Predicting soybean losses using carbon dioxide monitoring during storage in silo bags

• Published in: Journal of stored products research Vol. 82; pp. 1 - 8
• Main Authors: Taher, H.I., Urcola, H.A., Cendoya, M.G., Bartosik, R.E.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-06-2019
• Subjects: CO2; Hermetic storage; Losses prediction; Preventive monitoring; Soybean; Preventive monitoring; Soybean; Hermetic storage; Losses prediction; CO2
• ISSN: 0022-474X; 1879-1212
• DOI: 10.1016/j.jspr.2019.03.002

The rapid increase of the overall grain production of Argentina resulted with a storage capacity deficit in permanent structures of 40–50 million tons, and this context favored the rapid adoption of the silo bag technology. Silo bag allows differing grain selling from harvest time, taking advantage of the seasonal price changes and, hence, improving farmers’ income. However, storing grain in silo bag could be risky if inadequate planning, handling or monitoring is implemented. Thus, the objective of this article was to develop a prediction model for soybean losses in silo bag storage based on monitoring CO2 concentration and other sensible variables. During 2013, an experiment was conducted in 13 soybean silo bags placed at farms and grain elevators in Balcarce area, South East of Buenos Aires province, Argentina, since May to December. Grain samples were collected and grain quality was evaluated. Storage variables, such as moisture content and interstitial atmosphere gas composition were also recorded, and at the end of storage, physical grain losses were quantified for each silo bag (kg of spoiled grain not commercialized). The results showed that there was not generalized quality loss in any silo bag, but localized losses were observed. These losses occurred due to water entrance in the silo bag through openings which resulted in spoiled grain from 140 to 4320 kg, representing from 0.07% to 2.16% in a 200 ton silo bag. Next, a correlation to predict grain losses was developed, which considered grain moisture and a predictor related to the CO2 concentration at the silo bag closing end as independent variables. This correlation explained 73% of the grain losses variability, allowed to model different levels of losses, and was consistent with biological concepts.
•Currently there is no model to predict soybean losses during silo bag storage.•Predicting silo bag soybean losses can help improving storage management.•A silo bag CO2 based prediction model for soybean losses was developed.•The model explained most soybean losses under field conditions.