AIP for Climate-Resilient Agriculture

1 AIP for Climate-Resilient Agriculture

Contents 1. Introduction................................................................................................................3 1.1 Purpose of the document......................................................................................3 1.2 Definitions ...........................................................................................................3 1.3 Abbreviations.......................................................................................................4 2. Problem statement......................................................................................................6 2.1 Background ..........................................................................................................6 2.2 Problematics.........................................................................................................6 3. Stakeholders & End Users .........................................................................................7 4. AIP Conceptual Design..............................................................................................8 4.1 Scenario implementation (Indicator Instantiation) - AIP Cycle ..........................8 4.2 AIP CRA Conceptual Elements.........................................................................10 4.2.1. Measuring/Mapping/Monitoring/Modeling ..................................................11 4.2.2 Managing........................................................................................................12 5. AIP CRA Design......................................................................................................13 5.1 Climate-resilient Agriculture AIP......................................................................13 5.1.1 Main climatic change patterns for Thailand and the study region .................13 5.1.2 Effects of climate change on rice cultivation .................................................14 5.2 AIP Methodology ..............................................................................................15 5.2.1 Input Data .......................................................................................................17 5.2.2 Modelling approaches ....................................................................................18 5.2.3 Assessment of rice production under climate change scenarios ....................19 5.2.4 Action plan evaluations ..................................................................................20 6. AIP CRA dashboard features...................................................................................21 6.1 Dashboard tabs ..................................................................................................21 6.2 Dashboard features ............................................................................................21 6.2.1 Issues Tab .......................................................................................................21 6.2.2 Action Plan Tab..............................................................................................22 6.2.3 Report Tab ......................................................................................................22 2 AIP for Climate-Resilient Agriculture

1. Introduction 1.1 Purpose of the document This document, the THEOS-2 AIP for Climate Resilient Agriculture Development Plan, shows the design of the Climate-resilient Agriculture AIP which will be demonstrated on Rice cultivation in Nakhon Ratchasima province known as Korat province. This document is produced by GISTDA with the support of Airbus experts under the frame of the THEOS-2 WP 22400 AIP Support for New Area Development. The aim of this document is to give the direction for the AIP for Climate-resilient Agriculture (AIP CRA) development. The information in this document is the result from pre-study sessions, workshops with the experts from GISTDA and Airbus, AIP CRA working group discussion, and key informant meetings. The document contains scope of development and preliminary design which will be used during the detailed design and the development phase. Note that all tables, figures and schemas will be refined during the detailed design phase. 1.2 Definitions Table 1 includes the definitions of important terms used in AIP for Climate-resilient Agriculture (AIP CRA). Term Table 1: Definitions Description AIP The AIP or Actionable Intelligence Policy intends to bridge the gap ANN between science and policy. The AIP produces indicators that can support the goal of AIP and presents the results on a dashboard which allow end users to visualize. Artificial Neural Networks Goal The goal describes what would be the ultimate objective to be achieved by AIP usage. End users should be able to use the AIP dashboard to support their decisions in contributing to the goals. Dashboard The dashboard provides visualization of selected measures based on scenarios chosen by end users. The results presented on the dashboard can be used by the end users to make decisions. Stakeholder The stakeholders are groups, organizations, and people concerned by AIP usage and can support the AIP development through data sharing, transferring of knowledge, suggesting improvements, etc. 3 AIP for Climate-Resilient Agriculture

Term Description End users The end users shall be decision makers who decide actions and make decisions through AIP. End users can be part of stakeholders. Concept The conceptual design shows the link between the goals and the user design requirements. The conceptual design also explains the structure of the AIP without going into concrete applications. Design The design specifies all the necessary components linking the input data to the goal, and the interconnections between them. Development The development starts by the specification of methods to derive indicators from different input data to be implemented in a dashboard. After development the indicators will be able to be produced by using software and tools available in the dashboard. Indicator An indicator is value-added information created using a relevant model from a set of input data. This enables the decision makers to assess the different scenarios and action plans. 1.3 Abbreviations Abbreviations list can be found in Table 2 : Table 2: Table of abbreviations Abbreviation Definition AIP Actionable Intelligence Policy CRA Climate Resilient Agriculture DEM Digital Elevation Model DGR Department of Groundwater Resources DOAE Department of Agricultural Extension ECMWF European Centre for Medium-Range Weather Forecasts 4 AIP for Climate-Resilient Agriculture

