Soil & Tillage Research最新文献

{"title":"Mechanical properties and microstructural evolution of Malan loess with depth: Insights from multivariate statistical models","authors":"Xuanyu Gao ,&nbsp;Wanli Xie ,&nbsp;Kangze Yuan ,&nbsp;Qiqi Liu","doi":"10.1016/j.still.2025.106548","DOIUrl":"10.1016/j.still.2025.106548","url":null,"abstract":"<div><div>Malan loess is widely distributed on the Chinese Loess Plateau and poses great challenges to geotechnical, ecological, and agricultural practices due to its unique structure and collapsibility. It is essential to understand the evolution of these properties with depth to assess soil stability and reduce engineering risks in the area. This study investigates the mechanical properties and microstructural evolution of Malan loess with depth and employs multivariate statistical methods to explore their complex interrelationships. Oedometer-collapse tests reveal a 94.2 % reduction in collapsibility coefficient (<em>δ</em>_<em>s</em>) from 0.0722 at 1 m to 0.0042 at 9 m, indicating a significant reduction in collapsibility with increasing depth. According to the results of the direct shear test, it showed that the shear strength initially decreases and then increases due to the combined effect of the water content and dry density. Scanning electron microscopy (SEM) images reveal the densification of the loess structure, with changes in particle contact from point to face contact and the evolution from macropores to mesopores and small pores as depth increases. Quantitative analysis by Avzio showed a decrease of 61.5 % in macropores area and an increase of 62.5 % in small pores area. The results obtained by Pearson’s correlation analysis and random forest model showed that among these microstructural characteristics, the total pore area (<em>%IncMSE</em> = 22.77 %) is the most important factor influencing the collapsibility properties of loess and water content (<em>%IncMSE</em> = 17.72 %) acts a key role in controlling shear strength. Additionally, compared to traditional methods, the random forest model offers a more insightful understanding of nonlinear relationships and multifactorial coupling effects. These findings provide scientific guidance for geotechnical engineering in loess regions, aiding in risk mitigation and promoting sustainable construction.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"251 ","pages":"Article 106548"},"PeriodicalIF":6.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts of a fully mechanized timber harvesting system on soil physical properties after a pronounced dry period","authors":"Julian Grünberg ,&nbsp;Franz Holzleitner ,&nbsp;Maximilian Behringer ,&nbsp;Christoph Gollob ,&nbsp;Christian Kanzian ,&nbsp;Klaus Katzensteiner ,&nbsp;Martin Kühmaier","doi":"10.1016/j.still.2025.106551","DOIUrl":"10.1016/j.still.2025.106551","url":null,"abstract":"<div><div>Within the wood supply chain, timber harvesting plays a crucial role, especially with the growing importance of bioeconomy. This study focused on the effects of a fully mechanized timber harvesting system using a harvester and forwarder on various soil properties in a mixed stand dominated by <em>Picea abies</em> located in Upper Austria. Using portable wheel load scales, we measured the load per wheel of both the empty and loaded forwarder. We analyzed soil rutting and penetration resistance before and after two harvester passes, after two and nine forwarder passes, and at the end of the harvesting operations (n_passes = 18). Soil samples were collected using steel cylinders before and after the harvester, and at the end of the harvesting operation. We measured soil water content, bulk density, total porosity, and the C/N ratio. The results showed a mean rut depth of 13 cm after the harvesting operation was completed. Soil deformation began in predisposed areas (e.g., roots and stumps). Maximum penetration resistance occurred at 10–15 cm, highlighting the substantial impact of both harvester and forwarder at this depth. Harvester effects on penetration resistance were noticeable down to 40 cm, while forwarder effects extended beyond 80 cm. Significant changes in bulk density appeared only after forwarder passes. Low initial water content (12 Vol.–%) enhances soil stability, reducing impacts compared to winter harvesting under the high–moisture conditions. Harvesting at the end of a dry period (e.g., late summer) is a viable alternative for protecting soil physical properties.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"251 ","pages":"Article 106551"},"PeriodicalIF":6.