In a one-and-a-half-year study conducted in the ALS6 region in Europe (Ljubljana, Slovenia), the cultivation of microalgae in anaerobic digestate from food waste, mainly Scenedesmus dimorphus and Scenedesmus quadricauda, was investigated in three ponds (1260 L each) under a greenhouse. The effects of changing digestate quality and quantity as well as seasonal fluctuations on the productivity of the microalgae were investigated in three stages: Learning/Design (SI), Testing (SII), and Verification/Calibration (SIII). A decision support tool (DST) was developed using easy-to-measure parameters such as pH, temperature, electrical conductivity, mineral nitrogen forms and physical, biological parameters (OD, delayed fluorescence intensity). To control optimal pond operation, we proposed the photosynthetic culture index (PCI) as an early indicator for necessary interventions. Flocculation and nitrite levels (above 3 mg NO2-N L\u22121) were signals for the immediate remediation of the algae culture. Under optimal conditions in summer SIII, an average algal biomass production of 11 \u00b1 1.5 g m\u22122 day\u22121 and a nitrogen use efficiency of 28 \u00b1 2.6 g biomass/g N-input were achieved with the developed DST. The developed DST tool was, in this study, successfully implemented and used for the cultivation of microalgae consortia predominated by Scenedesmus dimorphus and S. quadricauda with biogas digestate. DST offers the possibility to be modified according to producers\u2019 specific needs, facility, digestate and climate conditions, and as such, could be used for different microalgae cultivation processes with biogas digestate as a food source.
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The WR model is composed of 3 sub-models concerning (1) soil water storage, (2) P and sediment loss in runoff, and (3) N leaching in percolating water. The WR decision support model was validated using a representative dataset of 94 field sites from across Europe and had an overall accuracy of 75% when compared to the empirically derived values across these sites. This highly accurate, reliable, and useful decision support model for assessing the capacity of agricultural soils to perform the WR function can be used by farmers and advisors help manage and protect their soil resources for the future. This model has also been incorporated into the Soil Navigator decision support tool which provides simultaneous assessment of the WR function and other important soil functions for agriculture.", "keywords": ["2. Zero hunger", "decision support tool", "Nutrition. Foods and food supply", "food security", "04 agricultural and veterinary sciences", "soil functions", "TP368-456", "15. 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The effects of changing digestate quality and quantity as well as seasonal fluctuations on the productivity of the microalgae were investigated in three stages: Learning/Design (SI), Testing (SII), and Verification/Calibration (SIII). A decision support tool (DST) was developed using easy-to-measure parameters such as pH, temperature, electrical conductivity, mineral nitrogen forms and physical, biological parameters (OD, delayed fluorescence intensity). To control optimal pond operation, we proposed the photosynthetic culture index (PCI) as an early indicator for necessary interventions. Flocculation and nitrite levels (above 3 mg NO2-N L\u22121) were signals for the immediate remediation of the algae culture. Under optimal conditions in summer SIII, an average algal biomass production of 11 \u00b1 1.5 g m\u22122 day\u22121 and a nitrogen use efficiency of 28 \u00b1 2.6 g biomass/g N-input were achieved with the developed DST. 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