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Site-Specific NutrieNt MaNageMeNt (SSNM) iN thailaNd

Attaya PhinchongsakulditOffice of Soil Survey & Landuse Planning

Land Development Department2003/61 Phahon Yothin Road,

Lat Yao, Chatuchak,Bangkok 10900, Thailand

AbstrAct

A site-specific nutrient management (SSNM) research project was started in Thailand in 1997 by Professor Dr. Tasnee Attanandana and colleagues. The project focused on a low-cost technology with high efficiency and the protocols that can easily be followed by farmers. Soil classification was prepared in a simple way so that Thai farmers can identify their own soil. A soil test kit has been invented which allows farmers to analyze soil nutrients (NPK) by themselves. DSSAT and PDSS models were used to generate nitrogen (N) and phosphorus (P) requirements, and a specific model for potassium (K) requirement was developed. Nutrient requirement data from crop modeling was processed by simple formulas to generate fertilizer recommendation that provides the highest return under specific conditions. After the experimental trials, the project led to the steps of technology transferring and programming of SSNM for maize (SimCorn) in 2001. After the success of SSNM for maize, in 2005, the project expanded into SSNM for rice (SimRice) and sugarcane (SimCane). During this time, the project emphasized the independency of farmers. In 2008, the Land Development Department generated the Onfarm program following the policy of the Ministry of Agriculture and Cooperatives. The Onfarm program used the data from the SSNM projects together with the fertilizer recommendation based on soil test by the Department of Agriculture. As a result, the Onfarm program recommended fertilizers for major crops in Thailand. At present, a new research team is conducting the SSNM for chili.

Keywords: Site-specificnutrientmanagement(SSNM),soilidentification,soiltestkit,cropmodeling, SSNMprograms.

IntroductIon

Many studies have been conducted in Thailand that aim to help in managing plant nutrients for high crop yields. Most of the studies conducted in the government’s research stations around the country focused on fertilizer experiments investigating the responses of the plants to nutrients in specific environments. However, when the technologies were transferred and used in different areas of the country, often inconsistent results were obtained due to existing environmental factors, such as soil properties, climate, etc. Site-specific nutrient management (SSNM) study, therefore, focuses on using crop models coupled with field experiments in order to increase the efficiency of nutrient management for each specific environment. The SSNM project of Thailand was started in 1997 by Professor Dr. Tasnee Attanandana

of Kasetsart University. The project consultant was Professor Dr. Russell Yost of the University of Hawaii. The project focused on integrating a chemical fertilizer technology with soil information and pedotransfer functions. Nutrient requirements were evaluated with crop modeling by Mr. Sahaschai Kongton of the Land Development Department. A practical handbook for identifying types of soil was made by Mr. Taweesak Vearasilp of the Land Development Department. The researchers from the Department of Agriculture and the Department of Agricultural Extension worked on plot experiments in the field using information recommended from the crop modeling. Results from the field test were used to validate the fertilizer recommendation in the final report. The chemical fertilizer recommendation based on SSNM was distributed to farmers. Information from SSNM was used to generate the SimCorn

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program. The technology from the first version of SSNM for maize was transferred to corn farmers in four provinces in Thailand in 2001.The SimCorn program has been constructed for the personal computers and palm devices. However, the palm devices were expensive; the next version of SimCorn was, therefore, released only on the personal computers. After the success of SSNM for corn, in 2005 Prof. Dr. Tasnee expanded the project to rice and sugarcane. Dr. Prateep Verapatthnanirund, from the Eco-community Vigor Foundation, conducted the project which focused on capacity building of farmer-leaders, emphasizing the importance of self-awareness of farmers. Farmer-leaders can conduct their own experimental plot under the close guidance of the researchers. As a result, SSNM for rice and sugarcane was quickly transferred to farmers in 2006. In 2008, Thailand was affected by the drastically rising price of oil. The price of fertilizers greatly increased and Thai farmers economically suffered. The Ministry of Agriculture and Cooperatives had a policy to reduce the use of chemical fertilizers. However, chemical fertilizers were still needed in commercial agriculture. The Ministry of Agriculture and Cooperatives then decided to encourage farmers to use fertilizers more efficiently. As a result of the policy, the Land Development Department, Rice Department, Department of Agriculture and Department of Agricultural Extension generated the OnFarm Program to recommend the chemical fertilizers according to soil analysis and integrated the results with the SSNM project. The OnFarm program has been continuously improved, and the most updated version is Version 3. At present, a new research team is conducting the SSNM for chili.

