The old growth tea forests of Jingmai ( pronounced 'jing my' ) are nothing short of idyllic. These ancient forests border Burma and hide smuggler trade routes still in use today. The biodiversity of the region and the nearly untainted old world methods and rituals of tea cultivation and harvesting make this area unique among world tea production. Large tea trees planted before Columbus discovered the Americas are commonplace. This blend is single region pure Jingmai big tree material. In Jingmai they refer to trees in terms of size rather than age. They have 3 main sizes of trees that fall into their big tree category. This is a blend of all three. We named this tea Midas Touch because of the processing skill used to create this tea has turned the leaf into gold. This tea is strong despite the young age. The aroma is thick with floral notes and hints of honey sweetness. The thick body produces saliva with some astringency and minimal bitterness. The flavor is smooth and slightly vegetal. This tea brews both gentle and strong at the same time. It is very energetic with a long aftertaste. It can surprise you. Stick this on a shelf for 10 years and you will have a truly amazing tea! Prefecture: Simao Elevation: 1300+ m Wood Fired Hand Rolled Indoor Sun Dried Hand Blended "Crimson Lotus Tea’s 2016 Midas Touch is a delicate, bright, and sunny tea with features you will miss if you drink it too fast."--Oolong Owl ( reviewing the 2016 tea ) "This puer blend is fantastic and intoxicating, along with beautifully highlighting the greater attributes of the Jingmai region, such as its characteristics and quality, in such a positive light. ... King Midas was resurrected in my tea cup, and ended up turning my tastebuds to gold…"--The Oolong Drunk ( reviewing the 2016 tea ) Video Tour of Jingmai Brewing Instructions Use 5-10 grams of leaves and brew with 75-150ml ( 2.5-5oz ) of water at or near boiling. Rinse once for a few seconds. Start with quick steeps under 10s. With each re-steep adjust the steep time to your taste.
Les recommandations reposent sur les notes, le style et le comportement de la communauté. Elles peuvent évoluer à mesure que les données s'enrichissent.
