Cooperating with Inner Mongolia Agricultural University, the project implemented "smart grassland management” on 200 hectares (3000 mu) of grassland in Helinge’er county, in combination with vegetation growth monitoring and use of meteorological data to determine the right time to start spring grazing. Herder were able to dynamically determine grazing time and intensity, as well as tailor the grazing plan with balanced grass and livestock. After 3 years of pilot work, the project has spearheaded the model of "grazing in warm seasons and feeding in cold seasons", suitable for the local area and other sites with similar conditions in the grassland of northern China.
The project helped the local farmers cope better with the accelerating water shortage, exacerbated by a changing climate. The farmers were embracing, the integrated technologies and practices of high-yield dry-farming, ecological dry-farming and soil testing formula fertilization, selected drought-resistant crop varieties, enhanced film mulching, and innovative irrigation to make full use natural precipitation. The approach—combining accessible data tools and new land management practices—has led to multiple benefits of water and fertilizer efficiency, and increased production and income.
- Collaboration with Inner Mongolia Agricultural University and local communities enabled our approaches grounded to the local needs and conditions.
- Wide use of smartphones in the rural area make the Smart Grasslands app easily accessible.
- Active engagement with the supportive farmers who then play the role of ambassadors to champion the method.
We were able to develop a close collaboration with the local communities by taking time to understand what challenges they were experiencing with existing techniques for farming and herding. We targeted community members who expressed dissatisfaction with the status quo and who hoped to change the production methods. Through this collaboration, and by explicitly valuing the local community’s traditional knowledge, our new scientific sustainable management methods were more suitable to the area and more likely to be adopted at scale. For example: detecting the feeding time (cold seasons) which suits their traditional practice, selecting drought-resistant crop varieties by learning what crops were no longer planted because of water shortage.