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Soil acidification is a natural phenomenon that human activity can accelerate or manage. Acid soils limit plant growth by reducing nutrient availability and causing aluminum toxicity, significantly reducing crop production. Liming has proven efficient for increasing crop yield on acidic soils. Knowledge is required on the effect of liming on the soil pH dynamics to find the suitable material, application rate, timing, and method. The objective of this study was to develop a prediction model of soil pH temporal variations after lime application using data from the literature. A database was built from research results extracted from 16 scientific papers that provided data on soil acidity changes over time under different liming treatments. Machine learning (ML) was used to predict soil pH dynamics from eight predictive parameters: rate of application, time since application, neutralizing value (CCE), grind fineness (D50), pH measurement depth, soil acidity (pH prior to liming), the type of solution used for pH measurement (water or CaCl 2) and the soil:solution ratio used to measure pH. Since soil pH fluctuates with seasons, all pH values were expressed as difference between the actual pH value and that of an unlimed control (ΔpH). The Random Forest (RF) algorithm was tested to predict ΔpH over time. On testing, we obtained an R2 between measured and predicted ΔpH values of 0.881 and an RMSE of 0.230. These results are excellent, considering the heterogeneity in soils, liming materials, and pedoclimatic conditions found in the 16 papers. The primary factors influencing ΔpH, ranked by their impact are application rate, time elapsed post-application, and lime characteristics including its grind fineness and neutralizing value. • Random Forest robustly predicts soil pH change over time following lime application. • The predictors are: rate, neutralizing value, particle size, depth, and initial pH. • The model predicts that, on average, lime increases soil pH for 82 weeks. • Machine learning models are promising tools in managing liming practices. [ABSTRACT FROM AUTHOR]

• Liming increases the amount of biological nitrogen fixation. • Liming provides higher soybean yields and a more positive N-balance. • Soil pH and Al saturation are key factors in biological nitrogen fixation. Soil acidity can impair biological N 2 fixation (BNF) and increase soybean (Glycine max L.) reliance on soil N to satisfy its plant N demand. This study aimed to evaluate the effect of liming, fertilization strategy, and grazing on: i) the N supplied via BNF process, ii) partial N-balance, and iii) soybean yield, in an integrated crop-livestock system under a non-tilled sandy and acidic Acrisol. Initial soil conditions were soil pH of 4.0 and Al saturation of 50%. Limestone was applied to rise soil pH to 6.0 and a control area was kept with the initial soil conditions. Soybean was grown in the summer and Italian ryegrass (as a cover crop or grazed by sheep) in the winter. Fertilization strategies consisted in applying P and K prior to soybean or ryegrass. Partial N-balance was calculated by the difference between N derived from BNF and N removed in soybean seeds. Contribution of N from the atmosphere to soybeans was on average 62 and 66% in plots without and with liming, respectively. The fixed-N was 27% greater under low soil acidity due to lime. Partial N-balance was positive in both treatments, but 57% greater in the limed- relative to the non-limed (+58 kg ha−1) soils, resulting in 11% soybean yield increase when lime was added. The most relevant soil chemical properties influencing N fixation were soil pH and Al saturation. Grazing and fertilization strategies had no effect on soil acidity properties, N supply, and soybean evaluations. Further research should explore the long-term effect of grazing, fertilization strategies, and liming. [ABSTRACT FROM AUTHOR]