GHG Greenhouse Gas GIS Geographic Information System GISTDA GPR Geo-Informatics and Space Technology Development Agency LDD Gaussian Process Regression MLR Land Development Department OAE Multiple Linear Regression ONEP Office of Agricultural Economics Office of Natural Resource and Environmental Policy and RCP Planning RID Representative Concentration Pathways RU-CORE Royal Irrigation Department Ramkhamhaeng University, Center of Regional Climate Change SPEI and Renewable Energy SSP Standardized Precipitation Evapo-transpiration Index SVM Shared Socioeconomic Pathways TDRI Support Vector Machine TMD Thailand Development Research Institute Foundation UC Thai Meteorological Department USGS Use Case United States Geological Surveys 5 AIP for Climate-Resilient Agriculture

2. Problem statement 2.1 Background According to the 2021 Global Climate Risk Index from GermanWatch, Thailand was ranked 9th globally in terms of its long-term vulnerability to the impacts of climate change. Climate change has demonstrated impacts on the agricultural sector, from directly affecting the physiology of plants and animals to altering the land suitability to specific crop cultivation. Changes in temperature and the pattern of rainfall can cause unprecedented drought and flood. Prolonged dry spells during the rainy season and short-duration intense precipitation have caused large-scale damage to agricultural areas and productivity. Agricultural practices would be hindered and would have to adapt. A country with an ill-prepared agricultural production system will likely face weakened food security which may lead to lower quality of life and impact economic sector. In response to these challenges, government agencies and relevant stakeholders must have a tool which allows them to anticipate the risks and impacts of future climate change on the country's agriculture and to assess the impact of mitigation of these risks by simulating dedicated action plans. 2.2 Problematics Thailand has a total land area of approximately 51.31 million hectares, of which around 40 percent is for crop production. Thailand's economic crops include rice, maize, cassava, sugarcane, rubber, and oil palm. Rice is the key economic crop that is not only a staple food source, but also an important agricultural export. Rice constitutes nearly half of the total cultivated area and almost 60 percent of which is in the north-east region. The high value fragrant Jasmine rice or Hom Mali rice is the main variety cultivated in the region. Nakhon Ratchasima or Korat is the largest province within the north-east region and almost 50 percent of its cultivation area is dedicated to Jasmine rice. Most paddy land is rainfed and can only support a crop of rice in the wet season which last from May to October. In this wet season, the erratic weather conditions, especially precipitation and temperature, can influence the occurrences of flood and drought which, in turn, affect rice production. Floods and drought have various impacts on the development of rice plants and its growth will eventually stop if the situation prolongs. Consequently, the harvested area and/or rice yields are expected to be reduced, especially on rainfed rice which highly depends on meteorological conditions. Therefore, GISTDA and stakeholders proposed the AIP Climate Resilient Agriculture (CRA) implementation in Korat as it is the largest province with substantial area for rainfed Jasmine rice. The AIP CRA will provide a dashboard for projecting and evaluating the impact of climate change on rice production. This dashboard will identify the rice production under climate change scenarios based on the rice areas and yield which experience flood and drought. The relevant end users can utilize the dashboard to design climate resilient policy and visualize their impacts. 6 AIP for Climate-Resilient Agriculture

3. Stakeholders & End Users The stakeholders have been identified based on two aspects: their knowledge relevant to climate-resilient agriculture and their potential usage of AIP CRA. The stakeholders include decision makers, government functional sectors, civil society organizations, and academia. In AIP CRA, there are 8 stakeholders. All 8 stakeholders can support AIP CRA development as information providers, while 5 main driving organizations who could be the users or early adopters of the AIP CRA were identified. The stakeholders priority, action and key roles relevant to AIP CRA goals, are described in the Table 3. Table 3: AIP CRA Stakeholders AIP Stakeholder Action Role Priority 1 Office of Agricultural Drive agriculture policy from Provider, Economics; OAE climate change impact User 2 Rice Department Increase farmers potential and Provider, rice technology, develop rice User varieties, develop agriculture organization 3 Office of Natural Set up climate change impact Provider, Resources and policy, coordinate between User Environmental Policy organization, set up countries and Planning; ONEP agreement, reduce greenhouse gas, set up budget, monitor the climate Department of Support and give advice on Provider, 4 Environmental Quality rice cultivation User Promotion 5 Department of Crop production information Provider, Agriculture User 6 Thai Meteorological Meteorological information for Provider Department agriculture, food security, disaster reduction, met information related to health and environment 7 AIP for Climate-Resilient Agriculture

