Browsing by Subject "Northern Thailand"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Publication
    Adaption to rainfall and temperature variability through integration of mungbean in maize cropping
    (2021) Khongdee, Nuttapon; Cadisch, Georg
    Climate change has threatened global agricultural activities, particularly in tropical and subtropical regions. Rainfed cropping regions have become under more intense risk of crop yield loss and crop failure, especially in upland areas which are also prone to soil erosion. In Thailand, maize is one of the important economic crops and mostly grown in upland areas of northern regions. Maize yield productivity largely depends on the onset of seasonal rainfall. Uncertainty of seasonal rainfall adversely affects maize yield productivity. Therefore, coping strategies are urgently needed to stabilize maize yields under climate variability. In order to identify suitable coping strategies, early maize sowing and maize and mungbean relay cropping were tested on upland fields of northern Thailand. The specific aims of this thesis were (i) monitoring growth and yield performance of maize and mungbean under relay cropping, (ii) testing early maize sowing and maize – mungbean relay cropping as coping strategies under rainfall variations (Chapter 2), (iii) testing effects of relay cropping on growth and yield of mungbean under weather variability (Chapter 4), (iv) determining suitable sowing dates under erratic rainfall patterns by using a modelling approach (Chapter 3), and (v) developing a technique for diagnosis of crop water stress in maize by thermal imaging technique (Chapter 5). Specifically, in Chapter 2 early maize planting or relay cropping strategies were assessed for growth and yield performance of maize under heat and drought conditions. Maize planted in July showed, regardless of sole or relay cropping, low grain formation as a consequence of adverse weather conditions during generative growth. However, July-planted maize relay cropping produced higher above ground biomass than July-planted maize sole cropping and early planting of maize in June. Despite unfavourable weather conditions, maize was, at least partly, able to compensate for such effects when relayed cropped, achieving a higher yield compared to maize sole cropping. June-planted maize sole cropping, however, was fully able to escape such a critical phase and achieved the highest grain yield (8.5 Mg ha-1); however, its associated risk with insufficient rain after early rain spells needs to be considered. Relay cropping showed to be an alternative coping strategy to cope with extreme weather as compared to maize sole cropping. However, relay cropping reduced maize growth due to light competition at young stages of maize before mungbean was harvested (Chapter 2). This negative impact of relay cropping is partly off-set by considering of land equivalent ratio (Chapter 4). Land equivalent ratio indicated a beneficial effect of relay cropping over maize and mungbean solecropping (LER = 2.26). During high precipitation, mungbean sole cropping produced higher yield (1.3 Mg ha-1) than mungbean relay cropping (0.7 Mg ha-1). In contrast to the period of low precipitation, mungbean relay cropping used available water more efficiently and was able to establish its plant, while mungbean sole cropping could not fully withstand severe drought and heat. Mulching effects of maize residues conserved soil water which was then available for mungbean to grow under extreme weather condition. WaNuLCAS modelling approaches can be used to support the decision of maize sowing date in northern Thailand to cope with climate change as indicated by goodness of fit of the model validation (R2 = 0.83, EF = -0.61, RMSE = 0.14, ME = 0.16, CRM = 0.02 and CD = 0.56) (Chapter 3) using forty-eight-year of historical rainfall patterns of Phitsanulok province. Only 27.1% of rainfall probability was classified as a normal rainfall condition. Consequently, maize in this region had faced with high rainfall variability. From long term simulation runs, the current maize sowing date led to strong maize yield variation depending on rainfall condition. Early maize sowing i.e. 15 and 30 days before farmers and staggered planting produced higher yield than current farmers’ practice (mid of July) in most conditions (91.7%). Simulations revealed that water was the most limiting factor affecting maize growth and yield while nutrients (N and P) had only limited impact. Results of the WaNuLCAS model could be used to identify optimal maize planting date in the area prone to soil erosion and climate variation of northern Thailand; however, the model cannot fully account for heat stress. Thermal imaging technique is a useful method for diagnose maize water status. As presented in chapter 5, the developed Crop Water Stress Index (CWSI) using a new approach of wet/dry references revealed a strong relationship between CWSI and stomatal conductance (R2 = 0.82). Our study results established a linear relationship to predict final maize grain yield and CWSI values at 55 DAS as follows “Yield = -16.05×CWSI55DAS + 9.646”. Both early planting of maize and/or relay cropping with legumes are suitable coping strategies for rainfall variability prone regions. The positive response of early planting and legume relay cropping offers the opportunity of having a short-duration crop as sequential crop, providing an additional source of protein for humans and fostering crop diversification on-site. This leads to a win-win situation for farmers, food security and the environment due to an enhanced sustainability of this cropping system.
