTGRO input and output

PLANTGRO reads input data from ASCII text files in a strict format and writes data as text reports to a printer or text file. There is no facility for reading data from or writing predictions to databases in standard formats. A computer program (SOILOUT.EXE) was written to take data from a standard database (Paradox) table and write this out to a series of PLANTGRO soil files (one soil per record). The following fields are read from the Paradox table:

Name Name of PLANTGRO soil fileFullname Soil (site) name stored in PLANTGRO fileAeration PLANTGRO aeration codeBS Base saturation (%)CEC CEC (meq/100g)PRD Potential rooting depth (cm)P Olsen Phosphate (ppm)K Exchangeable K (meq/100g)Sal Salinity (dS/m)Slope Slope (degrees)DEPTHA Thickness of “A” horizon (cm)DEPTHB Thickness of “B” horizon (DEPTHA+DEPTHB=Potential rooting depth)DEPTHC Thickness of “infiltration zone"' (cm)AWCA Available water in A horizon (%)AWCB Available water in B horizon (%)DRWA Drainable water capacity in A horizon (%)DRWB Drainable water capacity in B horizon (%)DRWC Drainable water capacity in C horizon (%)TEXTA Texture of A horizon (PLANTGRO code)TEXTB Texture of B horizon (PLANTGRO code)TEXTC Texture of infiltration zone (PLANTGRO code)

Writing a computer program to convert PLANTGRO output to a standard database table for use with PNGRIS requires the computer program to interpret the PLANTGRO text output m a format suitable for input to a relational database and use with computer mapping software.

44

3. PNGRIS data

PNGRIS stores soil data for the three main soils within each RMU. Most of the soil data required byPLANTGRO is included in PNGRIS (subject to translation) or could be reasonably inferred fromPNGRIS data. The main exceptions are:

Slope PNGRIS records two slopes and three soils per RMU but does not indicate whichslopes apply to which soils. This must be inferred from user knowledge of soils and their likely slope class.

Layers PNGRIS stores an overall depth rating rather than the thicknesses of individual layersas required by PLANTGRO. This overall thickness must be arbitrarily split between the soil A and B horizons for input to PLANTGRO.

AWC Available water capacity (AWC) is required for the A and B layers in PLANTGRO.PNGRIS stores a rating for the cumulative AWC for three different rooting depths. The ratings are too coarsely stepped to allow estimation of the individual layer AWCs by difference. As a result, AWC must be estimated from texture.

DRWC Drainable water capacity was not included in the PNGRIS data. This was estimatedfrom the texture.

Infiltration ZonePNGRIS does not include any data about the infiltration zone' parameter used in PLANTGRO. The infiltration zone is an area below the rooting zone which may impede soil drainage and lead to seasonal water-logging if its drainable water capacity or total thickness is sufficiently low. A constant infiltration zone of 1 m can generally be assumed with texture, AWC and DRWC the same as the B horizon.

4. Data inconsistencies between systems

The PNGRIS and PLANTGRO data formats differ in several ways. PLANTGRO uses numeric estimates for most properties while PNGRIS stores ratings. The PLANTGRO models are not particularly sensitive to the precise value of each input parameter. The level of precision in the PNGRIS data is probably adequate for most PLANTGRO applications.

A series of lookup tables were created converting the PNGRIS data code to one or more PLANTGRO soil parameters. Each lookup table was given fields named CODE giving the PNGRIS code, DESC giving the corresponding description, and one or more other fields giving the corresponding PLANTGRO soil characteristic(s)

45

The lookup tables are:

Table 1-1: Summary of lookup tables for converting PNGRIS soil data to PLANTGRO soil inputparameters

Lookup table PNGRIS field (CODE) Corresponding PLANTGRO field(s)

AVAILP AVAILP PGOLSEN Olsen Phosphate

BS BS PGBS Base Saturation

CEC CEC PGCEC Cation Exchange Capacity

DEPTH DEPTH PGDEPTH Potential rooting depth PGDEPTHA A horizon thickness PGDEPTHB B horizon thickness

DRAINAGE DRAINAGE PGAERAT PLANTGRO Aeration code

EXK EXCHK PGKX Exchangeable K

NITROGEN NITROGEN PGNIT Available Nitrogen

PH PH PGPH Soil pH

SALINITY SALINITY PGSAL Soil salinity

SLOPE SLOPE 1 or SLOPE2

PGSLOPE Slope

TEXTURE TEXTURE 1 or TEXTURE2

PGTEXT Texture (PLANTGRO code) PGAWC Available water capacity PGDRWC Drainable water capacity

5. Procedure for generating PLANTGRO Predictions

The following procedure was used to generate PLANTGRO predictions for arabica coffee, sweetorange, pineapple, Irish potato and lesser yam for each of the 57 soils within the 19 RMUs in the UpperRamu catchment. Climate data was taken from the Aiyura climate station in all cases.

The main steps were:

1. Data for the 57 soils in the 19 RMUs were extracted from the PNGRIS data file

2. The SLOPE 1 and SLOPE2 values from the PNGRIS data file were inserted into the extracted soil file and one of these was manually selected for each soil This selection was a “best guess'’ taking account of the erodibihty ratings of the soils and the presumed relative frequencies (soil 1 > soil2 > soil3). Only one RMU had SLOPE 1 and SLOPE2 values differing by more than one class so errors in this allocation were probably not critical.