AIP Stakeholder Action Role Priority 7 Center of Regional Climate change methods / Provider Climate Change and models Renewable Energy (RU-CORE), Ramkhamhaeng University 8 Thailand Development Advisor and public policy Provider Research Institute research Foundation; TDRI Three working group workshops have been organized by GISTDA to engage key Stakeholders in identification of problem statements, desirable goals and relevant indicators reflecting the past and current situations and plausible future changes. We will take benefit of the results from the workshops and consider them for the development of AIP CRA. 4. AIP Conceptual Design 4.1 Scenario implementation (Indicator Instantiation) - AIP Cycle Figure 1 presents a general cycle of AIP design which comprises 11 use cases (UCs). The cycle starts from problem statements and user requirements. The cycle may repeat starting from indicator development, which is a stage to define a quantitative impact or condition, to action plan evaluation, which uses said indicators to evaluate the suitable action. 8 AIP for Climate-Resilient Agriculture

Figure 1: AIP cycle based on 11 identified use cases. The 11 identified use cases are listed as follows: UC1: Problem Statement ● OAE and Rice Department are aware that rice production, yield, and area, is negatively affected by flood and drought which are impacted by climate change. ● OAE and Rice Department are aware that under climate change, the rice yield may decrease, while the suitable rice area may differ compared to existing. UC2: Indicator Development ● Indicators shall assess the current situation of damages area by drought, by flood, and rice production/yield. ● Once assessed on the current situation, indicators have to be assessed for the future situation (trend situation). ● The AIP CRA team designed a measure that can qualitatively reflect the success of selected action plans to adapt to climate change. UC3: Indicator Instantiation ● AIP CRA team prepares the input data which include historical and projected data, such as, geophysical data, climatic data, hydrological data, and crop data. ● API CRA team uses input data to instantiate measures/indicators in the models, such as crop model, etc. UC4: Visualize Target Area ● AIP CRA team visualizes both historical and projected flood/drought impact on rice areas, rice yield, and rice production under future scenarios. UC5: Issue identification, policy Action Plan and its target indicator creation by Policy Maker 9 AIP for Climate-Resilient Agriculture

● End users visualize the projection of flood/drought impact on rice areas, rice yield, and rice production under future scenarios without any action plans. ● End users create action plans to react. UC6: Policy action plan integration and scenario creation ● AIP CRA team takes the action plan from end users, then instantiates in the models to simulate the result of the selected action plan. UC7: Decide on Policy action plan (LAUNCH POLICY) ● End users visualize action plan results and use the results to select which action plans to be launched. UC8: Freeze Scenario ● End users agree on the selected action plans and freeze to compare with the historical and scenario without any action plan. UC9: Update Dashboard ● Once there is any input data change, the AIP CRA team updates the data in UC3 to instantiate and evaluate the results. UC10: Follow up and Evaluate policy. ● The result of update data on AIP dashboard UC11: Adapt Policy ● End users see the result on AIP and make a decision whether the policy needs to be changed or not (if there is any adaptation, move back to UC5). It should be noted that this document addresses the early stages of the AIP cycle which includes Problem Statement (UC1) to Visualize Information (UC4). 4.2 AIP CRA Conceptual Elements Figure 2: The constitutive elements of AIP CRA concept 10 AIP for Climate-Resilient Agriculture