  • Thumbnail Image
    Publication
    Media supported communication in agricultural extension and participatory rural development in Northern Thailand
    (2004) Fischer, Isabel
    The inhabitants of Northern Thailand, Thais as well as the members of the different ethnic groups, the so-called hill tribes, face a variety of very complex problems that range from natural resources conflicts via human rights issues to health problems. All in all, those issues constitute the initial point of departure for every extension, development and research activity, regardless of whether carried out by governmental, non-governmental organizations and/or other agencies. In order to analyze the current extension situation in Northern Thailand, field research was carried out in collaboration with different governmental and non-governmental organizations. Insights were gained into major areas of operation as well as currently used extension methods and media (especially picture supported communication tools) that are used in extension and rural development work, particularly when the target group is illiterate. Three organizations were observed in more detail and will serve as case studies. The paper presents Methods and Media Used by Different Organizations as well as major Criteria for Using Media in Extension and Development Work. Furthermore, the Application of Criteria for Different Methods and Media will provide the basis for the discussion of major potentials and limitations of currently used media in comparison to the Flannelgraph method, which was chosen as a major methodological tool of reference. In summary, it appears that the extension situation in Northern Thailand is too heterogeneous to identify the one ?right? extension approach. The use of media and the choice of extension methods depend on specific financial, logistic, methodological and cultural criteria as well as further issues, such as infrastructure, target group and the costs of the respective tool. In order to increase the potential and decrease the limitations of the currently used methods, the organizations have to concentrate on a better use of the already existing means as well as the improvement of those factors that are not fully used at the moment.
  • Thumbnail Image
    Publication
    Simulation of the sustainability of farming systems in Northern Thailand
    (2008) Potchanasin, Chakrit; Zeddies, Jürgen
    Introduction Due to an increase in environmental problems and resource degradation, economic development should be pursued with consideration of environmental functions and the supply and quality of natural resources. Monitoring and assessment of whether the development approaches a sustainable path are required to provide information for policy development. This becomes increasingly important ? especially for marginal areas where the environment and natural resources are sensitive. The study area is located in the mountainous area of Northern Thailand with abundant natural resources and a healthy ecological environment. However, population growth, land limitation, and external factors ? such as market forces ? are inducing change and pressure on resource utilization. The resources are intensively used and farming systems are changing to more commercial practices. Therefore, the region?s long term sustainability needs investigation. Objectives This study aims at assessing the sustainability of the farming systems in the study area under the sustainability concept, farming systems approach and Multi-Agent Systems (MAS) approach. The first objective of this study is to describe the characteristics of the farming systems in the study area. The second objective is to develop and use a MAS model to evaluate sustainability of the study area. The last objective is to use the model to present sustainability of farming systems under different scenarios based on changes of significant factors and policy intervention. In addition, the ability of the systems to cope with and recover themselves from these changes is examined. Methodology The sustainability of the farming systems in the study area was assessed through defined indicators representing three conditions: the economic, social and environmental condition. The indicators were defined based on the framework of indicator determination to serve the objectives and methodology of this study. The selected indicators for this study are: household income, net farm income, household capital, household saving, food security, top-soil erosion and fallow period. For these indicators the following sustainability classes were defined: Sustained (S), Conditional sustained (C), and Non-sustained (N) class. Evaluation of sustainability was carried out at two levels: the household and the village level. At the household level the sustainability situation was evaluated based on the individual farm household performance corresponding to each indicator. The sustainability at village level was assessed through the Sustainability index (SI) when single indicators are considered and the Performance index (PI) in which a group of indicators is regarded. The dynamics of the sustainability situation at household and village level were extrapolated over 15 years (2003 ? 