The property was purchased by Geoff Nutt from his family 9 years ago (after originally being subdivided . . . → Read More: “WEDGEWOOD”, PARNDANA, KANGAROO ISLAND – USING THEIR TOPOCLIMATE FARM PLAN TO IMPROVE SUSTAINABLE PRODUCTIVITY.]]> “Wedgewood”, the property of Geoff Nutt covers 522 ha, of which 405 ha has been cleared for farming. Clearing is restricted on the remaining 117 ha of bush and regrowth scrub, under the South Australia Native Vegetation Act 1991.

The property was purchased by Geoff Nutt from his family 9 years ago (after originally being subdivided as a Soldier Resettlement Block after the Second World War and settled by Geoff’s family in 1956).

Geoff joined the Kangaroo Island Topoclimate project because, in his words” While I knew that I needed a lot more subdivision on the property, I was uncertain where to site the fences or how to improve the pastures, water supply and my grazing systems to improve production”.

In his Topoclimate Farm Plan, Geoff has now amassed an enormous resource of quality information at a farm scale on his soils, microclimates, water resources and environmental issues and has used this information to develop a detailed Action Plan for the property (as shown below) to subdivide his thirteen existing paddocks into over thirty new paddocks, develop areas of specialised perennial grasses including Kikuyu and chicory to provide feed at critical times of the year such as at mating and for lamb finishing. He is also proposing to fence off all significant areas of bush and remnant scrub on the property to encourage regeneration of native species and improved wildlife habitat for the resident koala population.

“The Project has given me an extraordinary information resource for the farm and also the confidence to move ahead with an ambitious development programme for the property which will significantly improve my bottom line for my farm while reducing my costs” Geoff commented.

It’s remoteness and isolation has created a special environment for plants and wildlife and over 50% of the Island is protected in National Parks and Reserves.

The . . . → Read More: SUCCESSFUL OUTCOMES FROM PILOT TOPOCLIMATE PROJECT ON KANGAROO ISLAND]]> Kangaroo Island is the third largest Island in Australia, measuring 150 km from West to east and 100 km across and is located about 100km south of Adelaide in South Australia.

It’s remoteness and isolation has created a special environment for plants and wildlife and over 50% of the Island is protected in National Parks and Reserves.

The Island is home to about 4000 residents including over 360 farmers who run sheep and cattle as well as significant areas of cropping in canola, wheat, oats and barley. About 25 wineries are found on the Island along with a unique strain of Ligurian bees which produce organic honey from the native vegetation.

Tourism is also a major source of Island income with over 100,000 tourists per year visiting the Island by Ferry or Airplane and enjoying the rural landscapes and wilderness experiences.

The Kangaroo Island Topoclimate Pilot Project was developed and promoted by the Kangaroo Island Development Board in conjunction with the KI Natural Resources Management Board and Primary Industries & Research, South Australia (PIRSA),  in late 2008. Topoclimate Australia (TAPL) was selected and contracted to undertake the project in mid-March 2009 with nine local farmers over twelve properties (7600 ha) spread across Kangaroo Island.

TAPL developed Farm plans for each of the 12 properties in the pilot project, mapping infrastructure, establishing aims and objectives for each farmer, surveying the soils, microclimates, water resources, salinity and environmental issues on each property at farm scale and then developing an Action Plan for each farmer to maximise the value from the intensive level of data collected on each property in line with their stated aims and objectives.

The soil mapping utilised new radiometric and geophysics technologies to accurately map soil boundaries along with an intensive soil testing programme to delineate soil types. The company also used the unique Topoclimate Microclimate mapping technologies and over 100 temperature loggers to map microclimates across all of the properties.

Over 12 different maps and eight detailed reports were produced for each property as part of the project. The Action Plans, produced at the conclusion of the programme, covered the whole gamut of Farmer directions for improved productivity, diversification and sustainability including:

• Significant refencing of properties to mapped soil types and microclimates ,

• Improved protection of remnant vegetation, bush and wildlife habitat on each property.

