The cultivated soils in several semi-arid areas have very low organic matter due to climatic constraints that limit primary crop yield. Conservation tillage systems, outlined here as no tillage, no tillage with straw return and straw incorporation into the field, have been accepted as capable systems that preserve soil’s resources and sustain soil productivity. However, in semi-arid climates, there is presently no knowledge about the influence of different conservation tillage techniques on soil’s physical, chemical and biological properties at different soil depths in spring wheat fields and only little information about spring wheat yield in these management systems. Therefore, the present study was carried out with the objective of examining the impact of conservation tillage systems on soil properties (physical, chemical and biological) and spring wheat yield. The three conservation tillage treatments consisted of no tillage system (NT), wheat stubble return with no tillage (NTS) and straw incorporation with conventional tillage (CTS), as well as one conventional tillage (CT) control treatment, which were evaluated under randomized complete block design with three replications. The three conservation tillage treatments were compared with the conventional tillage control. Conservation tillage significantly increased the bulk density, gravimetric water content, water storage, hydraulic conductivity and soil aggregates and decreased the pore space and soil temperature compared to CT; however, no significant difference was found in the case of field capacity. Soil chemical properties in the 0–40 cm soil layer increased with conservation tillage compared to CT. Conservation tillage also notably increased the soil microbial counts, urease, alkaline phosphatase, invertase, cellulase and catalase activities relative to CT. Microbial biomasses (carbon and nitrogen) and wheat yield significantly elevated under conservation tillage compared to CT. Therefore, conservation tillage could significantly improve soil properties and maintain wheat yield for the research zone.

The influence of tillage and nitrogen fertilization methods on soil quality attributes and crop agronomic characteristics has been studied broadly under different agroclimatic conditions. Nevertheless, the interactive effect of tillage and fertilization approaches on soil properties on different soil depths and yield is rarely addressed, particularly on the Loess Plateau belt, and requires more exploration. Thus, this research was conducted in order to evaluate the interactive impact of tillage and nitrogen fertilization methods on soil properties and wheat productivity. The treatments included conventional tillage (CT) and no-till (NT) with different fertilization approaches (no fertilization: CK, chemical nitrogen fertilizer: N, organic fertilizer: M, combined application of nitrogen fertilizer and organic fertilizer: NM) and were explored in a split plot arrangement under a randomized complete block design replicated thrice on soil properties (SWC, SOC, TN, TP, NO₃⁻-N, NH₄⁺-N, and stoichiometric ratio) and wheat yield. The results showed that sole no-tillage and NT in association with nitrogen fertilization (inorganic and organic) significantly increased the soil water content, SOC, TN, NH₄⁺-N, C/P, and N/P ratios and wheat productivity but did not significantly yield TP, whilst it reduced the NO₃⁻-N and C/N ratio compared with sole CT and CT together with nitrogen fertilization (organic and inorganic). Overall, NT in association with the joint application of inorganic and organic N fertilization are the best techniques to improve soil water status and nutrient status under the wheat mono-cropping system conditions and yield.

Soil carbon and nitrogen pools are crucial for maintaining the balance of carbon and nitrogen cycling in ecosystems and also for reducing the impacts of global climate change. However, current research lacks an understanding of the effects of long-term vegetation restoration on soil carbon and nitrogen pools and their storage in vulnerable ecosystems. Therefore, we studied the characteristics of soil carbon (soil organic carbon, microbial biomass carbon, dissolved organic carbon) and nitrogen pools (total nitrogen, ammonium nitrogen, nitrate nitrogen) and their storage under four types of vegetation restoration (Stipa bungeana Trin., SB; Caragana korshinskii Kom., CK; Xanthoceras sorbifolia Bunge., XS; Picea asperata Mast., PA) in the Longzhong Loess Plateau area. We found that the carbon and nitrogen pools in the 0–40 cm soil layer under the XS and PA vegetation restoration types were higher compared to those under the SB and CK vegetation, and the values of soil ammonium–nitrogen ratios ranged from 0.72 to 0.83 under different vegetation types. Carbon and nitrogen interactions were stronger in the 0–40 cm soil under PA vegetation, which had significantly higher soil carbon (49.06 t·ha⁻¹) and nitrogen (1.78 t·ha⁻¹) storage than did the other vegetation types. We also found that soil carbon and nitrogen stores differed among different types of vegetation restoration. These elements were mainly distributed in soils from 0 to 20 cm depth, where the carbon and nitrogen pools in soils from 0 to 10 cm exceeded those in the lower layers. Furthermore, we discovered that redundancy analysis (RDA) supported by soil enzyme activity and physical properties significantly explained the variation in soil carbon and nitrogen triggered by vegetation restoration. According to this research, the stability and transformation of soil carbon and nitrogen pools in the region can be influenced by various forms of vegetation restoration. Additionally, the findings highlight that forest vegetation restoration can be a successful strategy for effectively sequestering soil carbon and nitrogen within the Longzhong Loess Plateau area.