The constitutive elements of AIP CRA presented in Figure 2, are as follows: ➢ Managing through policy implementation is necessary to address societal challenges. Adaptation policies for rice production to climate change are chosen for this AIP to ensure at least the maintenance of rice production. ➢ The impact of the AIP is the overall effect that is used to estimate and compare historical and projected conditions caused by environmental and political changes. Presented via the interactive dashboard, end users can use the impact of the AIP, such as rice production, to assess selected actions under future scenarios. ➢ Specific impact provides the estimate of effect in specific areas that underlies the overall impact. For the AIP CRA, three specific areas are chosen, namely impact on rice yield, impact on rice area of floods and impact on rice area of drought. ➢ The AIP indicators are calculated from several input data and representative of the key factors that influence the specific impact. For example, the duration and intensity of drought in rice-growing areas are needed to estimate the specific impact of drought. ➢ Data are input to estimate AIP indicators and can be obtained from observations, surveys, experiments or calculations. The example of drought duration data can include rainfall, temperature, evapotranspiration, ➢ The 5M concept describes five mechanisms including Measuring, Mapping, Monitoring, Modeling, and Managing. 4.2.1. Measuring/Mapping/Monitoring/Modeling To quantify the impacts of climate change on rice production involves measuring, mapping, monitoring, and modeling the impacts on rice yield and rice cultivation area, with special focus on the impacts of flood and drought. The input data are multi-source data characterizing the ecosystem under study (e.g. climate, geophysics, hydrology, vegetation, human impacts, etc.). Data are measured from sources ranging from ground surveys, weather stations, official statistics to satellite earth observations. They are static (soil type, topography, etc.) or from dynamic monitoring (weather, crop growth tc.), and geo-spatialized for mapping operations. Using historical data, the link between measurements/indicators and impacts on rice areas and rice yields is analyzed and modeled. Two categories of models will be considered: statistical models and physical -based or process-based models. Statistical models (eg multiple linear regression, neural networks, machine learning, etc.) are less complex to implement, but require data covering a wide range of observation conditions. Process-based models (e.g. crop models) or physical models (e.g. hydrological models) require detailed data describing the processes (e.g. plant growth parameters characterizing specific plant species), but as they include implicit relationships between model parameters, they require less observational data. The models developed for determining the main drivers of the impacts will use the full set of input data. Whereas the models which will be used for future projections will make use of geophysical and climate data that would be available for the future, such as projected climate data. 11 AIP for Climate-Resilient Agriculture

4.2.2 Managing To mitigate the impact of climate change, management options are needed and can be executed through the implementation of policies. The two main approaches to combating climate change are mitigation and adaptation. Mitigation focuses on reducing greenhouse gas (GHG) emissions, while adaptation focuses on preparing for and adjusting to the effects of climate change. While mitigation is crucial to addressing the root causes of climate change, the impacts of climate change are unlikely to be avoided any time soon. Adaptation policies are therefore urgently needed for agriculture, and in this study, for rainfed rice cultivation. Various adaptation options can be considered, including water management, which requires the construction of infrastructure (e.g. dykes, reservoirs, etc.), modification of crop varieties (for drought and flood resistant varieties) and adjustment of cultural practices. In the cropping practices option, modifying the cropping calendar and crop zoning are nature-based adaptation actions requiring low monetary investment. Crop calendar addresses the temporal aspect of adaptation for optimal planting time, while crop zoning provides spatial adaptation for suitable planting locations. In future climate change scenarios, the impact on rice production without any management action will be projected first. The modification of the crop calendar, crop zoning, or the combination of the two, suggested by the stakeholders, will be integrated into the climate change scenarios, so that the chosen action can be evaluated. Multiple trials with a combination of scenarios and climate change actions can be performed. Action that can ensure that rice production increases or, at least, remains unchanged can help end-users formulate adaptation policies. 12 AIP for Climate-Resilient Agriculture

5. AIP CRA Design The design depicted below was created by GISTDA with support from Airbus experts during GISTDA workshops, as part of THEOS-2 WP 22400 AIP Support for New Area Development, to reflect the need to develop a user-oriented tool on the impacts of climate change on rice production. To demonstrate the project concept, the working group proposed to use Nakhon Ratchasima province in the northeastern part of Thailand as the study site. This region is dominated by a rainfed lowland ecosystem, which experiences both drought and flooding. As agreed with stakeholders, in this AIP CRA development, we will only consider the impacts on rice production of climate change, not changes in human activities such as crop and water management, and other socio- economic factors. 5.1 Climate-resilient Agriculture AIP 5.1.1 Main climatic change patterns for Thailand and the study region Since the mid-20th century, observations show increase of annual temperature and precipitation across Thailand. Most of this increase occurs during the wet season. Observations at meteorological stations show significant increases between 1970–2006 in daily maximum, mean and minimum temperatures (0.12–0.59°C, 0.10–0.40°C and 0.11–0.55°C per decade, respectively). Under the RCP8.5 emissions pathway, average temperatures are projected to increase by 3.8°C by the 2080s. Table 4 provides information on temperature projections and anomalies for the four RCPs over two distinct time horizons; presented against the reference period of 1986–2005. Table 4: Projections of average temperature change (°C) in Thailand for different seasons (3-monthly time slices) over different time horizons and emissions pathways, showing the median estimates and the 10th and 90th percentiles in bracket (Limsakul, 2011). Under all emissions pathways, the likelihood of experiencing a heat wave (a period of extreme abnormally hot weather, defined with reference to a relative temperature threshold) increases considerably, up to 31% under the RCP8.5 pathway, by 2080– 2099. Over the 20th century, studies (Lacombe, 2012) observe an increase in annual precipitation, with an increase in precipitation during the wet season contributing most to this increase. which could explain the increase in flooding events. Variability of 13 AIP for Climate-Resilient Agriculture