• Increased subdivision to suit changing grazing systems and new pastures.

• More effective use of fertiliser

• Identification of land suited for regular cropping

• Provision of new perennial pasture swards of grasses such as kikuyu to provide better summer grazing.

• Improvements to property water supply systems to cope with refencing and increased stocking rates.

• Diversification into new crops including seed potatoes, pasture seeds, cherries, persimmons, truffles in the right places.

• Collation of an information resource in the Farm Plan that will add value to each property in the event of sale of the property.

All of the properties are planning on-farm works as a result of the programme and if these are carried out to fruition, it is expected that most farms will experience at least 20% improvement in their returns from the property, with some significantly exceeding this.

The project concluded with a interactive open Field day for all Kangaroo Island farmers on 23rd June following final delivery of farm Plans to each farmer. The whole project was delivered on time and on budget for a successful outcome for all stakeholders.

Kangaroo Island farmers Christine and Lloyd Berry have been very happy with the outcomes of their Topoclimate Farm plan . “Although we weren’t entirely sure what the outcomes would be we are very happy now to have been part of the pilot project.” Christine commented.

They were part of a pilot group of . . . → Read More: Topoclimate Farm Plan changes farm practices for Kangaroo Island Farmer]]>

This is the story of a New Zealand farming couple who demonstrate many of the Topoclimate process principles and making better use of land resource information on their farm. 

Merle and Bill Johnston intensively farm about 160 ha on the Waikaia Plains in . . . → Read More: How the TOPOCLIMATE Mapping process helped a Southland farming couple.]]>

This is the story of a New Zealand farming couple who demonstrate many of the Topoclimate process principles and making better use of land resource information on their farm. 

Merle and Bill Johnston intensively farm about 160 ha on the Waikaia Plains in northern Southland in the south of the South Island of New Zealand.

Their small family farm had ran Romney-cross Sheep producing lambs for the export fat-lamb trade for several generations and was very typical of farms in the area. 

Although they had made many improvements to the farm over the years with fencing and fertiliser and improved the quality of stock, changing farming economics and the limited size of the property meant that they weren’t making sufficient income to keep the farm profitable.

 Merle was facing the prospect of having to take up off-farm employment in one of the small towns in the District to contribute additional income to help the family budget. Merle didn’t relish the prospect of leaving the farm and wondered if there were any other land uses that she and her husband could diversify into to generate additional income for the family. 

She knew from experience that the soils on the property which comprised free-draining silt loam topsoil over outwash gravel subsoils were not what would be normally considered classic horticultural soils. However, as a keen gardener, Merle had established an extensive garden around her house and noticed the peony roses in her garden were as good as any in the district.

 Clearly, the farm also had the right microclimates to produce the very large flower blooms that florists were looking for to use for floral decorations over the Christmas period. She wondered whether it would be possible to grow these flowers commercially.

She discussed the idea with her husband, who reluctantly agreed to giving her a plot of land “out the back of the house” to try the new crop although he cautioned her that they could not afford to buy a lot of expensive equipment for her venture.

Merle enthusiastically planting peony tubers.

 With enthusiasm, Merle started developing the peony plot by hand (with support from her friends) using shovel and wheelbarrow. Bill also offered to fence the plot off once she had proven the commercial value of the enterprise but five years later, he was still reluctant to build a permanent fence around the block because every year she doubled the size of the enterprise.

The only significant equipment that they have purchased for the venture beyond what is normally found on a farm was a small chiller to condition the flowers after harvest.

Merle has subsequently established international markets for her peony roses and specialises in the white and Coca-Cola red varieties popular with the American market for use in Christmas table decorations.

She has been receiving up to $NZ3.50* PER BLOOM for her export grade flowers and is generating over $NZ100,000 per hectare from her crop. 