Concept of the site-specific nutrient management in Thailand

Low-cost technology for Thai farmers

Most of the Thai farmers are poor. Technology extension needs to focus on low-cost technology with high efficiency and on protocols that are easily followed by farmers. This concept is the main direction of the development of the SSNM in Thailand. In the SSNM project, soil analysis is the main cost of the technology for farmers. Using the soil test kit, analysis of each sample costs 50 baht. The SSNM technology can help decrease the chemical fertilizer cost. In addition, the cost for the technology research was supported by the government and, therefore, is not incorporated into the cost for farmers. When farmers use the SSNM technology, it is either that the fertilizer cost is reduced, or the yields increase, or both. These result in higher economic returns for the farmers (Table 1). The development of low-cost technology with high efficiency through the SSNM project includes:

- Using crop modeling to evaluate recommendation for nutrient management in specific sites;

- Simplifying soil information for farmers so that they can identify soil series in their farmlands;

- Developing a simple method for soil analysis by using soil test kit to determine the amount of nutrients in the soil; and

- Empowering the farmers, so that they can discriminate and compare information among each other.

At present, the development of low-cost technology with high efficiency cannot cover all the

Table 1. Examples of the successful SSNM for corn in Phitsanulok province

SSNM Farmer’s Practice Fertilizer Cost Yield Fertilizer Yield (US$/ha) (kg/ha) Cost (kg/ha)Farmer (US$/ha)

A 200 11,575 233 9,744B 200 7,500 232 5,938C 171 9,282 232 8,344D 200 11,063 245 9,594

Average 193 9,855 236 8,405

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inputs and overcome all problems that may arise in crop production because there are various factors to consider. Therefore, the SSNM project only focused on the management of primary nutrients, i.e. NPK, and disregarded other factors such as soil pH, secondary nutrients, and micronutrients. Soil management based on soil series should be used to solve problems from other factors, and should also include recommendation on managing soil-limiting factors and soil organic matter.

Crop modeling

In Thailand, the soil can be classified into more than 300 series. Each series has different soil properties. Determination of the appropriate recommendation for each plant in each soil series needs more research. Having so many soil series requires more time, more budget, and more researchers. However, at the present, there are many mathematical equations that describe the influence of soil properties and plant growth. When soil properties and other environmental factors such as climate and crop management are combined, the yield response under the given environment can be predicted using crop modeling. As the objective of the research project was to find the primary nutrient recommendations, crop modeling was used to determine the requirements of NPK fertilizer that offer the best economic returns. The crop modeling programs used to evaluate nitrogen and phosphorus were DSSAT and PDSS, respectively. A specific crop modeling was developed for potassium. The decision support system for agrotechnology transfer (DSSAT) is a software application program that comprises crop simulation models for over 28 crops (version 4.5). The DSSAT can set environmental factors such as climate, soil properties, genetic information, nitrogen content, and general crop management information. After setting the environmental factors and determining the condition that phosphorus and potassium have no effect, the nitrogen requirement can be evaluated. The development concept of this project focuses on nitrogen because effects from nitrogen deficiency are very sensitive. To evaluate nitrogen requirement in this project, the DSSAT environment factors were set as follows:

- The phosphorus and potassium have no stress.- The average climate data (rainfall and

sunshine) for 30 years in each province were used.

- The soil properties data were limited to only soil series data that show interesting crop.

- Plant genetic data was limited to some varieties that have been studied before.

- Nitrogen content was set at three levels which are consistent with results from the soil test kit.