3. The lookup tables were used to generate a new (Paradox) table with the PLANTGRO codes in the format required by the SOILOUT program. The name fields were generated from the RMU and soil numbers.

46

4. The SOILOUT.EXE program was run to produce PLANTGRO soil files for each of the 57 soils.

5. PLANTGRO program was run (using the multiple predictions option) to generate the 285 predictions (57 soils, 5 plants).

Results of the test application of PLANTGRO on PNGRIS RMUs is summarized in Section 4.4 of the main report.

6. Limitations to using PLANTGRO with a large dataset

Steps 1, 3 and 4 in the above procedure can be readily performed on a much larger number of soils. However, step 2 requires manual inspection of each soil and therefore requires more effort if a large number of soils were involved. The effort was relatively minor (at least for the 57 soils tested). Many plant files may not be markedly dependent on slope (erosion hazard should be considered separately) and it may, in fact, be acceptable to simply use the dominant (or mean) slope for all soils in an RMU.

The main limitation to running a much larger (e.g. 1000 soils) data set would be running PLANTGRO and interpreting the results. Even in the multiple prediction model PLANTGRO requires each soil, crop and climate file to be specified and has a limit of 7 soil files per run. Future developments of PLANTGRO should provide output formats which are compatible with data input and output formats which are readily compatible with relational databases for using with computer mapping packages.

7. Computer programmes

The computer programmes developed to extract PLANTGRO data from PNGRIS (SOILOUT.EXE, lookup tables) have been installed on computers in DAL Land Use Section.

ANNEX 2: Definitions of PNGRIS attributes used to define environment subclasses

Defimtions of the 'Inundation type’ environment subclasses

In u n d a t io n ty p e P N G R IS c o d e

L it t le o r n o in u n d a t io n 0 ,1 ,2

S e a s o n a l 3 ,4

P e r m a n e n t 5 ,6

T id a l 7 ,8

Definitions of the 'Slope' environment subclasses

S lo p e c la s s P N G R IS c o d e

L o w (< 1 0 ° ) 1 ,2 ,3

M o d e r a te (1 0 -2 0 ° ) 4

H ig h (< 2 0 ° ) 5 ,6

Definitions of the 'Rock type' environment subclasses

R o c k ty p e P N G R IS c o d e

A l lu v ia l d e p o s i ts 4 1 ,4 5

O ld e r s e d m ie n ts 4 0 ,1 2

P y ro c la s tic s 4 6

Definitions of the 'Dominant rock type' environment subclasses

D o m in a n t ro c k ty p e P N G R IS c o d e

N o i l - c a lc a re o u s s e d im e n ta ry 1 1 ,1 2 ,1 3

C a lc a r e o u s s e d im e n ta r y 14 ,15

M e ta m o r p h ic 2 1 ,2 2 ,2 3

A c id to i n te r m e d ia te ig n e o u s 3 0 ,3 2 -3 5 ,3 8 ,4 6

B a s ic ig n e o u s 3 1 ,3 6 ,3 7

48

ANNEX 3: Summary of crop suitability ratings for PNGRIS RMUs and median environmentsubclasses according to PNGLES

Key to Abbreviations:

Province:

WES Western MOR MorobeGUL Gulf MAD MadangCEN Central ESK East SepikMIL Milne Bay WSK West SepikNTH Northern MAN ManusSHY Southern Highlands NIR New IrelandENG Enga ENB East New BritainWHY Western Highlands WNB West New BritainSIM Simbu NSL North SolomonsEHY Eastern Highlands ALLPNG Totals for the whole of PNG

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. r -

X v --------------V

y% \Environment type

■ mangrove swamps estuaries and coastal plains

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Arabica Coffee productionHarding model

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Arabica Coffee productionHarding model

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%»V

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Arabica Coffee productionHarding model

£

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MAP 6: Suitability of PNGRIS RMU's for Oilpalm production

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-------provincial boundaries

PNGRIS PUBLICATIONS (ISSN 1320-5765)

Agricultural Laud Use o f Papua New Guinea: [map with explanatory notes]. Scale 1:1 000 000. PNGRIS Publication No. 1. ISBN 0 642 19604 4

Forest Resources o f Papua New Guinea: [map with explanatory notes]. Scale 1:1 000 000. PNGRIS Publication No. 2. ISBN 0 642 19603 6

Land Resource Survey, Upper Ramu Catchment, Papua New Guinea. PNGRIS Publication No. 3. ISBN 0 642 19605 2

Land Suitability for Selected Crops in Papua New Guinea. PNGRIS Publication No. 8. ISBN 0 642 19609 5

Rainfall Erosivity in Papua New Guinea. PNGRIS Publication No. 9. ISBN 0 642 19607 9

RELATED PUBLICATIONS

Geomorphology o f Papua New Guinea: [map with explanatory notes]. Scale 1:1 000 000. Land Research Series No. 33. (CSIRO Division of Land Use Research). ISBN 0 643 00092 5

Vegetation o f Papua New Guinea: [map with explanatory> notes]. Scale 1:1 000 000. Land Research Series No. 35. (CSIRO Division of Land Use Research). ISBN 0 643 00138 7

Papua New Guinea Inventory o f Natural Resources, Population Distribution and Land Use Handbook. Natural Resource Series No. 6 (CSIRO Division of Water and Land Resources).

ISSN 1320-5765 ISBN 0 642 19609 5