Both Merle and her husband now work full time in their Peony business, and employ a part-time farm manager to look after the rest of the farm.  

They have completely transformed their farming system (and their lives) by their understanding of their land and climate and selection of a suitable crop for which they had market advantage through their soils and microclimates.

Merle Johnson in her peony plot

Now over 300 different farmers grow peony roses in Southland and they work closely together to process and market their crop co-operatively to the world markets.

So what were the Factors for Success for Merle and Bill and in fact for the whole Southland Community?

 Firstly there is now widespread use of the Topoclimate mapping programme throughout the whole region at a farm scale. Secondly the whole Southland community has learnt from situations like the Johnsons and there is recognition of the importance of good information on soils and climates in making decisions about farming diversification.

The mapping information is being used to create and develop additional crops by application of the principles of the Topoclimate process.

Murray provided a very good example of the economic value of microclimate information in relation . . . → Read More: Beekeeper benefits from microclimate information]]> This is Murray Ballantyne, a beekeeper apiarist with over 35 years experience in beekeeping. Murray was also Chairman of the Topoclimate South Trust that ran the Topoclimate Project in Southland because he appreciated the value of microclimate information to farming.

Murray provided a very good example of the economic value of microclimate information in relation to his bee keeping business involving several thousand hives. Murray gave the details of his example as follows:

“I currently run two sites of hives in the Winton area at the points marked A & B on this map. I have noticed for a number of years that the bees at site A have always produced about 20% less honey that the same hives at site B.”

“Looking at the landscape at each site, I could not really see why this occurred as both sites were at similar altitude and had similar local sources of nectar so I was at a loss to understand why this difference was happening.”

Murray Ballantyne continued, “When I looked at the Growing Degree Day (GDD) maps for the two sites, I could start to understand why – Site A received significantly less annual heat than site B.”

“Because Site A was in a cooler gully that cold air drained into, my bees were expending much more energy in keeping warm and less in producing honey. Also, they would be slower to leave the hive to collect nectar and pollen for honey production”

“Suddenly, with good microclimate information, the answers become blindly obvious. My bees know where the good microclimates are but I had to use the Topoclimate maps to find these optimal spots.”

Murray Ballantyne commented further “Just by moving my hives two paddocks up the hill, I believe I can earn enough extra in honey production to pay for my varroa control costs in the event that the disease eventually gets down here”.

I believe that Southland farmers will be able to use the Topoclimate information in ways just like this to improve their farm production, to diversify into new things and to do things more sustainably.”

Callistemon

Three recent projects have developed and enhanced generic methods to evaluate the suitability of particular plants for general regions and specific sites. All three projects have concentrated on forestry in developing countries, but the methods are suitable for other regions and for plants other than trees. . . . → Read More: Predicting Plant Growth: Where will it grow? How well will it grow?]]>

 Article by Trevor H. Booth

Callistemon

Three recent projects have developed and enhanced generic methods to evaluate the suitability of particular plants for general regions and specific sites. All three projects have concentrated on forestry in developing countries, but the methods are suitable for other regions and for plants other than trees. Two of the projects have emphasized the development and application of cheap easy-to-use PC-based programs, whilst the third has shown how a simple PC-based model can also be applied as part of a multi-million dollar land evaluation study using the ARC/INFO GIS on Sun workstations.

Four methods used in the projects are outlined here, including the development of climatic interpolation relationships, the development of climatic mapping programs, the application of the Plantgro simulation model and the development of simulation mapping programs.

Though more work is needed to validate and calibrate these programs for the hundreds of tree species important in tropical and sub-tropical areas it is concluded that they already provide a useful basis for selecting species and provenances for planting in different regions.

Introduction

The world’s rapidly rising population requires most countries to make the best possible use of their land resources for agriculture, horticulture, forestry and conservation. Being able to predict where and how well particular plants are likely to grow in different regions is vital for land use planning. Linking GIS and models can help to answer these questions, but decision makers and researchers in developing countries have limited access to these technologies.

