Materials for Agriculture Resource Imaging Analytics at High Resolution (MARIAH)

Abiotic stresses such as drought, salinity, and extreme temperatures and biotic stresses like weeds insects and diseases currently reduce crop yields to less than 50% of their potential. Furthermore, climate change is projected to exacerbate these losses. Breeding for crop varieties with enhanced stress resistance is a useful technique to reduce these losses, but this process is hindered by phenotyping bottlenecks and limited knowledge of the genetic bases of different forms of stress resistance. Management practices such as irrigation, fertilization, cultivation, and pesticides also are important tools to compensate for plant stress, but they have significant economic and environmental costs. Moreover, the economic and environmental outcomes of growers’ cropping decisions are shaped by a complex network of interactions that are not yet understand well enough to predict. This network includes multifactorial interactions among:

1. The genetics (G) of the crop, its pests, and other organisms in the environment;
2. environmental conditions (E) such as temperature, rainfall, and soil type;
3. The growers’ management decisions (M) such as irrigation, fertilization, and pest management; and
4. socioeconomic factors (S) such as commodity prices, crop insurance, producer and consumer attitudes and values.

We propose that these interactions can be fully understood through an interdisciplinary systems approach that teams experts in plant science and agronomy with experts in engineering and the physical and social sciences. To address this need, we have developed a land-grant network of collaborators nationwide (MARIAH) that aims to advance research on G x E x M x S (GEMS) systems, and to translate research findings into improved tool and evidence-based crop management approaches.

Goals

• Develop decision support systems (DSS) for plant scientists to facilitate crop breeding, optimize management practices, and support research in sustainable agriculture
• Develop DSS for agricultural producers, to maximize agricultural productivity and profitability while minimizing environmental externalities.
• Train an interdisciplinary and inclusive workforce to deploy these DSS ethically and profitably
• Engage stakeholders to promote the adoption of DSS and to refine DSS based on stakeholder feedback