Climate-smart decision support system for climate-smart agriculture

Global water scarcity remains the main growing challenge faced by the agriculture sector. This condition is attributed partly to the tremendous increase in the demand for different water uses and the climate change phenomena. Modeling crop water demands and irrigation water allocation under climate change involves several step-by-step approaches that may be tedious and time-consuming for many water users who may be laymen in this field. Climate-smart Rice Irrigation Management Information System (CSRIMIS) is a user-friendly interactive program consisting of four main modules integrated in MATLAB and its graphical user interface development environment (GUIDE). The program comprises of 4 key modules; stochastic rainfall, reference evapotranspiration, water demand and water allocation and monitoring. MATLAB programming language was used to develop the program and its graphical user interface (GUI). It simulates rainfall, potential evapotranspiration and water allocation deliveries in paddy fields at the Tanjung Karang irrigation scheme while accounting for the impacts of climate change. The model is runs with ten global climate models (GCMs) and three emission scenarios (RCP4.5, 6.0 and 8.5). Several hydro-climatic parameters can be generated from the model based on a daily water balance model with inputs data from GCMs projections, crop, soil and field conditions, and therefore, allows water managers to make a fast decision for rice water management. A stochastic daily rainfall model was developed for forecasting future rainfall. Outputs of a hydrological model were incorporated within the program for assessing flows of the Bernam River, which is a source of irrigation water for the scheme. The program assesses the water requirements by adjusting the reference evapotranspiration (ETo) for projected future emission scenarios based on GCM simulations. The interface is the framework for linking all the modules within the program and provides the user with the ability to access data and output from the system, based on a mouse-driven approach with pop-up windows, pull-down menus and button controls. The multi-model projections show an increase in future temperature (tmax and tmin) in all respective scenarios, up to an average of 2.5 °C for under the worst-case scenario (RC8.5). This paper discusses the CSRIMIS program and presents some of its outputs as relates to the four modules. Generated outputs can be obtained via individual GCMs as well as multi-models (ensemble) projection in the form of graphs and tables that can be converted into excel format for further analysis. The model was applied to evaluate climate change impacts on irrigation water demand and other key hydro-climatic parameters over the time period 2010-2099 with respect to the baseline period (1976-2005) in the Tanjung Karang Rice Irrigation Scheme, in Malaysia. The analyses show that the irrigation water demand will increase in the off-season and, a decrease is expected during the main season due to significant contribution from effective rainfall. The tool can be used as a guideline for managing water resources under climate change, and could therefore be helpful in promoting adoption of appropriate adaptation and mitigation strategies that can lead to more sustainable water use at farm level climate forcing. CSA-DSS gives a faster and reliable projection of the future conditions of global climate systems. It allows improved water management and adaptation of agricultural systems to enhance water use performance and water productivity, particularly, to face water scarcity. This tool provides better understanding of the Government sectors for instituting water policy and implementing allocation measures for irrigation and water resources. It has the capability to support the knowledge-based decision making through intensive climate-related research and development; and capacity building of adaptation and mitigation measures. It helps to develop new strategies to adapt to climate change impacts and new climates. Methodological limitations to the study and suggested future improvements are also discussed.

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