High-Quality Fertilizers from Biogas Digestate

Sürmen, Mustafa; Kara, Emre

doi:10.1007/978-3-030-71571-7_10

Cited by 2 publications

(2 citation statements)

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“…The digestate is a valuable biofertiliser containing many plants essential nutrients and microorganisms [15]. Due to its high nutrient contents, it can be used as a fertiliser in agriculture [16], thereby reducing the utilisation of chemical fertiliser [17][18][19]. Still, the nutrient element imbalance and component unpredictability are the main issues with biogas digestate [20].…”

Section: Introductionmentioning

confidence: 99%

“…Combining inorganic and organic fertilisers in different ratios enhances the yield [26][27][28][29][30] and can reduce the cost of rice production [31]. However, combined with chemical fertiliser, biogas digestate has faster reactions and is more effective for crop yield [17]. Khan et al [32] noted that applying 50% chemical fertiliser and 50% biogas slurry increased cob yield by 20%.…”

Section: Introductionmentioning

confidence: 99%

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Optimisation of Anaerobic Digestate and Chemical Fertiliser Application to Enhance Rice Yield—A Machine-Learning Approach

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Panja,

GhoshThakur

et al. 2023

Sustainability

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The present study evaluates the synergistic application of an anaerobic digestate for enhanced rice yield. The study utilised the digestate as a fertiliser with various inoculum-to-substrate (IS) ratios of anaerobic digestion from cow dung and water hyacinth (CW–BF) with combinations of NPK (16-22-22) fertiliser for rice yield optimisation. The outcome of the combined digestate and fertiliser application on rice cultivation was observed in terms of parameters such as the number of tillers, panicle number, panicle length, fertile panicles, and 1000-grain weight. The digestate combination of CW–BF:NPK (3:1:1) resulted in the highest grain yield (7521 kg/hectare) with increased panicle length, test weight, and more filled grains than the other combinations. Moreover, various machine-learning approaches were used to study the efficacy of the different combinations of applied fertiliser (cow dung, water hyacinth, and NPK). The gradient-boosting machine-learning model was appropriate for predicting the modelling based on the measured data. Principal component analysis revealed NPK as the first principal component with high loading values and the digestate as the second principal component, which indicates its crucial role in fertiliser preparation. Therefore, deploying such hybridised fertilisers using the proper statistical analysis and machine-learning approaches can improve rice yield, which would be essential for the socio-economic uplifting of marginal rice farmers.

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