precipitation in Thailand was driven particularly by El Niño Southern Oscillation, with years of strong El Niño correlated with moderate and severe drought. Projections of local long-term future precipitation trends, despite uncertainties, indicate evident global trends such as the increase with temperature of intensity of sub-daily extreme rainfall events. The average daily rainfall is predicted in some studies (Westra, 2014) to increase (e.g. by 0.24-0.73 mm per day), whereas number of rain days decreases (e.g. 1.3 to 5.9 days per decade). Consequently, the projected less frequent, but intensified precipitation events would increase the probabilities to have drought and flood during wet season, which would significantly affect the Thailand rainfed rice cultivation. 5.1.2 Effects of climate change on rice cultivation To understand the impact of climate change on rice production, it is important to know how changes in temperature, rainfall patterns and resulting floods and droughts will affect rice growth, rice development and rice productivity. Increased atmospheric temperature can adversely affect crop yield by affecting its phenology, physiology and yield components. The sensitivity of rice to high temperatures varies according to growth phase, day/night temperature and genotype. Although the productivity of crops becomes high with rising temperature, it declines due to the heat stress when the temperature exceeds the optimal range. In rice production, the occurrence of sterile spikelets caused by high temperatures during flowering is a constraint factor of yield (Oh-e, 2007) (Matiu, 2017). Spikelet sterility is greatly increased at temperatures above 35°C and may be aggravated by increased levels of CO2, possibly due to reduced transpirational cooling. It was found that the elevated concentration of CO2 accelerates development and shortens the total growth time of rice. The increase in growth response with increasing CO2 concentration was attributed to greater tillering and a greater number of grain- bearing panicles. Net assimilation rate and net canopy photosynthesis also increased with increasing CO2 concentration. However, this tendency tends to be compensated when the temperature increases in parallel with the increase in CO2. Increased CO2 levels can also lead to direct inhibition of maintenance respiration at nighttime temperatures above 21°C (Krishnan, 2007). In the region studied, flooding can occur due to heavy rains and/or overflowing rivers and streams for a period of a few weeks and lead to the complete submersion of plants. Depending on climatic conditions, such floods can occur several times during a rainfed rice growing season, damaging the rice crop. Although rice plants need a lot of water during growth, the stress of flooding can lead to severe crop losses (Thakur, 2011). Floods cause many complex abiotic stresses, and the amount of damage that can be done to flooded plants varies depending on the duration of the flood and the characteristics of the flood water such as temperature, turbidity, depth water, oxygen, carbon dioxide concentration and light intensity. These affect important plant processes like chlorophyll retention, underwater photosynthesis, carbohydrate accumulation, elongation and survival. In particular, if a limited amount of sunlight reaches the leaves, the potentiality of plants for photosynthesis decreases. Light intensity is also very important to maintain O2 and CO2 concentrations, and therefore greatly affects the 14 AIP for Climate-Resilient Agriculture

physiological state of rice plants underwater. Temperature is another important factor that plays a vital role in the survival of rice plants under floods. The high temperature (30°C) increases plant mortality and decreases the solubility of O2 and CO2 in flood waters, while accelerating anaerobic respiration, resulting in plant starvation and death in a short time. Drought is a term commonly defined as the insufficiency of water availability, including a period without significant rainfall which affects soil moisture storage capacity and crop growth. Drought has been recognized as the major constraint to rainfed rice production (Moonmoon, 2017). Northeastern Thailand, for example, experiences periodic droughts during the rainy season, and agriculture is the main sector affected. Seasonal rainfall is bimodal, generally beginning in May and ending around mid-October, but is highly variable. Drought can develop at any time during the growing season. Early season drought is occurring in most areas, affecting the rice early growth phase. Late-season drought develops most years at the end of the rainy season, before crops mature. Drought affects plants at different levels and stages of their life cycle. This abiotic stress not only results in reduced water content, but also affects stomatal closure, limits gas exchange, reduces transpiration, and disrupts photosynthesis. Severe water stress can lead to the cessation of photosynthesis, disruption of metabolism and ultimately the death of the plant, especially during the rice vegetative phase (from sowing to panicle initiation), and reproductive phase (from panicle initiation to flowering). In summary, the following climate data and related indicators are needed to analyse the impacts of climate change on rice cultivation in the study region: daily temperature (maximum, minimum, day/night temperature), daily precipitation, with focus on consecutive days without rainfall, and reversely, days of intense rainfall, flood extent, flood depth (or by default plant submergence) and flood duration, drought indicators (integrating both precipitation and evapotranspiration); and CO2 atmospheric concentration. In terms of rice cultivation, the following geospatial data are necessary in the impact analysis: identification of rice crop (vs non rice), rice phenology (or by default rice Start-of-Season (sowing date), and rice flowering period), rice varieties (e.g. Khao Dok Mali 105 (KDML105) and Rice Department 6 (RD6), the two major varieties of Jasmine rice grown in the study region) which have different growth cycle duration. 5.2 AIP Methodology To quantify the possible impacts of different climate scenarios on rice production, it is necessary to understand the responses of various eco-physiological processes to a combination of environmental conditions, such as temperature, CO2, water - including flooding and drought - and also agronomic management. The sensitivity of rice productivity (yield) to climate variables is to be investigated with the aim to develop models to be used to simulate rice productivity in future climate scenarios. Considerable efforts have been made to understand rice production in a changing climate. Several of these efforts have been made using process-based crop models. These models are essential for studying the integrated effects of various chemical, physical and biological processes. For example, such a model can be used to explore 15 AIP for Climate-Resilient Agriculture