- Crop management data was set as default. Although crop modeling is a very useful tool, data entry and analyses for each data set take a lot of time. This is because there are many soil series and climatic conditions to consider. The analyses show yield data and rate of nitrogen fertilizer applied. The highest economic return can be obtained by multiplying the optimum recommendation yields with the price that was subtracted by the cost of nitrogen fertilizer. The nitrogen applied at highest economic return is defined as the amount of nitrogen fertilizer recommended for the specific crop and specific nitrogen content in soil under general management. The analyses from various climate data offer the maturity period and the appropriate planting time. The Phosphorus Decision Support System (PDSS) is being designed to assist in the diagnosis and the correction of P deficiencies in soils and crops, with emphasis on tropical conditions. This crop modeling uses less environmental factors than DSSAT, and gives only phosphorus prediction. The NPK recommendation from crop modeling analyses was presented in the form of N, P2O5 and K2O, which can be calculated into the chemical fertilizer recommendation using basic equations. The program also allows users to select the fertilizer formula and the fertilizer rate to be calculated. Although crop modeling was used to estimate the amount of nutrient requirement for plant growth and product yields, the estimation may not be hundred percent accurate. Verification with the actual field tests is needed. Many tests have been conducted in the fields to verify the accuracy of the models. Although they do not cover all of the soil series, the tests cover most of the major soil properties. When field results are not consistent with results from the model, the values of fertilizer recommendation will be adjusted, and a field test will be conducted again. The adjustment and testing will be conducted until the results are statistically acceptable. In the beginning of the project, the field tests were managed by government agencies, which had limited budget, time and number of researchers. After the farmers empowerment program, the farmer-leaders could manage the field

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tests, which resulted in an increase in statistically accepted results (Table 2). However, the fertilizer recommendation from crop modeling evaluated by field tests and adjusted in accordance with the instructions is still the only guideline for farmers to consider. The difficulties are due to the differences in the climate data in crop modeling and the local climate. Additionally, the soil data that were used in crop modeling came from the standard soil series data, which may not be similar with soil in the fields. The differences in soil and climate may affect the growth of plants. Genetic coefficient data that was used in crop modeling is genetic coefficient data of the representative varieties, which may be different from the varieties that the farmers grow. The agricultural management of farmers may also be different from the standard management. The differences in all of these parameters thus emphasize that the SSNM technology should still be in practice of farmers, which is needed to be applied to real environment of each farm. The success of the SSNM technology is an incentive to farmers. The technology just requires further improvement to make it suitable to the specific environment in their farms.

Simplified soil information

The nutrient management technology is affected when there is a change in the soil series. This is because the soil series is a bridge between the soil properties and nutrient management. Farmers do not need to understand the classification of soil series in detail, but they need to know the soil series of their farms. The challenge is to teach the farmers to identify their own soil.

Soil information is generally based on soil classification. The soil classification system in Thailand is based on the USDA soil taxonomy classification. The smallest unit in soil taxonomy is soil series that indicate the soil properties, such as soil texture, soil structure, soil color, mottle, soil depth, drainage, coarse fragment, soil fertility, etc., as well as the soil genesis and development of the soil. There are more than 300 soil series in Thailand. It is difficult for farmers to know and recognize all soils. The concept of the project is to simplify soil information for farmers so that they can identify the soil by themselves. Several factors have to be taken into an account. These are the following:

1) Soil properties that farmers can check manually and easily.

2) Optimal number of soil properties - not too many so that farmers can understand, and not too few that may cause lack of detailed properties to identify the soil.

3) Soil properties used can create a tree diagram of the soil series.

The handbook on simple soil identification for corn employs five soil properties, which are soil color, soil texture, coarse fragment, soil depth, and soil pH. These properties can distinguish 64 soil series in the Corn Belt area, using the categories of soil properties as follows:

- Soil color was divided into 4 levels: black/dark brown, brown/yellowish brown, red/reddish brown and light gray/pink.

- Soil texture was divided into 7 levels: loam/sandy loam, gravelly loam, clay, gravelly clay, sandy, loamy with rock fragment and sandy clay.

Table 2. Examples of site-specific fertilizer recommendation for rice in Nonthaburi province

Nutrient recommendation for Bangkhen series (Bn), Expected yield = 6,250 kg/ha

VL L M HN 50 25 0 P 25 19 13K 31 19 0

Fertilizer recommendation for soil analysis VL-L-M (N-P-K) (kg/ha)

N-P-K Basal application Top dress

50-25-19 18-64-0 63 46-0-0 31 0-0-60 31 46-0-0 56

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- Soil coarse fragment was divided into 6 levels: none fragment, rock fragment, limestone nodules, lateritic nodules, stone and boulders, and Quartz.

