Background: National School Lunch Program (NSLP) standards have improved school lunch dietary quality (DQ), however, further improvements could be made. Acceptability and feasibility of higher DQ are potential barriers. Thus, the purpose is to compare acceptability and feasibility of best practice (BPSL, optimizing DQ) with typical school lunches (TSL, meeting minimum NSLP standards) served separately and concurrently. Methods: Forty elementary school-aged participants were recruited for a randomized crossover trial. Participants attended three meal conditions (MC) choosing one of two meal types—MC1) BPSL1/BPSL2, MC2) TSL1/TSL2, MC3) BPSL/TSL. Acceptability included taste test surveys, weighted plate waste assessments, and hunger scales. Feasibility included meal cost, time, and skill and equipment requirements. Results: There were no significant differences in total taste test score, average total plate waste, or change in hunger (ps > 0.017) before or after adjusting for covariates. TSL was selected significantly more often in MC3 (TSL = 83.3%, BPSL = 16.7%, p = 0.001). Meal cost (p = 0.783) and skill and equipment requirements were not significantly different between meal types. BPSL required significantly longer preparation time (TSL = 60 ± 25 min, BPSL = 267 ± 101 min, p = 0.026). Conclusions: Results indicate few differences in acceptability and feasibility between BPSL and TSL. This study could inform decision and policy-makers seeking to improve school lunch DQ and acceptance of higher DQ meals.

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The growing number of participants in the grain market shows just how globalized this sector is in Brazil nowadays. It is difficult to note how mergers and acquisitions between local companies as major tradings and funds have been acquiring part or even the totality of Brazilian companies, like the soybean crushing company “XYZ” that acquired some assets such as crushing plants in the state of Mato Grosso, located on the center-west region of Brazil and access to 2 port terminals to export the byproducts. This work contains data from January of 2014 to September of 2017 including prices of soybean and by products, freight rates, basis at the ports and operational costs. This information comes from different sources including government and private agencies and Thomson Reuters Eikon software. This information was used to build a model in Microsoft Excel and by using the Risk Solver Platform, a powerful statistic calculator tool, to find a solution to improve “XYZ” profitability. The results of the model suggest changes in the origin of grains, reducing the volume in some locations and increasing the volume originated in other locations. After running 2 different scenarios, “XYZ” profits was increased by 1.24 million of US dollars.

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