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing soil property predictions using spectral fusion: Comparisons between outer product analysis and vector concatenation and among modeling algorithms","authors":"Adnane Beniaich ,&nbsp;Fabrício S. Terra ,&nbsp;José A.M. Demattê ,&nbsp;Ingrid Horák-Terra ,&nbsp;Jhonny K.D. Martins ,&nbsp;Ivana P. Sousa-Baracho","doi":"10.1016/j.still.2025.106546","DOIUrl":"10.1016/j.still.2025.106546","url":null,"abstract":"<div><div>Multiple approaches have been employed to improve predictions of soil properties using vis-NIR-SWIR and mid-IR spectra, including techniques for extracting/enhancing spectral features and various types of regression models. However, only a few studies have conducted statistical comparative analyses to evaluate the impact of spectral fusions and different algorithms in enhancing prediction performances. So, the aim of this study was to compare the predictions of several soil properties (32 in total) using individual (vis-NIR-SWIR and mid-IR) and fused spectra and four different modeling algorithms. We used a spectral database comprising 1259 soil samples, collected in four Brazilian states, and containing chemical, physical, and mineralogical attributes. Outer Product Analysis (OPA) and Vector Concatenation, called here Side-by-Side (SbS), were used as spectral fusion strategies. Additionally, we also tested the following regression models: Support Vector Machine (with linear and radial Kernel functions), Partial Least Squares Regression, and Boosted Regression Trees. Our results showed the effectiveness of both fusion strategies in enhancing the spectroscopic modeling of soil properties compared to the individual ranges. Fused spectra significantly improved the modeling of most soil properties by utilizing all spectral information in distinct ways. Comparing these two strategies, OPA produced better results (models with R² ≥ 0.50) for 70 % of all soil properties, while this proportion was 48 % for SbS. Support Vector Machine with linear kernel trick performed the best significant modeling results for 78 % of all soil properties with R² ≥ 0.50. Thereby, our results confirm the usefulness of the spectral fusion as an effective technique to improve the spectroscopic prediction of soil properties important for soil survey, classification, mapping, and management.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"251 ","pages":"Article 106546"},"PeriodicalIF":6.1,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of different VisNIR and MIR spectroscopic techniques and the potential of calibration transfer between MIR laboratory and portable instruments to estimate soil properties","authors":"F.H.C.A. Silva ,&nbsp;Nuwan K. Wijewardane ,&nbsp;Michael S. Cox ,&nbsp;Xin Zhang","doi":"10.1016/j.still.2025.106555","DOIUrl":"10.1016/j.still.2025.106555","url":null,"abstract":"<div><div>Spectroscopic analysis of soil using visible-near infrared (VisNIR) and mid infrared (MIR) regions is a rapid, low-cost, and nondestructive tool which has the potential of substituting or complementing conventional laboratory methods. Numerous studies have used different data acquisition, preprocessing, and modeling techniques to predict different soil attributes, but their impact on prediction accuracies was not consistent. In addition, instrumental disparities prohibit the application of models from laboratory to portable spectrometers limiting field applications. The goal of this study was to enable the field application of spectroscopic techniques for soil sensing using portable spectrometers. To this end, three objectives were defined: (i) to evaluate the impact of preprocessing and modeling algorithms, (ii) to compare the different spectral regions and portable versus laboratory spectrometers, and (iii) to evaluate the potential of different calibration transfer approaches to eliminate the instrumental impact in MIR region. A total of 474 soil samples were collected, air dried, ground, and sieved to obtain &lt; 2 mm fraction followed by scanning with five spectrometers. Four preprocessing techniques (no preprocessing, Savitzky-Golay, standard normal variate, multiplicative scatter correction) were compared for prediction accuracy. Four different modeling techniques (partial least square regression (PLSR), support vector regression, random forest, and artificial neural network) were used to build and validate the models. Results revealed that PLSR outperformed all other nonlinear modeling techniques and preprocessing was not required to calibrate robust and reliable models. In general, the MIR region outperformed the VisNIR region while portable instruments performed on par with laboratory instrumentation. Four calibration transfer methods (external parameter orthogonalization (EPO), direct standardization (DS), slope bias and spiking with extra weights) were deployed to evaluate the transferability of the models between laboratory and portable spectrometers in the MIR region. Extra weighted spiking consistently yielded superior performance in correcting instrumental disparities in the spectra with EPO and DS showing significant variability in prediction accuracy across different properties.