how temperature increases associated with high CO2 levels and lack of water influence crop production responses. Effective crop growth models, calibrated and validated in the study region, can then be used as tools to quantify the possible impacts of different climate scenarios. However, the models are used to predict the crop yield, but not the damage of the crop leading to complete loss of harvest due to adverse conditions of flood or drought. The other limitation of these models, when applied for regional assessment is the difficulty of taking into account the diversity of cultural and environmental conditions in the region. With the emergence of remote sensing, geospatial data are available to account for the spatial and temporal diversity of conditions in a region. Based on historical remote sensing observations and in situ data, statistical approaches are being developed to assess the relationships between climate data and rice production at different scales (e.g. district, province, region, ..). The idea is to use these statistical models to simulate future rice production with projected climate data. The major limitation of this approach is the quantity and quality of historical data to be used to build the statistical models. In AIP-CRA, we investigate both statistical and modelling approaches to estimate rice production in changing climate conditions. The work will be conducted at district level in the study region. Figure 4 provides the flowchart of the methodology conceptual design of the AIP CRA. In Figure 4, statistical models are trained and process-based models are calibrated using historical data. Using the models, the main drivers of the rice yield and of the loss of harvest can be identified, and the relationships between past climate variables and rice production (yield and harvest area) can be assessed. For the future projections, the models will be trained using only input data that would be available in the future. Finally, the retained models will be used to simulate yield and harvest area under future climate scenarios. For action plan, stakeholders can select various options of adaptation to minimize the future impacts on rice production. The two options under consideration are changing crop calendar and changing crop zoning. Figure 4: The methodology conceptual design flowchart of the AIP CRA, related to the assessment of current climate change impacts on rice production using historical data. 16 AIP for Climate-Resilient Agriculture

5.2.1 Input Data The data to be used in the study include geophysical data, climate data, hydrological data, and crop data ; the latter comprises historical recorded damaged area and harvest area (reduced from planting area). Table 5 provides the list of existing data that can be used for the study in this region. The data are from different sources, geographical data, national statistical data, agricultural survey, damage declarations, etc.. Several other data are derived from satellite observations, at different spatial and temporal resolutions. Table 5: AIP CRA Stakeholders 17 AIP for Climate-Resilient Agriculture

