TradeLanes works with exporters around the world, including many of the world's largest agricultural companies, to help them make their B2B global trade frictionless. We watch with interest as the U.S. and China continue to implement their trade agreement.
Yesterday, over 180 organizations from the U.S. Agricultural community came out in mass to sign on to a letter seeking support from the Trump Administration to ensure that U.S. interests are advanced in the implementation of the current trade agreement and future negotiations.
ProFarmer summarized the letter as the "U.S. ag sector writes President Trump to give China time to fulfill Phase 1 farm purchase commitments."
Quoted from the letter: "As you know, the U.S.-China Phase 1 Trade Agreement is critical to both the near- and longer-term success and growth of American agriculture — and the millions of American jobs the agricultural sector sustains..... While the current pace of U.S. agricultural exports to China is below the pace needed to meet the Phase 1 goals, American farmers, ranchers, and rural communities remain optimistic that the purchases under this agreement will accelerate and be fulfilled by China, and that as a result, the American agriculture sector will enjoy important market opportunities."
For those interested, we provide a copy of the letter to White House.
We continue to monitor the 2nd and 3rd effects that are resulting - and recommend following Lori Ann LaRocco of FreightWaves as she continues with her smart coverage and analysis of the state of the implementation of the U.S.-China trade deal.
Thanks to David Everett Strickler for allowing us to use his photo which we found on Unsplash
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TradeLanes Trade Delivery Platform is the new generation of global trade management software. TradeLanes brings global trade execution entirely online, automating and streamlining all aspects of an international sale - from issuance of sales contracts to product quality, inventory allocation, fulfillment, bookings, loading, shipment workflow and execution - and applying machine learning ensure that cargo is delivered on-time and at-margin.
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