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Innovation and Data Driving Sustainability in US Cotton Industry

As pressures mount to make fashion more sustainable, brands, retailers, policymakers and industry leaders are all seeking assistance and assurances that global supply chains are using verified data and technology to improve the industry's footprint.

Agriculture is often viewed as a trade based on intuition, with production knowledge passed down from one generation to another. While those traditions still exist, producers know that today’s issues — such as a changing climate and limited natural resources — require more complex solutions, providing US commodity producers the opportunity to lead the way when it comes to technological innovation.

As pressures mount to make fashion more sustainable, brands and retailers — as well as policymakers and industry leaders — are seeking assistance and reassurances that global supply chains are using verified data and technology to improve environmental footprint.

US-grown cotton is one of the most technologically advanced industries in the world; and it has used the innovations at its disposal to become continuously more sustainable. For more than 35 years, US cotton growers have made huge strides to progress the sustainability of their farms. They have improved soil health, reducing loss and erosion by 37 percent per acre all while increasing soil carbon levels. Additionally, they have used 79 percent less water and 54 percent less energy, reduced greenhouse gas emissions by 40 percent and land use by 49 percent — all while increasing yield by approximately 42 percent.

A new generation of ag tech

As technology has improved, growers continue to utilize emerging innovations to grow cotton more efficiently. According to a study from Duke University and Cotton Incorporated, 51 percent of US cotton growers use GPS-enabled swath control to ensure they are not overlapping practices such as planting, fertilizer applications and crop-protection applications. Nearly 7 in 10 growers surveyed also use GPS auto-steering functions on their equipment, such as tractors and pickers.

Precision agriculture has a major role in this new era of innovation around farming and sustainable agriculture. These technologies gather farm-specific parameters including soil conditions, nutrients and water availability. Many US cotton growers use precision technology throughout the cotton season — with 63 percent reporting that they use GPS receivers, multi-spectral images and ground-based sensors to further improve their sustainability. Consulting real-time weather radar improves efficiency by allowing growers to avoid activities that would be affected by weather, such as the timing of applying nutrients and herbicides. There is also the potential to reduce GHG emissions from optimized nutrient management and water use efficiency.

Quantifiable, verifiable goals and measurement for key sustainability metrics of US cotton production

Growers need to be able to demonstrate their sustainable farming practices to brands and retailers who are facing increased pressure from their customers and policymakers to provide transparent sustainability metrics. This is where the U.S. Cotton Trust Protocol comes in: The Trust Protocol aims to be the catalyst to shift the market towards continued sustainability progress. The program underpins and verifies the US cotton industry’s sustainability progress through data collection and independent third-party verification, and helps growers easily measure their progress in six key sustainability metrics — land use, soil carbon, water management, soil loss, greenhouse gas emissions and energy efficiency — thus, enabling growers to make informed decisions from one year to the next.

There is no doubt that agriculture has come a long way in the last three decades. The Trust Protocol aims to drive new possibilities through collaboration and technological advancements to ensure that the US cotton industry can lead the world in sustainable farming practices and its growers can achieve their goal of a better harvest through data-informed decisions in the field.

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