Soybean Management for Seed Composition: The Perspective of U.S. Farmers

The soybean [Glycine max (L.) Merr.] compositional quality is mainly provided by the seed concentration of protein and oil. These traits are critical for sustaining global use, and although there is demand for high protein soybean, no mechanism to differentiate production is in place. At the opposite end of the supply chain, farmers are remunerated on a mass basis without having any incentive regarding seed composition. This study evaluated farmers\u27 perspectives and knowledge on soybean quality and their propensity to adopt quality improvement technologies. Farmers from the main U.S. producing regions (n = 271) were investigated with a self-administrated survey containing 21 questions during 2020 and 2021. Our results show that 84% are unaware of the current protein and oil levels from their own production. A small portion (1.4%) make management decisions (e.g., choice of genotypes or monitor quality) based on the implications on seed quality. However, practices already in place are likely to enhance the quality of seed, namely N nutrition (via rhizobia [12.9%] or fertilizer [5.9%]) and late-season crop protection (17.1%). If farmers are financially rewarded by US$0.50 per bushel, a mindset change may occur. Based on these results, we concluded that shifts in the U.S. production system targeting protein or oil markets are possible, and the constraints are mainly related to on-farm management. However, the challenges for improving the U.S. soybean competitiveness in global or niche markets also rely upon other segments of the production chain, specifically breeders, technology suppliers, and logistical structure

A Research Road Map for Responsible Use of Agricultural Nitrogen

Nitrogen (N) is an essential but generally limiting nutrient for biological systems. Development of the Haber-Bosch industrial process for ammonia synthesis helped to relieve N limitation of agricultural production, fueling the Green Revolution and reducing hunger. However, the massive use of industrial N fertilizer has doubled the N moving through the global N cycle with dramatic environmental consequences that threaten planetary health. Thus, there is an urgent need to reduce losses of reactive N from agriculture, while ensuring sufficient N inputs for food security. Here we review current knowledge related to N use efficiency (NUE) in agriculture and identify research opportunities in the areas of agronomy, plant breeding, biological N fixation (BNF), soil N cycling, and modeling to achieve responsible, sustainable use of N in agriculture. Amongst these opportunities, improved agricultural practices that synchronize crop N demand with soil N availability are low-hanging fruit. Crop breeding that targets root and shoot physiological processes will likely increase N uptake and utilization of soil N, while breeding for BNF effectiveness in legumes will enhance overall system NUE. Likewise, engineering of novel N-fixing symbioses in non-legumes could reduce the need for chemical fertilizers in agroecosystems but is a much longer-term goal. The use of simulation modeling to conceptualize the complex, interwoven processes that affect agroecosystem NUE, along with multi-objective optimization, will also accelerate NUE gains

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p