- Soil depth was divided into 3 levels: shallow, moderate and deep.

- Soil pH was divided into 3 levels: 4.5 - 5.5, 5.5 - 6.5 and 6.5 - 8.0.

When this soil identification guideline was used with the SSNM for rice and sugarcane, the results were not sufficient. Efforts were made to increase the soil mottle and the levels of certain soil properties in order to give more specific discrimination. However, the discrimination of each soil series was still not acceptable. Soil map was, therefore, needed to help locate soil series in farmers’ farm (Fig. 1). The well-established soil map will help farmers identify the soil by themselves. However, to distribute the soil map and to teach the farmers to read the map are still challenging. It also does not account for variations in the plots of land due to limitations of the resolution of the survey. The technology to produce the map also contains several steps that could easily result in an error. In addition, the exploration of some soil maps was made 30 years ago. Since then, the science principles, concepts and technologies have changed, which could result in changes in the soil classification. Therefore, direct checking by the farmers is the most accurate and reliable method, and results will be in accordance with the principles of nutrient management in most areas. Although the soil maps have limitations in identifying soil series, they can be used to formulate a set of soil series found in the area. The location of the soil gives a new concept of how to select factors that can be used to identify soil series. In

the OnFarm program, sub-district was used to filter out the soil series that are not found in sub-district for farmers, giving only a set of soil series that have properties similar to the soil characteristics of their farms. However, the program needs to be updated as well.

The soil test kit

The SSNM requires soil information from soil series, which relies on the recent soil analysis. Fertilizer recommendations will be incorrect when the soil analysis data from the original data set is used. However, the soil laboratory in Thailand cannot support all soil samples from farmers, and the test will take a long time. To solve this problem, Professor Dr. Tasnee Attanandana has developed a soil test kit which is efficient, quick, easy, and cheap. The basic steps of this soil test kit are: soil samples are extracted by acid, then the solution is used for analysis by various reagents, and the colors of the solution are compared to the standard colors chart. The critical point is that the amounts of soil samples and all reagents need to be measured with a standard cup and dropper for accurate analysis. The soil test kit developed by Kasetsart University uses the double acid extract agent (Mehlich I). It can analyze nitrogen in the nitrate and ammonium form. It can also analyze phosphorus, potassium, and soil pH. The results from the analyses are classified into five quality levels. Farmers have confirmed that this soil test kit is easy to use (Fig. 2).

Capacity building of the farmer-leaders

The capacity building of farmers and their organizations are essential for achieving a

Fig. 1. Soil identification handbook for four provinces in the Corn Belt area

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balance in economic, social and environmental development goals, and for enhancing sustainable rural development. The main components of capacity building include the following:

1) Self-reliance: Emphasis was placed on self-reliance by mobilizing social capital, local wisdom, and natural resources for sustainable rural development. Two fundamental concepts serve as the philosophical basis for the technique: Self-awareness and Self-reliance. Self-awareness is taught through discussions with the farmers by reminding them of the capital that they possess and for which they are responsible. This change in thinking will lead to adjustment in their ways of thinking, and subsequently to greater self-reliance. Examples of the capital they own are: morality; local wisdom; habits of saving and helping each other; warm family; strong community; rights & freedoms; good health; natural resources, etc.

2) Farmer-centered and balanced development: An enabling environment was created to facilitate farmers to develop by themselves. Development meant not only improvement in farmer’s knowledge and capacity for income generation but also in morale and increasing human security.

3) Participation of farmers: Active participation means that farmers must assume a major role in decision-making and managing their own affairs. Efforts were made to build confidence in farmers so that they make decisions on how to solve the problems identified by them. Other players, such as the Government Organizations and Nongovernment organizations (NGO), had supporting roles in providing guidance, comments, advice, and training required for confidence building.

Interactive Learning through Action is a

methodology developed to enable farmers to gain more control of their lives and businesses as farmers. Five steps of “interactive learning” are taught, often by example and by doing rather than by giving only the theory and concepts. The five steps include:

- Assembling the relevant people for a particular issue or problem, and gathering them in one place at the same time for discussion/action.