In order to reduce the number of variables involving in the analysis, various indices and indicators have been derived from the original input data. This applies particularly to the studies of drought and flood impacts, to localize the impacted areas and to analyze the affected yield. During the first phase of the project, the relevant data sets will be collected, processed to be ready-for-analysis, and harmonized in terms of spatial and temporal resolutions. The quality assessment of collected data sets will be done in order to define the optimal modelling approach to be used. 5.2.2 Modelling approaches Statistical models have been used to identify the relationship between past climate variables and yield data, as well as the influence of other parameters (geophysical data, crop data..) on these relationships. Regression techniques such as multiple linear regression (MLR), Gaussian process regression (GPR), and machine learning techniques, such as artificial neural networks (ANNs), support vector machines (SVMs) are some of the techniques used in the literature. The performance of the methods to analyze the main drivers of the yields and the relationships between climatic data and the rice production depends strongly on the quantity, quality and the relevance of input data. In this study, different techniques can be tested, after a first assessment of the input data. For statistical approach to assess the drought impact, the Drought indices (Zargar, 2011), for example the Standardized Precipitation Evapo-transpiration Index (SPEI), will be used. By evaluating historical drought indices over rice cultivation areas at different rice phenological stages, the responses of rice to drought can be assessed. Utilization of historical data on declared drought damage together with drought indices calculated from historical weather recordings can help establish a drought indices threshold with an implication to the loss in rice cultivation. Consequently, the minimum drought duration and intensity that influence rice production can be defined which can be used as historical drought baseline. Climate variables from climate change scenarios can be used to calculate future drought indices, drought duration, and drought intensity to evaluate their future impacts on rice production. To assess the flood impact, statistical analysis considers various factors, including historical climate data, flood data, impacted rice areas, topography, land use, soil type, crop calendar, and other relevant data and indicators. Relevant and high quality data sets can then be used to develop statistical models to link the precipitation pattern with flood indicators such as extent, flood depth and duration in a given region. Similarly to drought assessment, the thresholds of a combination of flood indicators which implies the complete damage of rice crop could be determined. Process-based crop models are essential for investigating the integrated effects of various chemical, physical, and biological processes. For example, a validated system model can be employed to explore how temperature increases associated with elevated CO2 levels influence the responses of crop production. Effective crop growth models, calibrated and validated at the study region, can then be used as tools to quantify possible impacts of different climate scenarios. 18 AIP for Climate-Resilient Agriculture

Rice growth models are physiologically based and predict day-to-day canopy photosynthetic process, respiration process, development, biomass partition, and crop growth as a function of input information. The latter comprises everyday-based weather data, soil characteristics, management practices, as well as genotype features (Radanielson, 2018). Rice growth simulation models, for example, ORYZA2000 (Bouman, 2001), DSSAT CERES-Rice (Timsina, 2006), AquaCrop (Amiri, 2014), can concomitantly integrate nonlinear relations among soil, water, rice plant, weather data, and crop management practices for the determination of yield, and environmental stresses (Bouman and Van Laar 2006; Maki et al. 2017). The three above-mentioned models can reach various simulation results depending on the simulation objectives, among the following: study of the best crop management practices, breeding and crop improvement, yield forecasting and gap analysis, and impact of climate change. For example AquaCrop from FAO that is a water driven model particularly useful in irrigation studies, and DSSAT-CERES models are particularly important when applied to various crop types in a region, including rice. In this study, we will use the most adapted and widely used model for rice, which is the ORYZA 2000 model, first developed in 1994 and has been refined over several decades since then. This process-based crop model has advantages in its capacity to capture the complexity of rice development and growth mechanisms and simulate climate impacts on rice yields. The model can be used in combination with statistical analysis to explore the causal relationship between long-term El Niño-Southern Oscillation (ENSO) changes and rice yields, or to identify climate ceiling for rice production. However, wider applications of this model have been constrained by the availability of high- quality data sets used in the calibration and validation (Intergovernmental Panel on Climate Change, 2014). This explains that in this study, our effort will be made in the generation of the relevant input data sets to be used. Flood inundation models: Numerical models have been used to relate rainfall data and river flow in river basin to flood occurrence. The most used models available for flood inundation assessment are the hydrologic model—Soil and Water Assessment Tool (SWAT (Gassman, 2014)) and hydrodynamic model—Hydrologic Engineering Center River’s Analysis System (HEC-RAS (Hicks, 2005)). The SWAT is currently one of the most frequently used large-scale hydrological models for investigating rainfall-runoff relationships at regional scales. Among the hydrodynamic models, the HEC-RAS 1D- 2D coupled model is recommended for flood inundation modeling. The input data include the DEM (Digital Elevation Model), the river cross section, discharge, and daily water level data. The limitation of models for this study is the difficulties to have high resolution and high quality data sets to take into account the heterogeneous environmental characteristics of the region under study. It can be anticipated that the use of these models will not be considered in the first phase of the project. 5.2.3 Assessment of rice production under climate change scenarios Representative Concentration Pathways (RCPs) and the Shared Socioeconomic Pathways (SSPs) are the scenarios used for climate change projections. RCPs cover different trajectories of time-dependent projections of atmospheric greenhouse gas (GHG) concentrations which affect the climate radiative forcing and the consequent warming of the planet. SSPs provide qualitative narratives and quantification of key socioeconomic variables which affect mitigation and adaptation to climate change. 19 AIP for Climate-Resilient Agriculture