Kangaroo Paw

This paper outlines on-going efforts to provide improved access to databases, mapping programs and simple simulation models to assist land evaluation. The work described relates mainly to forestry projects in developing countries, including work for the Australian Centre for International Agricultural Research (ACIAR) project 9127 “Predicting Tree Growth for General Regions and Specific Sites in China, Thailand and Australia” and the Australian Agency for International Development (AusAID) project on “Improving Tree Productivity in Southeast Asia”. However, the methods could also be applied in other areas and with plants other than trees.

Four methods are outlined. First, the development of climatic interpolation relationships which allow mean climatic conditions to be estimated reliably for any location within a particular country. Second, the development of climatic mapping programs which use interpolated data to indicate where particular trees can be grown. Third, the use of the Plantgro model, which uses detailed soil and climate information to estimate how well a particular tree is likely to grow on a specific site. Fourth, the use of simulation mapping programs, which use a simplified version of the Plantgro model, to predict how well a particular tree might grow at thousands of locations across a country or continent. The purpose of this paper is to provide a brief introduction to the methods. More details are available in the proceedings of an international workshop entitled “Matching Trees and Sites” (Booth 1996a).

 Climatic interpolation

Climate has an important influence on plant growth. It is particularly useful as a means to predict where particular plants will grow, as mean climatic conditions can now be reliably estimated for most locations around the world.

For example, Dr Michael Hutchinson (Centre for Resource and Environmental Studies, Australian National University) has developed a package known as ANUSPLIN, which uses Laplacian smoothing splines to interpolate spatially between data recorded at meteorological stations Hutchinson 1989, 1992).

As part of ACIAR project 9127 mean monthly data were collated from meteorological stations in a single area including China, Thailand, Vietnam, Laos, Cambodia and Peninsula Malaysia (Zuo et al. 1996). Monthly mean data for 1222, 1117, 3832, 898 and 903 stations were collated for maximum temperature, minimum temperature, precipitation, solar radiation and evaporation respectively. Interpolation relationships were developed relating these monthly mean values to latitude, longitude and elevation.

Latitude and longitude are easily estimated for any location, but elevation must also be provided to interrogate the interpolated climatic relationships. A digital elevation model was prepared by Zuo et al. (1996) and monthly mean values for all five climatic factors were estimated for a 1/20th of a degree grid (5km approx) of approximately 400 000 points across China and mainland Southeast Asia.

Example colour maps showing mean annual values of these five factors across China and mainland South East Asia are included in the ACIAR proceedings (Booth 1996a). Climatic interpolation analyses for Indonesia (Jovanovic and Booth 1996a) and the Philippines (Jovanovic and Booth 1996b), which were developed as part of work for the AusAID, are also described in the ACIAR proceedings. Details of the interpolation methods used, sample outputs and references to studies in other areas are provided by Hutchinson et al. as well as Kesteven and Hutchinson .

 Climatic mapping programs

Very large climatic databases are impressive, but they are of little help for decision-making in developing countries if potential users cannot access them. In countries where average incomes may be no more than a couple of hundred dollars a year, few individuals or institutions have access to sophisticated GIS programs or powerful computers.

Even if low-cost PC-based GIS programs, such as IDRISI (Eastman 1993), are available they may seem complex to many first time users. Climatic mapping programs were developed to provide very easy access to interpolated climatic information (Booth 1996b).