- Brainstorming about impacts of past development projects on their quality of life; problems and possible solutions are discussed, and improvements are proposed.

- Working together. Suggestions arising from the brainstorming sessions are acted upon rather than just discussed and dropped. This is an essential step where theory, ideas and new knowledge synthesized from the group’s experiences are put into practice.

- Summarizing the lessons learned. After the working sessions are concluded and the chosen activities are carried out, some time is taken to summarize, as a group, the lessons learned from the working activity and brainstorming sessions.

- Accepting the outcomes together. The final step in the first iteration of the process is reaching a consensus on the results and acceptance of the outcomes as a group with shared rewards and a feeling of community. This is a step of discussing, realizing, accepting, and participating in the results of the group activity. This is a step wherein social capital is built from the interactive learning experience.

4) Action research by farmers: Farmers were encouraged to conduct field experiments with advice from researchers. Knowledge and experiences gained from field

Fig. 2. The soil test kit developed by Professor Dr. Tasnee Attanandana, Kasetsart Univer

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experiments by different farmer groups or networks were exchanged through interactive learning forums.

5) Networking of farmer groups: Exchange and interactions between individual farmers or farmer groups include interactive learning and participatory technology development and transfer. Farmer networks are a way of facilitating farmer-to-farmer exchange of knowledge and experiences related to agricultural practices, natural resources management, and sustainable community development.

Site-specific nutrient management computer programs

SimCron, SimRice and SimCane

After the experimental trials, the project led to the steps of technology transfer. Implementation of the site-specific nutrient management computer program begins to make it easy for staff and farmer-leaders that can use the computer to recommend chemical fertilizers to farmers quickly. The SSNM program consists of chemical fertilizer recommendations and soil information that are more detailed than in the handbook. Users need to know only the name of the soil series or basic soil properties, type of plant, province, and the value of soil analysis from the soil test kit. After the user puts in the data, the programs will examine and compare the amount of fertilizer used per 0.16 acre. If the user changes fertilizer formula, the programs will recalculate the fertilizer rate varying according to the recommended nutrients. The parameters that were used to develop the applications are as follows:

1) Soil information or the soil series which includes the soil properties, soil management, soil profile photo, etc. - In the case of the SimCorn program, the soil properties are separated for each attribute of the database for soil identification by the program. The other applications for the identification system are not yet complete.

2) Province data sets which give information about the amount of soil series and the climate of the province - The soil series data in the province were obtained from the soil map of Thailand. In some cases, the same soil series in different provinces may have different fertilizer recommendation. This is because the research used climate data of

a specific province. The climate data has a dramatic effect on crop production such as the start of the planting season.

3) Crop data set, i.e. type of crop - In the first concept of the project, the recommendation gave details for a specific plant variety. Therefore, plant varieties were included in the crop data set. However, there is limited information on the genetic coefficient of crop varieties, making it impossible to conduct research on every plant variety. For example, there is genetic coefficient data for only one hybrid variety of corn, which was used for every other corn varieties. This is also the same case for sugarcane. In the case of rice, the research was conducted using genetic coefficient data from two varieties. The genetic coefficient of RM 105 was used for the native rice strain, and the genetic coefficient of Patumtani 1 was used for the non-sensitive to photoperiod strain. Therefore, the SimRice program users are required to determine the rice strain as well.

4) Nutrient requirement data set - This data set is the main data that is transferred to farmers. It includes the amount of nutrients in the N, P2O5

, K2O forms, which is used for each soil analysis using the soil test kit. The fertilizer recommendation data is the result of matching the user’s input data and the database. Therefore, the input data consists of soil information, province, type of crop, and soil analysis. The programs will calculate fertilizer recommendation from the nutrient requirement and chemical fertilizer formula.

5) Fertilizer formula - This data set is contained in a drop-down list, which facilitates users to put in the data quickly.