To improve the applicability of crop models, this study compared two algorithms for optimizing the single objective parameters of the spring wheat in the dryland grain growth sub-model to identify the more efficient algorithm for application in future model parameter optimization. Based on field experiments from 2015 to 2021 in Gansu Province, this study combined weather data and yearbook yield data from 1984 to 2021 to optimize parameters related to grain growth of spring wheat in dryland based on the next-generation APSIM using two algorithms: the Nelder–Mead simplex algorithm and the DREAM-zs algorithm. The results were as follows: the optimization results of both algorithms were the same, but the DREAM-zs algorithm converged faster; the optimized parameters for the grain growth stage of Dingxi35 spring wheat were: a grain number per gram stem of 25 grains, an initial grain proportion of 0.05, and a maximum grain size of 0.049 g; after optimization, the root mean square error (RMSE) of observed and simulated yield values decreased from 186.84 kg/hm² to 115.71 kg/hm², and the normalized root mean square error (NRMSE) decreased from 10.33% to 6.40%. The optimized results were consistent with the growth and development process of wheat and had high applicability.

Process-based crop growth models, as an important analytical tool in agricultural production, face the problem of calibrating many parameters during the application process, and sensitivity analysis (SA) can quantify the effects of the model input parameters on the model output and provide an important basis for parameter calibration. In this study, we combined the good performance of the Agricultural Production Systems sIMulator Next-Generation (APSIM NG) model in simulating crop growth, soil carbon and nitrogen cycles, and soil N₂O emissions with the efficient computational efficiency of the extended Fourier amplitude test (EFAST) method. The sensitivity of the APSIM NG model to the simulation of soil N₂O emissions was systematically investigated using the EFAST method in a dry-crop wheat field in the semi-arid region of the Loess Plateau in Longzhong, China, where 28 crop cultivar parameters, 15 soil parameters, 4 meteorological parameters, and 4 field management parameters were selected. The parameters were selected based on the existing literature and the official documents of the model, and the parameter boundaries were determined based on the initial values of the APSIM NG model and the measured data and adjusted upward and downward by the standard normal distribution. In this study, parameters with a first-order sensitivity index ($S_i$) > 0.05 and a total sensitivity index ($S_{Ti}$) > 0.10 were identified as having a significant influence on the model outputs. The results of this study demonstrated that soil N₂O emission modeling in dry-crop wheat fields showed high sensitivity to the following parameters: (1) Among the crop cultivar parameters, the sensitivity from high to low was the leaf appearance rate, maximum leaf area, maximum nitrogen concentration of the grain, and thermal time from the starting grain-fill stage to end grain-fill stage. (2) Among the soil parameters, the sensitivity from high to low was a lower effective moisture limit, wilting coefficient, and ammonium nitrogen content. (3) Among the meteorological parameters, precipitation and solar radiation showed high sensitivity. (4) Among the field management parameters, the nitrogen application rate exhibited the most significant sensitivity. For this reason, we believe that by prioritizing the calibration of the most sensitive parameters through the results of the sensitivity analysis in this study, the workload of the APSIM NG model in the calibration process can be effectively reduced, which is conducive to the rapid localization and application of the model.