Various RCPs and SSPs are available, but the two RCP-SSP combinations will be used in this AIP CRA, namely RCP45-SSP2 and RCP85-SSP5. The selected climate change scenarios are employed to assess the impacts of the shift in climate variables on rice production in two aspects. Firstly, the direct effects of changing climate variables (patterns of temperature, precipitation..) on rice yield are evaluated, using statistical models or process-based models developed in 5.2.2 . Secondly, the indirect effects of floods and droughts on the suitability of the land for rice cultivation are assessed using statistical analysis results on the conditions for which the crop is frequently damaged. Using projected yield and suitable rice planting areas, changes in rice production under climate change can be anticipated. 5.2.4 Action plan evaluations Action plans are crucial in determining the suitable policy that will contribute to the overall goal. For AIP CRA, the goal is for rice cultivation to adapt to future climate which can be assessed through minimizing the reduction in rice production. Various options of adaptation include rice breeding (for flood and drought resistant varieties), water management (building reservoirs, ponds, trenches to store water). In this study, we focus on the crop calendar and crop zoning. Since the impacts of temperature, precipitation, flood and drought depend significantly on the growth stage of the rice plant, the first option is to simulate changing crop calendar to avoid or minimize the negative impacts. The other option is to change the geographical location for rice crop cultivation for an optimal crop zoning. End users can propose different crop calendars, crop zonings, or the combination of both. The proposed action plans can be evaluated on AIP CRA (Cf. Figure 5). Figure 5: The methodology conceptual design flowchart of the AIP CRA, related to the assessment of an action plan (changing crop calendar in the example). 20 AIP for Climate-Resilient Agriculture

6. AIP CRA dashboard features 6.1 Dashboard tabs The dashboard is an online application that will present the use case which provides 3 tabs of issue, action plan and report. Figure 5: The AIP for Climate-resilient Agriculture dashboard tab • Issues: This tab provides information on the current situation of drought and flood impact on rice cultivated area, yield, and production (annual and monthly). • Action Plan: This tab presents the result of future drought and flood impact on rice cultivated area, yield, and production based on climate change scenarios with and without selected action plans. For example, end users can select the time frame of 2030-2040, RCP8.5 climate change scenario, and action plan as crop calendar. The dashboard will visualize the map, graph, and numbers for rice cultivation area, yield and production that follows the scenario that the end user selects. • Report: This tab is a summary of a dashboard. It allows executives to understand the overall situation of rice production under climate change scenarios both with and without selected action plans. 6.2 Dashboard features 6.2.1 Issues Tab This tab presents the issues in Nakhon Ratchasima province using historical and current inputs which reflects the baseline. Three main panels are presented on this tab. On the first panel, the specific district (Amphoe) and sub-district (Tambon) can be selected by end users. The middle panel shows the map and graph of the rice area (in rai) affected by flood or drought. The rice production in tons, the harvested area after flood/drought in rai, and the rice yield in kg/rai will show on the third panel of the map 21 AIP for Climate-Resilient Agriculture

Figure 6: The AIP for Climate-resilient Agriculture dashboard for issue tab 6.2.2 Action Plan Tab This tab provides the comparison of flood and drought impact on rice cultivation in a specific location based on current, future prediction year (without Action Plan), and future prediction (with Action Plan). The example of the Action Plan tab is shown in Figure 7. In the first panel (left-side), the end users can select the time frame between 2030 to 2040, climate change scenario as RCP8.5, and the action plan as Crop Calendar1. The map and graph of the rice harvested area in Rai, which are shown in the middle panel, will present the 3 results of the current, the prediction year (without action plan) and the production year (with action plan). The last panel (right-side) will show the rice production (in tons), the harvested area after flood/drought (in rai), and the rice yield (in kg/rai) in the future under the selected climate change scenario both with and without action plan. Figure 7: The AIP for Climate-resilient Agriculture dashboard for action plan tab 6.2.3 Report Tab The objective of the report tab is to provide an executive summary based on the Action Plan tab. The report will include the specific location and the scenarios that end users selected which are Amphoe, Tambon, Time frame, Climate Change Scenario, and Action Plan. The report also describes the results which includes the harvest area after flood/drought (in rai) and rice production (in tons). Lastly, the user can download the summary report as a pdf file. 22 AIP for Climate-Resilient Agriculture

Figure 8: The AIP for Climate-resilient Agriculture dashboard for report tab 23 AIP for Climate-Resilient Agriculture

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