Simple descriptions of ranges of climatic conditions are entered and the climatic mapping programs show which areas, if any, satisfy those sets of conditions. As climatic mapping programs were mainly developed for forestry studies the following set of six climatic factors were used initially:

a. mean annual precipitation (mm)

b. rainfall seasonality (uniform/bimodal, summer, winter)

c. dry season length (months)

d. mean maximum temperature of the hottest month (oC)

e. mean minimum temperature of the coldest month (oC)

f. mean annual temperature (oC)

These factors had proved to be useful as part of an expert system developed to assist selection of tree species for tropical and sub-tropical plantations (Webb et al. 1980). In the second edition of their system, Webb et al. (1984) described the climatic requirements of 175 species in terms of ranges of these six climatic factors. Any one of these descriptions can be input into a climatic mapping program. Areas which satisfy the requirements are shown on the microcomputer screen in green, whilst locations which do not satisfy the description are shown in red. The user can move a marker over any location and check the detailed climatic conditions at a particular location.

Descriptions of the requirements of particular trees (or other plants and also animals) can be developed from bioclimatic analyses of natural distributions. For example, the BIOCLIM program accepts geocoded information describing natural distributions and uses climatic interpolation relationships to determine the range of climatic conditions where a particular plant or animal is found (Booth 1985, Nix 1986, Busby 1991). This type of analysis can provide a first indication of a particular tree’s climatic requirements. However, many trees can grow successfully in conditions which are somewhat different from those they experience within their natural range. Information from trials and large scale plantations outside the natural range can be used to improve descriptions of requirements.

Climatic mapping programs, like more sophisticated GIS packages, allow users to visualise the implications of particular descriptions. For example, the Webb et al. (1980, 1984) description of the requirements of Pinus radiata implied that New Zealand was climatically unsuitable when it was plotted using a climatic mapping program for the whole world. As there are over 1.2 million hectares of Pinus radiata plantations in New Zealand something was obviously wrong with the description. Using the program’s moveable marker it was easy to check conditions at sites in New Zealand and correct the errors in the description (Booth 1990). Working with individuals who have experience with growing trees in particular regions, climatic mapping programs can be used to quickly check and improve existing descriptions of trees’ requirements (e.g. Booth and Pryor 1991).

As part of ACIAR Project 9127 climatic mapping programs were prepared at the CSIRO Division of Forestry for China (100 000 grid points), Thailand (40 000 grid points), South East Asia (10 000 grid points) and Latin America (66 000 grid points – interpolated climatic data kindly supplied by Dr Peter Jones, CIAT, Colombia). Most of the programs have been developed for the MS-DOS environment, which is the most common operating system on PCs used in the developing world. However, a version of the THAI program has recently also been developed for the Windows environment. Windows allows multitasking, which makes it easy to compare maps produced by different descriptions on the computer’s screen, as well as providing built-in support for hundreds of different printers. Figure 1 shows the areas of Thailand which are climatically suitable for provenances of Acacia auriculiformis from Papua New Guinea.

figure 1

As part of an AusAID-supported project climatic mapping programs were developed for Indonesia (26 000 grid points), Vietnam (16 000 grid points) and the Philippines (13 000 grid points). Papers describing the development of these programs are all included in the ACIAR proceedings.

Whilst a climatic mapping program satisfies a simple definition of a GIS as “;a computer system capable of holding and using data describing places on the earth’s surface”; (ESRI 1992) many people would consider them too simple to be called a GIS. Whether they are a GIS or not they certainly provide a useful and appropriate means of delivering environmental information to users in developing countries.

Users who have both the equipment and skills necessary to operate more elaborate systems find it easy to incorporate the interpolated climatic databases used by climatic mapping programs into GISs.

 Plantgro

Being able to identify where particular trees (or other plants) will grow is useful, but many people need to know how well they will grow on particular sites. Generally they do not require highly precise predictions of yield, but they do need to know whether growth will be good, fair, poor or useless.

Detailed process-based models are available for the dozen or so major crop plants, such as wheat and rice, which dominate world agricultural production (e.g. Godwin et al. 1989, Singh et al. 1993). These simulate complex processes such as light interception, photosynthesis and translocation and in many cases provide quite reliable estimates of yield.

A few process-based models have been developed for trees (e.g. McMurtrie et al. 1989), but there is no prospect of such detailed models being developed for the hundreds of tree species which are important in forestry around the world.

