| Sumario: | Methodological advances for use with large-n datasets hold the promise of transforming the ways that agricultural landscapes are described, understood, and managed. Nevertheless, most countries lack comprehensive characterization data for crop production systems and this constrains the application of emerging analytical methods. New datasets are required that are routinely collected, representative, and topically robust through surveys that are efficiently deployed at scale, especially in smallholder-dominated systems where heterogeneity is common. Here we present results from a collaboration between the Indian Council for Agricultural Research (ICAR) and the Cereal Systems Initiative for South Asia (CSISA) to address agricultural data gaps in India. With more than 39,000 fields surveyed, novel insights have been developed for closing yield gaps, reducing greenhouse gas emissions, and targeting solutions through predictive analytics. To capitalize on investments in data, methodological and institutional changes must be combined so that learning from landscapes empowers innovation and sustainability transition
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