SimCorn, SimRice and SimCane share the same principles. Users need to choose the program according to plant type. After that, they have to launch the program on MS Access 2003 or higher, select the soil series by comparing soil properties listed in the program and soil properties in farm. SimCorn contains the soil identification system, by which after selecting the soil series, users are required to determine the province. And in case of SimRice, users are required to determine the strain of rice as well. The last step is to put in the soil analysis results from the soil test kit. The program will display how much each nutrient is needed for a particular season in the N, P2O5 and K2O forms. The users can also determine particular fertilizer

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formula for program to calculate the amounts of each chemical fertilizer needed.

The OnFarm Program

In 2008, the Ministry of Agriculture and Cooperatives had a policy to prepare the fertilizer recommendation according to the soil test for six major crops of Thailand (rice, corn, sugarcane, cassava, para rubber, and oil palm) as an alternative method for farmers to use fertilizers more economically and more efficiently. The Land Development Department had supplied the fertilizer recommendation application. The development team wanted to use the concept of SSNM like SimCorn, SimRice and SimCane, but there was no SSNM data for cassava, para rubber, and oil palm. Moreover, the fertilizer application recommendation for rice, corn, and sugarcane based on the SSNM concept did not cover all provinces in Thailand. With limited time, the development team decided to consider the fertilizer recommendation based on soil analysis that was developed by the Department of Agriculture. Although the analysis does not define nutrients for specific soil and climatic conditions, the fertilizer recommendation for each crop based on the soil analysis is acceptable. The development team integrated the data from the Department of Agriculture and the SSNM data together by weighting the importance of the data to the SSNM. The SSNM data were used when available. If not, the data recommended by the Department of Agriculture were used. This application is included in the Onfarm program. The principles of the OnFarm program development are the same as those in the SimCorn program (Fig. 3). It is easy to use, and aims to provide the answer to plant nutrient management in chemical fertilizer form. Users are required to know the administrative district, the name of soil series, type of plant, and the value of soil analysis. After the user enters data into the program,it will examine and compare the amount of fertilizer used per 0.16 acre. If the user changes fertilizer formula, the program will calculate new fertilizer rates according to the recommended nutrients. Therefore, the data that was used to develop the OnFarm program is similar to that of SimCorn program. The differences are as follows:

1) Administrative data, i.e. the sub-district of Thailand – The sub-district boundary will filter the soil series in sub-district, and display a list on user interface. The purpose of the selection of the sub-district is to make

it easier to match similar soils converted to farmland. However, the province data is still used for fertilizer recommendation if the selected data is the SSNM data that refers to the climate data of the province.

2) Soil information, including all soil series in Thailand - The soil series data includes soil properties, soil management, soil profile photo, etc. In the case of the OnFarm program, the importance of the soil data is for soil management because the fertilizer recommendation is based on the soil analysis of the Department of Agriculture, not the soil series. However, the soil series data is still used for fertilizer recommendation if the selected data is the SSNM data that refers to soil properties of soil series.

3) Crop data set, i.e. type of crop - The OnFarm program includes fertilizer recommendation for all crops in one program. Therefore, the type of crop is used to determine the amount of nutrient required. However, some crops may be separated according to strain or age. Genetic coefficient data is still used for fertilizer recommendation if the selected data is the SSNM data.

4) Nutrient requirement data set, i.e. the main data that is transferred to farmers - The soil analysis data can come from different methods. The OnFarm program supports the soil analysis data from the government soil laboratory services and from the soil test kit, including different types of solvent extraction of soil analysis. A major problem is the result of one soil analysis method may not be comparable with other soil analysis methods. The OnFarm program contains the database of standard soil analyses for all soil series. Therefore, the OnFarm program can give fertilizer guidelines, which can be applied by farmers who have no soil sample or soil analysis data.

5) Fertilizer formula - This data set is contained in a drop-down list, which facilitates users to put in the data quickly.

Users of the OnFarm program have to select the province, district, and sub-district. The program will filter the soil data and show the soil series list that can only be found in the sub-district. After selecting the soil series by comparing the soil properties in program with the soil properties in the farm, the users have to select the type of crop. The program will get the standard soil analysis

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Fig. 3. SimCorn program

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of the soil series from the database, and show fertilizer recommendation guideline on the screen. If users have new soil analysis, they can input the data and get a new specific and accurate fertilizer recommendation. In some conditions, mostly for rice and corn, the fertilizer recommendation data is the SSNM data, by which the recommendation is

related to the soil properties and climate. OnFarm program may not be counted as SSNM. Although the program contains the information on SSNM, it is a starting point for the development techniques and knowledge to SSNM (Fig. 4).