Dr Clive Hackett faced a similar problem in 1984 when he was asked to take part in a study of village-based subsistence agriculture and small-holder cash cropping in Papua New Guinea (Hackett 1988). There were numerous plant species involved and relatively little was known about their environmental requirements.

Hackett devised a new and simple method for providing coarse predictions of the growth of lesser-known plants. To assess the suitability of particular climatic or soil factors he used ‘notional relationships’, which are simply two-dimensional graphs made up of linear segments indicating conditions which are most suitable for growth and those which are less suitable.

These are used along with more complex calculations of the effects of light, temperature and moisture. To combine the effects of all factors he used Liebig’s Law of the Minimum (Liebig 1885), which was originally devised to describe the effects of available plant nutrients on plant performance.

In simple terms this states that the most limiting factor determines plant performance (i.e. favourable levels of other factors do not compensate for the unfavourable level of the limiting factor). Overall conditions were evaluated according to limitation ratings on a 0-9 scale where 0 indicates ideal conditions (i.e. no limitations) and 9 indicates the greatest possible limitations.

The PC-based Plantgro program (Hackett 1991) evaluates 11 soil factors including phosphorus, potassium, nitrogen, slope and drainage. Climatic data used include maximum temperature, minimum temperature, precipitation, evaporation and solar radiation. Monthly mean data are usually used for trees, but the program can also evaluate ten-day or weekly data.

The program evaluates the effects of temperature on development as well as carrying out simple water balance calculations. To run the program a plant file, soil file and climate file are required. The program provides summary predictions of likely growth patterns as well as detailed evaluations of limitations due to light, temperature, moisture and important soil factors.

Figure 2 shows an example of the summary output produced by Plantgro for a northern provenance of Eucalyptus camaldulensis growing at a trial site near Bangkok in Thailand. Growth is steady for much of the year, but is limited in the period from December to March. Inspection of Plantgro’s detailed output would indicate that the growth limitations in the December to March period are mainly due to moisture stress because of the dry period, whilst growth during the rest of the year is limited at this particular site by soil depth. The ’77.0′s at the foot of the figure indicate that a perennial plant is being evaluated.

 The Plantgro program has recently been used as part of a multi-million dollar project developing a ‘National Masterplan for Forest Plantations’ (NMFP) in Indonesia. This work was carried out by the DHV consultancy company and some of the work is outlined in the ACIAR proceedings. For example, Davidson (1996) describes how Plantgro plant files were developed for about 50 tree species. For the better-known species, such as Tectona grandis (teak) and Acacia mangium numerous trial results provided a good basis for the development of the notional relationships which described the trees’ responses to environmental conditions. For the lesser-known trees the notional relationships were more educated guesses.

Though the exact form of a response may not be known, there is usually some evidence for a species general preferences, for example, its need for acidic, neutral and/or alkaline soil conditions. Pawitan (1996) describes how a batch file version of Plantgro was developed, Plantgro limitation ratings were related to standard growth curves for different species to predict potential yield, yield predictions were used to evaluate the economic viability of particular projects and recommended land uses were plotted using the ARC/INFO and Arc/View GIS packages.

Plantgro was also used in ACIAR project 9127 and Hackett (1996) describes the use of foresters’ expert opinions to develop Plantgro plant files. The development of a database of 244 Plantgro soil and climate files for Thailand is also described (Taweesak et al. 1996). The soil files were developed from detailed soil chemical and physical analyses, which had been carried out for four horizons at sites representing the major soil types of Thailand. The climate files were prepared using the interpolation relationships for

Thailand (Zuo et al. 1996). Using the tree files developed for the NMFP project it would be possible to estimate the potential productivity of 50 tree species for all these 244 sites. As part of ACIAR project 9127 tree growth, soil and climate measurements were also collected from over 200 tree trial plots in Thailand and China (Sirirat et al. 1996, Yan Hong 1996).

