conservation measures do make a difference regarding erosion, a dynamic decisionmaking model would need to allow farmer decisions to keep track of these factors
separately for each pixel.
In Chieng Khoi, after 10 years of intensive maize cropping, the trend of
increasing yields is still ongoing despite obvious soil degradation, and the
simulations shown here, and in Sect. 10.4, point in the right direction. On the
other hand, farmers are aware of problems caused by maize monocropping and soil
degradation, such as paddy and reservoir siltation, pest pressure and others, which
have not been modeled here. Recently, farmers have been starting to expand fodder
grass and cassava cultivation, which both reduce erosion, so in the future maize
may be grown only on the less erodible plots. The future generation of models
needs to account for such plot-specific characteristics.
Currently, LUCIA-Choice is being developed – a decision-making module,
which can be coupled with LUCIA. LUCIA-Choice contains a decision algorithm
based on household resources, crop preferences and plot quality. The latter includes
top-soil carbon contents and other indicators of soil fertility, and it is up to the
farmers (as parameterized by the user) how much importance they attribute to these
factors. This will allow a reflection of farmers’ levels of local knowledge on plotspecific characteristics in terms of their land.
10.3 Case Study 2: Assessing the Impact of Rice Production
in Thailand Under Climate Change Scenarios
10.3.1 Introduction
Mainland Southeast Asia covers six of the ten Association of Southeast Asian
Nations (ASEAN) member states, namely Cambodia, Lao PDR, Malaysia,
Myanmar, Thailand and Vietnam, and has an estimated population of 252 million
(2010).
3 Rice ecosystems cover a total area of 30.6 million ha, with respective
country land areas being 2.7, 0.9, 0.7, 8.0, 11.0 and 7.4 million ha for the above, and
these systems are very sensitive to changes in climatic, edaphic and socio-economic
conditions. Decision making to maintain rice ecosystem productivity, as well as
livelihoods, requires well-organized knowledge and information system tools to be
in place. Models that integrate spatial information and crop/weather databases
reflect such tools, as they facilitate better decision-making through collective
efforts, and provide an efficient communications platform based on organized and
standardized databases, structures and key processes for the relevant ecosystems,
including agricultural systems, watershed and regional production systems. The
purpose of this paper is to present an information technology tool, CropDSS, which
is able to link the Crop System Model-Decision Support System for Agro-technology
3 This section was written by Attachai Jintrawet, Chitnucha Buddhaboon, Vinai Sarawat, Sompong
Nilpunt, Suppakorn Chinvanno and Krirk Pannangpetch.
384
C. Marohn et al.
separately for each pixel.
In Chieng Khoi, after 10 years of intensive maize cropping, the trend of
increasing yields is still ongoing despite obvious soil degradation, and the
simulations shown here, and in Sect. 10.4, point in the right direction. On the
other hand, farmers are aware of problems caused by maize monocropping and soil
degradation, such as paddy and reservoir siltation, pest pressure and others, which
have not been modeled here. Recently, farmers have been starting to expand fodder
grass and cassava cultivation, which both reduce erosion, so in the future maize
may be grown only on the less erodible plots. The future generation of models
needs to account for such plot-specific characteristics.
Currently, LUCIA-Choice is being developed – a decision-making module,
which can be coupled with LUCIA. LUCIA-Choice contains a decision algorithm
based on household resources, crop preferences and plot quality. The latter includes
top-soil carbon contents and other indicators of soil fertility, and it is up to the
farmers (as parameterized by the user) how much importance they attribute to these
factors. This will allow a reflection of farmers’ levels of local knowledge on plotspecific characteristics in terms of their land.
10.3 Case Study 2: Assessing the Impact of Rice Production
in Thailand Under Climate Change Scenarios
10.3.1 Introduction
Mainland Southeast Asia covers six of the ten Association of Southeast Asian
Nations (ASEAN) member states, namely Cambodia, Lao PDR, Malaysia,
Myanmar, Thailand and Vietnam, and has an estimated population of 252 million
(2010).
3 Rice ecosystems cover a total area of 30.6 million ha, with respective
country land areas being 2.7, 0.9, 0.7, 8.0, 11.0 and 7.4 million ha for the above, and
these systems are very sensitive to changes in climatic, edaphic and socio-economic
conditions. Decision making to maintain rice ecosystem productivity, as well as
livelihoods, requires well-organized knowledge and information system tools to be
in place. Models that integrate spatial information and crop/weather databases
reflect such tools, as they facilitate better decision-making through collective
efforts, and provide an efficient communications platform based on organized and
standardized databases, structures and key processes for the relevant ecosystems,
including agricultural systems, watershed and regional production systems. The
purpose of this paper is to present an information technology tool, CropDSS, which
is able to link the Crop System Model-Decision Support System for Agro-technology
3 This section was written by Attachai Jintrawet, Chitnucha Buddhaboon, Vinai Sarawat, Sompong
Nilpunt, Suppakorn Chinvanno and Krirk Pannangpetch.
384
C. Marohn et al.