2017) in order to examine the sustainability of the study area?s farming systems. The MAS model was developed and named CatchScapeFS. The model structure relies on descriptions of the farming systems in the study area. The MAS approach was applied in order to capture the complexity and extrapolate the long-term sustainability situation in the study area. The model composes of two components: a biophysical and a socioeconomic component. The biophysical component is based on the CatchScape3 model. It consists of biophysical models: a hydrological model, a crop model, a water balance model and a soil erosion model, which are embedded in the landscape model of the study area (represented in spatial grid cells as plots of one rai or 0.16 ha). The socioeconomic component is composed of farm household agents and other social elements. The farm household samples were classified based on the similarity of characteristics and behaviour into the market, subsistence, and partnership oriented group. The Monte Carlo technique was applied to generate farm agents out of the existing farm household samples. The CatchScapeFS model was designed according to the object-oriented modelling approach. The CORMAS platform was selected as a capable tool to facilitate modelling and simulation. During a simulation time step covering 10 days, activities in six principal phases including activities in eight phases of farm agent household activities are executed. The model was validated and tested for its stability. Validation was conducted by social validation and statistic data comparison validation. The results of the model validation and stability test showed the reliability of using the model to serve the study objectives. Main results Sustainability of study area at the household level The results show unsustainability over time in the study area. The number of households in the Sustained class (S) decreases whereas the number in the Non-sustained (N) and Conditional sustained class (C) tend to increase. For the economic condition, unsustained aspects occurred because of rising private household expenditure and decreasing capital products on the farm. For the social condition, the results show an increase of the households? rice deficit and rice acquisition in the long run which enhances the area?s unsustainability. For the environmental condition, erosion and shortening fallow aspects induce the area?s unsustainability. The area?s erosion is severe and increases over time. For the fallow aspect, the average fallow period is shortening because of intensive land use in order to produce for consumption ? which potentially induces land degradation in the long run. Sustainability of the study area at village level Similar to the results at household level, the findings show that farming systems in the study area are not sustainable. Unsustainability was observed by a declining Performance index (PI) and declining Sustainability indexes (SIs) of all indicators in the long term. By considering PI values with the trends, the area?s sustainability in economic condition is better than the social and especially environmental condition. This can be explained by relative high SI values for the economic indicators compared to the SIs of the social and environmental indicators. By considering all SIs and their dynamic trend, sustainability issues can be ranked to determine the sustainability issues which need to be improved. Food security is the most unsustained issue followed by the issues of household saving, household capital, top-soil erosion, household income, fallow period, and net farm income respectively. Scenario analysis The scenarios were the implementation of a policy to improve sustainability and occurrence of unexpected events through changes of biophysical and economic factors. The scenario of the sustainability improving policy is defined as introduction of a high yield variety of upland rice and maize including introduction of mango to the households who currently only produce annual crops. Unexpected events due to the change of biophysical factors were simulated with a drought and rain increasing scenario. A decreasing crop price scenario represented an unexpected event due to the change of an economic factor. Implementation of proposed sustainability improvement policy The results show that the sustainability in the study area is obviously improved; represented by an increase of the PI value with a positive trend over time. In addition, the SIs of many indicators increase in this scenario, except the SI of household saving, which was rather constant. The PI of economic indicators improves with a higher number of households in the sustainable class when considering the household income, net farm income and household capital indicators. For the social condition, PI and SI values of food security increase because of a reduced rice deficit. For the environmental condition, the PI value of the environmental indicators increases because of a reduction of soil erosion and a longer fallow periods. It can be concluded that this scenario provides a policy option which potentially leads to an improvement of the sustainability situation in the study area. Drought scenario The results show that the study area was still unsustainable similar to the baseline scenario. However, the results show a slightly better PI during drought with a higher value and a slower decrease over time. These are the effects of the trade-offs between the indicators. The top-soil erosion indicator (influenced by decreasing rain) becomes better. This positive effect compensates for the negative effects regarding household savings, food security and fallow period indicators ? which all declined. In addition, the simulation results presented the adaptation and reaction of farm agents to drought. Drought is perceived and causes a delay in planting to avoid damage. This induced a variation of the planted area. However, the variation becomes lower because of adaptation as the farm households learn from their experiences. During drought, an increase in the rice and maize deficiency occurred. The average amount of borrowed rice increased over time and the rice acquisition