Simulation mapping programs 

The Plantgro program is generally used to predict growth at individual locations or small numbers of sites. However, it is useful to be able to see the suitability for particular species and provenances over wide areas both to check descriptions of requirements and to make recommendations of trees for particular regions. PC-based simulation mapping programs allow a simplified version of the Plantgro model to be run for thousands of grid points. They use interpolated monthly mean climatic data to carry out the same light, non-linear heat sum and water balance calculations as Plantgro, but use information from maps to carry out a simplified assessment of soil limitations.

The ACIAR proceedings includes a description of simulation mapping programs developed for Africa, Australia, Thailand and China containing data for 10 187, 11 299, 6 242 and 15 789 grid points respectively (Booth 1996c). The programs output three maps showing limitations for soils, climate and the two factors combined. For example, Figure 3 shows the suitability of 15 789 sites across China for the Petford provenance of Eucalyptus camaldulensis. In the example shown the model was run for all 15 789 grid points, a process which takes about 85 seconds on a 90mhz Pentium PC. It is possible to speed the operation of the program by restricting the analysis to areas satisfying a description of climatic requirements similar to those used by climatic mapping programs. This description is shown below the maps.

 figure 3

 A marker can be moved over any location and a summary of the month-by-month limitations is shown for that particular location (see Figure 4).

 In Figure 4 the Ge73-2/3a soil type was assessed as having a level 4 limitation rating, which remains unchanged for all the months of the year. In January limitations for solar radiation, temperature and moisture were 3, 8 and 4 respectively. Applying Liebig’s Law of the Minimum the greatest limitation in January is due to temperature and is rated as 8 (i.e. a major limitation). In contrast the greatest limitation in July is due to soil factors and is a moderate rating of 4. The colours shown for this single location on each of the three maps in Figure 3 simply indicate the mean limitations for soil, climate and overall conditions.

 Discussion

The need for effective integration of GIS and environmental modelling is probably greater in developing countries than in the rest of the world, as environmental systems in many areas are already under great strain. At the same time the support available for sophisticated technologies is a fraction of that available in wealthy nations. Appropriate technologies need to be quick, simple and cheap.

Reliable climatic data are essential for predicting plant growth and modern interpolation methods can provide this information economically for any location on earth. Climatic mapping programs provide an effective means of delivering this information to users with minimal computing facilities and GIS skills. The Plantgro model provides a simple means of providing estimates of the potential growth of lesser-known plants. It was originally designed to work if necessary on text-only microcomputers, but the National Masterplan for Forest Plantations project has shown that it can also play a vital part in a multi-million dollar GIS analysis. Simulation mapping programs provide broadscale Plantgro-based analyses on PC-based systems.

Preparing appropriate tools and making them available either free or at minimal cost is only part of the process of ensuring the uptake of environmental assessment methods in developing countries. Training has been an important part of the work described here. In the last three years courses have been given in Thailand (2), China (2), Vietnam, Indonesia and the Philippines. Generally, one week training courses have been provided. The first day has been an open seminar, which could be attended not only by the main group of trainees, but also by senior decision-makers and students (see, for example, Murdiyarso and Booth 1994). In the following three or four days a much smaller group of 12-16 trainees have been given "hands-on" training in the use of the climatic mapping, simulation mapping and Plantgro programs, usually operating two to a computer.

A course in Vietnam was successfully given in 1994 using microcomputers with 286-type processors. These modest machines were not only capable of running the programs described here, but were also used to show an animated fly-by of a digital elevation model of Vietnam (Booth 1995). Animations are useful in providing a quick appreciation of the effects of topography on climate. A good example of the effective uptake of the training provided in Vietnam was the use of a climatic mapping program by one of these trainees after the course to develop descriptions of the climatic requirements of nine native and nine exotic tree species important for plantations in Vietnam (Nghia 1996). Maps generated using climatic mapping programs are also beginning to appear in reference texts, such as “Growing Exotic Trees in China” (Pan and You 1994) and “Trees for Saltland” (Marcar et al. 1995).

