Goal: To evaluate the potential of strategies to promote green corridors of soybean exports from Brazil to China. Design / Methodology / Approach: The best transportation corridors are evaluated in terms of minimum transportation cost and minimum CO2 emissions in transport. This problem was modelled as a linear programming problem and was solved by GAMS. It involves transporting soybeans from the producing farms in the Brazilian Center-West to the main Brazilian export ports and, subsequently, to China, through the various logistical infrastructures available. Results: In general, strategies aimed to reduce CO2 emissions in the Brazilian soybean exports to China, and also promote the reduction of transport costs, as presented in the results of the different scenarios. Since the greatest impact on both indicators came from the use of Capesize ships, the study suggests that investments in port infrastructure to increase its vessel berthing capacity generate positive impacts, both in terms of lower transportation costs and CO2 emissions. Limitations of the investigation: The producing regions were divided in mesoregions. The road distance considered is between the centroid of the producing region to the destination. Practical implications: Especially in the scenarios of unrestricted transport infrastructure, the study suggests the configuration of optimum soybean flows, as well as the respective level of utilization of multimodal terminals. From the findings of the study, agents involved in the logistics of exporting soy to China can identify the investments in rail and port terminals necessary to a greener logistics. Originality / Value: This work will contribute to the literature in the Green Supply Chain Management field of study, especially in a Brazilian perspective.

All of Brazil's competitive advantages and productive gains tend to bleed away as its farm products pass through the various post-harvest stages, mainly due to logistic bottlenecks and deficiencies. To improve the prognosis for food security, one can avoid losses along the supply chain. This research is intended to adapt indicators and mitigation strategies derived through research in the USA to reduce post-harvest losses, especially in the transport of grains, and make this information available in Brazil. The implementation of a specific policy designed to prevent the post-harvest loss in the USA by either a public or private agent was not encountered during this research, which makes one think that this type of care is mostly driven by the market, economics, and perhaps the farmers' interest in protecting their harvested grains and taking pride in a job well done.

Reducing food losses presents an opportunity to enhance food security, minimize waste, and improve profitability within the production sector. Creating awareness among various stakeholders in the value chain about the significance of reducing postharvest losses is a fundamental step in this discussion. This article addresses the Postharvest Loss Information System (SIPPOC) development and applicability. SIPPOC encompasses tools designed to facilitate understanding food loss occurrences across different supply chain segments. The article provides insights into the tools incorporated within the information system and describes its historical background and protocol for database updates. In essence, SIPPOC enables the analysis of food loss throughout diverse logistical stages, thereby aiding in identifying critical points and implementing targeted actions for loss reduction. Drawing on SIPPOC data, the article further examines losses within the logistics chain by comparing potato, tomato, and mango agricultural productions.

Background: Brazil is one of the largest consumers of fertilizers and is highly dependent on the international market to meet its demand for agricultural production inputs. The complexity of the fertilizer supply chain motivated us to carry out this study on redesigning the fertilizer logistics chain and evaluate strategies for reducing logistics costs by redesigning the fertilizer mixing network in Brazil, a country that is heavily dependent on imported fertilizers for agriculture. Methods: We introduce a multi-product mixed-integer linear programming optimization model encompassing the logistics network, from import ports to mixing factories and agricultural fertilizer supply centers. This model includes logistics infrastructure and taxes, accounting for greenhouse gas emissions (specifically carbon dioxide) in fertilizer logistics. Results: The results indicate that expanding the port capacity for fertilizer importation can significantly reduce logistics costs and greenhouse gas emissions by up to 22.5%, decreasing by 23.9% compared to the baseline. We also observed that removing taxes on fertilizer importation can reduce logistics costs by approximately 11%, but it increases greenhouse gas emissions by 2.25% due to increased reliance on road transport. We identified 15 highly resilient regions for establishing mixing factories, evaluated various scenarios and determined the importance of these locations in optimizing the fertilizer supply network in the country. Moreover, the results suggest a significant potential to enhance the role of Brazil’s Northern Arc region in fertilizer import flows. Conclusions: Public policies and private initiatives could be directed toward encouraging the establishment of mixing factories in the identified regions and increasing transport capacity in the Northern Arc region. Improving the logistical conditions of the fertilizer network would contribute to food security by reducing the costs of essential inputs in food production and promoting sustainability by reducing greenhouse gas emissions.

This study aimed to identify the most profitable feeding endpoint for feedlot cattle based on each individual animal’s optimal economical endpoint (OEE) using the BeefTrader Decision Support System (DSS). The hypothesis was that the traditional slaughter endpoint (TSE, currently used in commercial feedlots) would have different profitability than the OEE which is determined when the marginal net profit (MNV = daily change in revenue – expense) becomes negative. Using Monte Carlo simulations, MNV curves for 280 Nellore breed cattle were generated for 14 feedlots, using the Davis Growth Model reparametrized for Nellore. These simulated animals were sold by the model to 83 meat packers in Brazil. The constraints used were feedlot period ≥ 1 d, shrunk BW ≥ 390 kg, and empty body fat ≥ 16%. The optimized method considered the most suitable time for sale. From 280 animals simulated, 258 were sold by optimization process. The integrated MNV increased up to 74% (P < 0.05) for OEE compared to TSE. On average, an animal sold by OEE had a net profit of R$ 202.64 ± 6.35 (± SD) and for TSE R$ 116.54 ± 34.80. The variability of the animal’s sale to OEE was significantly lower (about 3 times) in relation to the TSE, showing that BeefTrader can determine the time of slaughter based on both the chemical and physical body composition. At present, TSE is determined by visual inspection by trained technicians, resulting in greater variability and increased risk, since it is a non-optimized process. BeefTrader changes this scenario using an individual animal’s dynamic growth and chemical composition, along with internal and exogenous economic information. The next step for this DSS is to be tested in a real farming situation, because the results presented here are encouraging. Acknowledgements: Grant #2015/07855-7 [BeefTrader PIPE Project and scholarships (16/02347-6; 16/02451-8; 16/07154-1; 16/12825-2; 16/12709-2)]. Copyright © 2017. American Society of Animal Science .