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"251 ","pages":"Article 106555"},"PeriodicalIF":6.1,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Are gaseous nitrogen losses affected by the fertilizer type and rate in maize?","authors":"Sergio Tovar Hernández ,&nbsp;Fernando Salvagiotti ,&nbsp;Nuria Lewczuk ,&nbsp;Walter Carciochi ,&nbsp;Keren Hernandez Guijarro ,&nbsp;Micaela Biassoni ,&nbsp;Esteban Kehoe ,&nbsp;Oscar Avila ,&nbsp;Gisela Garcia ,&nbsp;Mirta Toribio ,&nbsp;Fernando García ,&nbsp;Hernan Sainz Rozas ,&nbsp;Nahuel Reussi Calvo","doi":"10.1016/j.still.2025.106519","DOIUrl":"10.1016/j.still.2025.106519","url":null,"abstract":"<div><div>Nitrogen (N) management practices have the potential to reduce nitrous oxide (N₂O) emissions and ammonia (NH₃) volatilization while enhancing maize (<em>Zea mays</em> L.) productivity. However, the specific effect of N rate and source remains unclear. This study aimed to evaluate, during the maize growing season, the effect of both N rate and source on <em>i</em>) gaseous losses through NH₃ volatilization and N₂O emissions and <em>ii</em>) agronomic traits such as grain yield and N uptake (N_upt). Four experiments were conducted in the humid temperate region of Argentina between 2020 and 2022. Treatments consisted of four N rates, between 0 and 200 kg N ha⁻¹ combined with four N sources [urea, urea with urease inhibitor (U_UI), urea with nitrification inhibitor (U_NI), and calcium ammonium nitrate (CAN)]. We observed a significant response to N fertilization on grain yield with increases from 20 % to 64 %. However, no differences between N sources were determined. CAN presented the greatest N_upt, which was up to 27 % higher than urea-based N sources. NH₃ volatilization was the major N loss process with urea exhibiting the highest values. U_UI and CAN significantly reduced NH₃ volatilization in comparison to urea. CAN showed the highest N₂O emissions, losing up to 1.5 % of the applied N. U_NI significantly reduced N₂O emissions by 50 %. Emission factors for both NH₃ and N₂O were consistently lower than the default values suggested by the IPCC guidelines. In conclusion, N sources such as U_UI and U_NI lead to achieve high grain yields with a low environmental impact.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"251 ","pages":"Article 106519"},"PeriodicalIF":6.1,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A two-stage algorithm for regional-scale SOC prediction: Eliminating the spatial scale effect between multi-source remote sensing data","authors":"Yilin Bao ,&nbsp;Xiangtian Meng ,&nbsp;Huanjun Liu ,&nbsp;Mingchang Wang ,&nbsp;Xinle Zhang ,&nbsp;Abdul Mounem Mouazen","doi":"10.1016/j.still.2025.106552","DOIUrl":"10.1016/j.still.2025.106552","url":null,"abstract":"<div><div>Mapping the spatial distribution of soil organic carbon (SOC) content with high spatial resolution is important for sustainable soil management. Remote sensing variables (RSV) and environmental covariates (ECs) have been widely used for SOC content prediction and mapping. The spatial resolution mismatch between RSV and ECs, where ECs typically have lower resolution, introduces a scale effect. For example, as RSV values vary significantly within a 1 km * 1 km range, incorporating ECs at a 1 km * 1 km resolution may smooth pixel values in the mapping results, thereby compromising prediction accuracy and the mapping results. Currently, few studies have focused on how scale effects affect mapping results. Therefore, the aim of this study is to construct a two-stage algorithm for regional-scale SOC content prediction, which can effectively address the issue of spatial scale inconsistency among remote sensing images and ECs, and then combine this with convolutional neural network (CNN) model to establish a high accuracy and robust SOC content prediction model. A total of 11,841 cloudless Landsat Operational Land Imager images from 2016 to 2021 were obtained to calculate RSV, and climate, soil properties, and terrain variables were employed to reflect ECs. In the two-stage algorithm, the coefficient of variation (CV) of RSV at the spatial resolution of ECs was calculated and divided into three levels of high, medium, and low. Then we determined the level of CV of RSV, at which ECs cannot be added as input variables for SOC content prediction, thus avoiding the scale effects. The results indicated that the prediction accuracy based on RSV and ECs was higher than that based solely on RSV or ECs, while the prediction accuracy based on RSV was higher than that based on ECs. The prediction results of RSV combined with ECs obtained the best performance without considering the variability, with mean RMSE, R², RPIQ, and MAE values of approximately 5.60 g kg⁻¹, 0.74, 1.75, and 4.12 g kg⁻¹, respectively. When the variability was considered, with mean RMSE, R², RPIQ, and MAE values of approximately 4.96 g kg⁻¹, 0.78, 2.05, and 3.72 g kg⁻¹, respectively. It was found that only RSV should be used as input variables in regions with CV&gt; 1, whereas both RSV and ECs should be used in regions with CV&lt; 1. This could improve the prediction accuracy and eliminate the influence of the spatial scale effect of ECs on the mapping results. When the resolution difference between the RSV and ECs is 16 times or more, the influence of the spatial scale effect on the mapping results cannot be ignored. The approach proposed in this study can be applied to any problems of regression using multi-source remote sensing data of different spatial resolution, thus avoiding scale effects on the mapping results.