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Organic material input can improve soil health and mitigate adverse environmental consequences; however, there is controversy regarding the promoting effects of organic material inputs on soil health due to a lack of quantitative information on the effects of different organic materials from agricultural sources on soil health. This study proposed a novel soil health evaluation method using tree-structured machine learning algorithms to quantitatively assess the effects of animal-sourced (AS) and plant-sourced organic materials (PS) on soil health. The results demonstrated that in the testing set, the two ensemble models, random forest regressor (RFR) and light gradient boosting machine regressor (LightGBMR), showed high coefficient of determination (R 2) of 0.879 and 0.877, respectively, and relative percent deviation (RPD) of 2.83 and 2.84, respectively (P < 0.001). Our findings indicated that the application of AS resulted in superior improvement of soil health for rice, wheat, and maize in comparison to PS. Compared with chemical fertilizer (CF), AS improved soil health indices (SHIs) for rice, wheat, and maize by 23.8–39.8%, PS improved SHIs for rice, wheat, and maize by 13.4–24.4%, and AS + PS improved SHIs for rice, wheat, and maize by 16.0–35.4% (P < 0.001). In addition, the positive effect of AS on SHIs was greater than that of PS. Compared with PS, the SHIs for rice, wheat, and maize increased by 7.34–14.9% for AS (P < 0.001). The reasonable nitrogen (N) application rates for rice, wheat, and maize were 262–290, 235–259, and 299–331 kg ha−1 year−1, respectively. Based on the positive feedback effects on soil health, the reasonable AS substitution rates for rice, wheat, and maize were 26.4–29.2%, 27.0–29.8%, and 29.6–32.8%, respectively. This study confirmed that organic materials improve soil health through optimized substitution, thus contributing to sustainable agricultural management and reducing adverse environmental impacts. [Display omitted] • A novel soil health assessment method using machine learning algorithms was explored. • Manure showed a better effect on soil health indices than plant-sourced materials. • Ideal manure substitution rates for rice, wheat, and maize were 27.8, 28.4, and 31.2%. [ABSTRACT FROM AUTHOR]

Abstract: Chemical N input is essential for high rice yields. However, low recovery efficiency of chemical fertilizer N with flooding irrigation in the anthropogenic-alluvial soil resulted in N lost from the rice field in Ningxia Irrigation Region in the upper reaches of the Yellow River. With the technique of stable isotope 15N-traced urea, we conducted two years experiment to estimate the recovery efficiency and loss of applied chemical fertilizer in a rice field. The three fertilizer N treatments included 300kgha−1 (N300, the conventional application rate), 240kgha−1 (the optimized N application amount, N240) and no N fertilizer application treatment (CK). We estimated the recovery of 15N-labelled urea in grain, straw and root of rice and residues in soil profile. The 15N not accounted for in the plant and soil was presumably lost. The results showed that more efficient use of N fertilizer could allow current N application rates to reduce by 20%. This would still maintain crop yields while substantially reducing N losses to the environment. The high N fertilizer application increased the N uptake by rice derived from fertilizer, and the amount of N rice taken up from soil reduced correspondingly, which resulted in the higher N surplus in soil. Under the conventional irrigation and fertilizer management level, the recovery rate of 15N-labelled urea in rice–soil system was about 48–49%. The 15N-labelled fertilizer recovery in rice plant (N dff ) was 26–30%. In the paddy soil profiles of 0–90cm, the residual of 15N-labelled fertilizer in soil (N dfs) were 54–70kgha−1, and N residual rate in soils were 18–23%. The annual N loss from the rice field in the Irrigation Region was 28,865tons. The distribution of 15N abundance variability in different soil profile indicated that fertilizer N leached into the deep soil layers along with irrigation water as a result of continuous yearly rice planting. Optimization of nitrogen fertilizer can significantly reduce the amount of N residuals and N loss from the paddy field. Compared with N300, optimized nitrogen fertilizer application could decrease the loss of fertilizer N by 22–34kgha−1, and reduce the amount of N surplus by 26–33kgha−1 while the dry matter of rice increased 8–15% and N uptake by rice increased 2–6%. Considering the high food production and the minimum environmental threat, we should fully take into account the optimization application by reducing fertilizer N inputs. However, the interaction between irrigation management and N application rate on N use efficiency in alkaline anthropogenic-alluvial soil needs to be further studied. [Copyright &y& Elsevier]