of the farm agents is performed by borrowing from the village rice bank and neighbours In addition, the farm agents acquire maize by collecting wild vegetables to feed their animals. Furthermore, the results indicate the ability of the farm households to cope with and to recover to some extent from a drought. Rain increasing scenario In this scenario, the study area was still unsustainable, similar to the baseline. However, for this scenario, the top-soil erosion is worse because of the increasing rainfall. The PI of economic indicators slightly increased in the first year with increasing rain because of the rising income from livestock production. However, this was caused by random effects influencing the model?s initial stage. For the social condition, there are only small random changes compared to the baseline scenario. For the environmental condition, the PI and SIs of environmental indicators become worse due to an increase of top-soil erosion. Price decreasing scenario The results show that the area?s sustainability is worse compared to the baseline. A reduction of the crop price directly affects household income and cash ? which consequently generates a cash deficit problem. However, due to the area characteristics and household behaviour, there is no effect on resource use because prices do not influence the farm agents? decision making. The PI of this scenario declines faster than in the baseline. This was affected by the decrease of the SIs of the economic indicators which decreased during the periods of the price fall. The households are confronted with a decline in cash which results in a deficiency of cash. Cash acquisition of the households is performed by selling livestock and borrowing from the village fund and neighbours. For the social and environmental condition, there are only small changes due to random effects. Policy recommendations Based on the study results, policies to improve sustainability of the study area farming systems are recommended. Firstly, to improve the area?s sustainability, the introduction of high yield variety of upland rice and maize with conservation practices as well as the introduction of mango to the farm households who currently produce only annual crops is recommended. Secondly, diverting research efforts to develop cash crop alternatives is required in order to improve household cash income. Thirdly, the promotion and support for raising livestock and off-farm activities, such as weaving and the development of tourism, should be performed in order to increase household cash income. Fourthly, awareness raising measures for stakeholders concerning environmental and resource protection have to be executed and achieved. For this, the CatchScapeFS model can be used as a tool to promote a common view between stakeholders. Fifthly, the introduction of birth control in this area is also necessary. Simultaneously, an understanding of households? regarding the effects of population growth should be created in order to obtain the villagers? cooperation without cultural conflicts. Recommendations for further research Guidelines for further studies and applications are recommended. Firstly, development of the model to be more realistic could be undertaken by representing more details of the systems, for example, introducing a nutrient soil dynamic model. However, this should be based on the considered research question (s) and should consider both the marginal benefits and marginal costs of development. Secondly, application of the CatchScapeFS model to other study areas would need to consider the compatibility of the model components and structure of the characteristics in the new study area. In addition, if applied to new areas the indicators to represent sustainability of the study area should be revised. Thirdly, applications following this study framework can be extended to different sustainability approaches ? such as sustainable rural livelihood or sustainable land management. However, the compatibility and relationship of the indicators with the study framework should be considered. Fourthly, a framework through application of object-oriented modelling is recommended as an alternative for further studies to investigate the consequences of policy interventions. However, resource requirements for any research application should be taken into account. Fifthly, the CatchScapeFS model can be used as a tool to test and monitor the effects of potential policies which can be implemented into Bor Krai village. Also, the model can be used as a tool to promote a common view of the overall village systems as well as to support collective decision making managed by stakeholders of the systems. Recommendations for newcomers to MAS application research Suggestions from the present study for newcomers have been proposed. The first recommendation to deal with the MAS application research is that newcomers have to learn the computer programs and programming. Learning programming with advice of programming experts at the beginning period and attention of newcomers to apply the code in different circumstances are highly recommended. Secondly, development of an integrated model in multidisciplinary research requires learning the academic knowledge from other disciplines. Therefore, determining the study objectives within the possible extent, introducing assumptions to simplify the additional disciplines, and consulting specialists to learn the required knowledge within a short time frame are suggested. Lastly, the development of integrated model requires a huge amount of data. Therefore, in the case which required data cannot be obtained, introducing assumptions based on theory and literature is recommended.