## 3 Method
### Defining Future Land-Use Scenarios  
A \"scenario\" is defined as plausible future in the context of uncertainties, particularly when expected outcomes are highly contingent on indirect and direct effects [41]. There are several methods available to construct scenarios within a decision-making context. These include exploratory, target seeking, ex-ante and ex-post assessment and prior policy screening scenarios. Within the Nan context, a consultation workshop comprising 30 stakeholders from relevant agencies (local and national levels), interested individuals and groups, was conducted in April 2018 to share ideas  
Figure 1: Location of Nan Province and protected areas, Thailand. and information from their collective knowledge. The workshop participants proposed three future land-use scenarios in 2030, called (1) trend, (2) market-oriented, and (3) conservation (Figure 2). These scenarios accommodate and represent various socio-economic drivers, historical and future economic development in Nan Province and ambitious national policies on forest protection and biodiversity conservation. Each scenario prioritizes demand for different land-use types, which are aggregated at the Province level. The trend scenario was based on a continuation of the land-use change of recent years (2009-2016 [41; 42]). The Markov Chain Model [42] predicted that maize area will increase from 17.5% of the province's area at present to 23.1% in 2030, while natural forests will decrease from 61% in 2016 to 49.3% in 2030. If forest plantation is added, it will reach 51.4% (Figure 2). Perennial trees (mainly rubber plantations) will increase by two-fold from the current level under the assumption of high rubber prices. This scenario also assumed poor forest protection and enforcement, meaning further encroachment inside and outside of protected areas similar to previous trends. The market-oriented scenario was constructed using target seeking. The Nan Development Plan (2018-2021) aims to achieve a lower rate of land transformation compared to the trend scenario [36]. Furthermore, it targets increased commodity production by 5% per annum from the baseline (2016) to reduce reliance on imported agricultural products from other provinces. With this ambitious target and the assumption of constant agricultural land productivity, the model predicts that forest cover would be less than 30% of the province's area in 2030 (see equation below). \[\text{F}_{\text{n}}\text{=F}_{\text{0}}-\left(\sum_{\text{i}=1}^{\text{i}} \text{L}_{\text{i0}}[(1+\text{r})^{\text{n}}\text{-}1]\right) \tag{1}\]  
where  
\(\text{F}_{\text{0}}\): Forest area at baseline  
\(F_{\text{n}}\): Forest area in year n  
i: Index of crop  
\(L_{\text{i0}}\): Agricultural area of crop i at baseline  
r: annual economic growth ratio  
Considering constraints on agricultural expansion, the increment rate of 2% per annum was recommended by the workshop participants, resulting in 57% of the province under forest cover in  
Figure 2: Land-use scenarios in 2030 in Nan Province. 2030. This scenario assumes 50% of the recent rubber plantations (2014-2016) will be converted to other economic crops, especially maize, and decreased rubber prices. Maize plantation will increase at a rate of 3% annually, half of the recent rate. Increased human settlements (3% annually) and water body class (2% annually) are also expected as the result of tourism trends in the city. Degraded forest will be rehabilitated through agro-forestry and multi-layer cropping practices. The conservation scenario aims to strictly protect biodiversity in protected areas and maintain watershed services. As mentioned previously, Nan protected areas, cover about 35% of the province's area, while the watershed class (WSC) 1 accounts for about 46%. The total of these two categories, excluding overlapping areas, is approximately 70% of the province's area.