Fig. 4. OnFarm program version 3.0

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concLusIon

Problems and Limitations

Limitations of crop modeling

The SSNM concept that has been practiced in Thailand is not the best concept, but it is the most appropriate for Thailand today. However, there are limitations and problems involving the techniques, data, and process. Further researches are needed to solve the problems and improve the efficiency. The major limitation is lack of crop models. At present, there is no crop model that is perfect enough to handle all the environment factors. For example, the current version of DSSAT does not support phosphorus (P) and potassium (K) evaluation. The PDSS model is still needed to evaluate phosphorus (P). Both crop models consider different environmental factors, but do not consider the interaction between nitrogen (N) and phosphorus (P) affecting crop production. Another limitation of the crop models is that there is no crop model for trees that supports the SSNM concept. The data used in crop modeling is important for accuracy. The genetic coefficient data also does not cover all plant varieties. In addition, the climate data, such as rainfall, cannot be predicted at 100% accuracy in each year. Therefore, the crop model cannot be used to determine the optimal crop under rainfed conditions.

Difference in soil analysis methods

Soil analysis is an important factor in SSNM. Results of the soil analysis are the nutrient contents in the soil, thus providing a decent amount of fertilizer requirement of plants at the moment. The important problem is that different methods of analysis or different kinds of extract solution can give different results. Additionally, there is no good equation to convert the soil analysis results from one method to another. The crop modeling based on one soil analysis method may not give the same results when different soil analysis methods are used.

rEFErEncEs

Attanandana, T. and R.S. Yost. 2003. A site specific nutrient management approach for maize-Thailand. Better Crops International. Vol. 17, No.1 (3-7), May 2003. Potash and Phosphate Institute/Potash and phosphate Institute of

Canada.Attanandana, T. C. Suwannarat, T. Vearasilp, S.

Kongton, R. Meesawat, P. Boonampol, K. Soitong and A. Charoensaksiri. 1999. Nitrogen fertilizer recommendation for grain yield of corn using a modeling approach. Thai J. Agric. Sci. 32(1):73-83.

Attanandana, T., C. Suwannarat, T. Vearasilp, S. Kongton, R. Meesawat, P. Bunampol, K. Soitong, C. Tipanuka and R.S. Yost. 2000. NPK fertilizer Management for maize: decision aids and test kits. Thai Journal of Soil and Fertilizer 22:174-186.

Attanandana, T., C. Suwannarat, T. Vearasilp, S. Kongton, R. Meesawat, P. Boonampol, K. Soitong, C. Tipanuka and R.S. Yost. 2000. NPK fertilizer recommendation systems for corn: Decision aids and test kits. Thai Journal of Soil and Fertilizer 22(4):174-186.

Attanandana, T., C. Suwannarat, T. Vearasilp, S. Kongton, R. Meesawat, P. Boonampol, K. Soitong, C. Tipanuka and R.S. Yost. 2001. NPK fertilizer recommendation systems for corn: Decision aids and test kits. Paper presented at the International Conference on Nutrient Balances for Sustainable Agriculture Production and Natural Resource management in Southeast Asia. IBSRAM and DOA-URRC, Bangkok, Thailand. 20-22 February, 2001.

Verapattananirund, P. 2002: Local capacity building for poverty alleviation in rural Thailand. Paper presented at the International Workshop on Development of Slopeland Agriculture in Mainland Southeast Asia, March 14-15, 2002, Chiang Mai, Thailand.

Verapattananirund, P. 2003: Concept and process of empowering the Thai farmers. Paper presented at the International Training Workshop on Applying Information Technology for Site-specific Agriculture in Small Farms of the Tropics, August 4-10, 2003, Bangkok, Thailand.

Verapattananirund, P. 2004: Capacity building of farmer leaders for sustainable agriculture development in the central region of Thailand. Paper presented at the International Conference on Perspectives and Approaches for Sustainable Rural Development in Africa, 18-20 February 2004, SUA Center for Sustainable Rural Development, Morogoro, Tanzania.

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