Validation is a major problem with developing and applying models such as Plantgro. Some brief reports of validation work are included in the ACIAR proceedings (Booth 1996). However, more validation work needs to be done particularly with large datasets (i.e. >50 sites). Unfortunately, few datasets include information in sufficient detail or from a large enough number of sites to provide a really effective basis for validation (e.g. Schonau 1969, Hunter and Gibson 1984).

Even where such large datasets exist, access may be restricted for commercial reasons. The large datasets which do exist tend to be from single countries and therefore usually do not explore the full range of conditions under which a species may be grown. Opportunities to develop large datasets by combining information from several countries are severely restricted because of the lack of an internationally agreed minimum dataset for recording results from forestry trials. Forestry trials are expense to establish and maintain, so it is unfortunate that greater

efforts are not made to encourage the sharing of information. There is a great need for organisations such as the Center for International Forestry Research (CIFOR) to establish standards which would facilitate the exchange of data between countries.

The programs described here are of great help in assisting species introductions. However, the decision to introduce new species to an area should not be taken lightly and ecological as well as socio-economic impacts need to be carefully considered. Small scale trials should always be undertaken before large scale plantation establishment is attempted. Attention should also be given to establishing plantations which are ecologically sustainable (Nambiar and Brown 1996).

More information about the methods described here is included in ACIAR Proceedings no. 63 ‘Matching Trees and Sites’ which is available from Bibliotech, GPO Box 4, Canberra, ACT 2601, Australia (fax +61 6 257 5088). Persons and institutions working in relevant areas in developing countries who may be eligible for a free copy should write to Publications, ACIAR, GPO Box 1571 Canberra, ACT 2601, Australia.

 Acknowledgements

I am grateful to the Australian Centre for International Agricultural Research (ACIAR) and the Australian Agency for international Development (AusAID) for their financial support of the work described in this paper. I am also grateful to numerous collaborators in Australia, China, Thailand, Vietnam, Indonesia, the Philippines, Laos, Costa Rica, Colombia, Kenya and many other countries. Full acknowledgements are given in several papers included in the ‘Matching Trees and Sites’ publication (Booth 1996).

 References

Booth, T.H. (1985) A new method for assisting species selection. Commonwealth Forestry Review 64: 241-250.

Booth, T.H. (1990) Mapping regions climatically suitable for particular tree species at the global scale. Forest Ecology and Management 36: 47-60.

Booth, T.H. (1995) Flying around the world. GIS User 14: 18-20.

Booth, T.H. ed. (1996a) Matching Trees and Sites. Proceedings of an international workshop held at Bangkok, Thailand, 27-30 March 1995. ACIAR Proceedings No. 63.

Booth, T.H. (1996b) The development of climatic mapping programs and climatic mapping in Australia. In Booth, T.H. ed. Matching Trees and Sites, ACIAR Proceedings No. 63.

Booth, T.H. (1996c) Simulation mapping programs for Africa, China, Thailand and Australia. In Booth, T.H. ed. Matching Trees and Sites, ACIAR Proceedings No. 63.

Booth, T.H. and Pryor, L.D. (1991) Climatic requirements of some commercially important eucalypt species. Forest Ecology and Management 43: 47-60.

Busby, J.R. (1991) BIOCLIM – a bioclimatic analysis and prediction system. In Margules, C.R. and Austin, M.P. eds. Nature Conservation: cost effective biological surveys and data analysis. Melbourne: CSIRO, pp. 64-68.

Davidson, J. (1996) Developing Plantgro plant files for forest trees. In Booth, T.H. (ed.) Matching Trees and Sites, ACIAR Proceedings No. 63.

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Trevor H. Booth

CSIRO Division of Forestry

PO Box 4008