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"251 ","pages":"Article 106552"},"PeriodicalIF":6.1,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of nitrogen fertilizer application enhanced sugar beet productivity and socio-ecological benefits in China: A meta-analysis","authors":"Longfeng Wang ,&nbsp;Baiquan Song ,&nbsp;Muhammad Ishfaq ,&nbsp;Xiaoyu Zhao","doi":"10.1016/j.still.2025.106547","DOIUrl":"10.1016/j.still.2025.106547","url":null,"abstract":"<div><div>Nitrogen (N) fertilizer is a key driver for improving sugar beet production, however, the comprehensive effect of the N application rate on beet yield and socio-ecological benefits across various sugar beet-producing regions in China is remains unclear. To address this issue, a comprehensive meta-analysis was conducted on 256 data sets from 87 studies published between 1980 and 2024. The goal was to optimize nitrogen fertilizer management for sugar beet production. The analysis revealed a clear correlation between nitrogen application and improved sugar beet yield in China. Nitrogen application led to an average increase in beet yield by 26.93 % and sugar yield by 17.74 %, emphasizing its key role in boosting productivity. The highest increase in beet yield (31.10 %) and sugar yield (21.65 %) were achieved with nitrogen application rates between 100 and 200 kg N ha⁻¹. However, the benefits of increasing nitrogen rates diminished beyond this range, as indicated by reduced agronomic efficiency and partial factor productivity. This suggests a threshold where additional nitrogen offers little yield improvement. Interestingly, the optimal N application thresholds are lower in North China as compared to Northeast and Northwest China. Ecological and social benefits were maximized at nitrogen application rates of 169–188 kg N ha⁻¹, with a clear reduction in diminishing returns. Excessive N application can lead to reduced ecological and social benefits. Site-specific factors, such as soil pH of 6.5–7.5, total N ≤ 1 g kg⁻¹, and available potassium ≤ 150 mg kg⁻¹, further enhanced beet yield by 36.24 %, 33.84 %, and 35.37 %, respectively. Sugar yield was improved by 27.74 %, 26.13 %, 14.98 %, and 21.84 %, respectively, with optimal conditions including total N ≤ 1 g kg⁻¹, soil pH ≤ 6.5, alkali-hydrolyzable N &gt; 120 mg kg⁻¹, and available phosphorus 20–40 mg kg⁻¹. This study provides valuable insights for optimizing N fertilizer usage in sugar beet production, enhancing environmental sustainability, and advancing green agricultural practices, with potential implications for global sugar beet nitrogen management.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"251 ","pages":"Article 106547"},"PeriodicalIF":6.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143636395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Root plasticity improves the potential of maize/soybean intercropping to stabilize the yield","authors":"Zhen Fan ,&nbsp;Pengzhao Liu ,&nbsp;Yanrong Lin ,&nbsp;Binbin Qiang ,&nbsp;Zhipeng Li ,&nbsp;Mengwei Cheng ,&nbsp;Qihang Guo ,&nbsp;Jinpeng Liu ,&nbsp;Xiaolong Ren ,&nbsp;Xining Zhao ,&nbsp;Xiaoli Chen","doi":"10.1016/j.still.2025.106553","DOIUrl":"10.1016/j.still.2025.106553","url":null,"abstract":"<div><div>Intercropping is considered an effective practice to improve farmland productivity and root plasticity is a major reason for the high yields from intercropping. Understanding the changes in root systems is crucial for optimizing intercropping systems. Previous investigations of intercropping root systems have focused mainly on the impact of interspecific interactions on root distributions, with relatively less attention paid to the allocation of assimilates in root systems and the morphological characteristics of roots in the maize/soybean intercropping system, especially under different water and nitrogen (N) conditions. In this study, a two-year field (2022–2023) experiment was conducted in the eastern hilly area of the Loess Plateau in a two-factor split-plot design, with three cropping patterns (sole maize, sole soybean, and maize/soybean intercropping) as the main treatments, and N fertilizer application (no N fertilizer and N fertilizer) as the sub-treatments, to quantify the effects of nitrogen application on the root plasticity of the maize/soybean intercropping system between years of different rainfall. The results showed that maize/soybean intercropping enhanced land use efficiency, with a land equivalent ratio (LER) of 1.09–1.16. Compared with sole cropping, the yield per unit area of intercropping maize increased by 21.3 %-29.3 %, but that of intercropping soybean decreased by 7.7 %-14.2 %. Intercropping changed the allocation of assimilates in both maize and soybean, such that the root:shoot ratio of intercropping maize decreased by 4.5 %-17.8 %, and that of intercropping soybean increased by 27.8 %-41.5 %. In the wet year, intercropping maize and soybean exhibited a shallow root growth pattern, whereas maize demonstrated a deep root growth pattern during the dry year. The roots of intercropping maize tended to grow towards the border rows of intercropping soybean, where the root length density (RLD) of soybean decreased by 36.6 %-49.5 %. In addition, differing moisture and N conditions altered the root morphology of maize and soybean, with N application and intercropping increasing the proportion of thick roots in maize and soybean in the upper soil layer in 2023. In summary, across different water and nitrogen conditions, the plasticity shown by maize and soybean in the allocation of assimilates, root distribution, and root morphology promoted an increase in yield in the intercropping system. Our research results help researchers to better understand the mechanisms of root interactions in the maize/soybean intercropping system, facilitating a more reasonable arrangement of intercropping planting patterns.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"251 ","pages":"Article 106553"},"PeriodicalIF":6.1,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Co-cultivation of rice and aquatic animals: Improving soil fertility and providing more rice in China","authors":"Kai Chen ,&nbsp;Ming Yu ,&nbsp;Bo Cheng ,&nbsp;Cougui Cao ,&nbsp;Yang Jiang","doi":"10.1016/j.still.2025.106526","DOIUrl":"10.1016/j.still.2025.106526","url":null,"abstract":"<div><div>Over the last few years, the co-culture of rice and aquatic animals (CRAA) has been developed intensively with an area of 2.878 million hectares in China owing to its important economic benefits. However, at this rapid development process, a systematic and quantitative analysis on how CRAA affects rice yield and soil fertility has remained unexplored in actual field conditions. For this, we performed a meta-analysis of the CRAA effects on the rice yield and soil fertility with 1028 comparisons from 278 publications during 1987–2024. Yield and soil fertility varied by co-culture modes, regions, management practices and initial soil conditions. Our results indicated that the rice-amphibians co-culture had a better effect on crop yield and soil fertility than that of fish, waterfowl and crustaceans. In different regions, CRAA had the greatest benefits for soil fertility, and it caused the highest increases in rice yield by 11.27 % in south of China than that in other regions. Furthermore, we found that in acidic soils (pH&lt;7) with TN≤ 1.5 g kg⁻¹ provide a better soil environment for rice growth under CRAA. And the yield of the <em>indica</em> rice showed a more obvious advantages under CRAA treatments. In different fertilization schemes, the chemical fertilizer combined with organic fertilizer (CF+OF) increased rice productivity and soil properties, which was higher than that of sole chemical fertilizer. In general, the increase of rice productivity is closely associated with the improvement of soil fertility in CRAA. Our results indicated that CRAA improved sustainable soil fertility and rice yield in China.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"251 ","pages":"Article 106526"},"PeriodicalIF":6.1,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of installing soil-loosening plow on the working performance and power consumption of the shovel-type seedbed preparation machine for rapeseed in rice stubble fields","authors":"Jianxin Lin ,&nbsp;Qingxi Liao ,&nbsp;Yan Kang ,&nbsp;Wenbin Du ,&nbsp;Wenbin Zhang ,&nbsp;Chongjiu Wu","doi":"10.1016/j.still.2025.106544","DOIUrl":"10.1016/j.still.2025.106544","url":null,"abstract":"<div><div>In the rice–rapeseed rotation area of the mid-lower Yangtze River in China, the shovel-type seedbed preparation machine (SSPM) is used to efficiently prepare seedbed for direct rapeseed sowing in a single operation. With a tillage depth of 200 mm, the shovel will be subjected to a significant impact load when penetrating the soil at high speed, leading to accelerated wear of key components and reduced machine reliability. Herein, an active/passive combined tillage machine (ACTM), comprising soil-loosening plow at the front and the SSPM at the rear, was developed and tested in two rice stubble fields. Field experiments were conducted to compare the working performance and power consumption of the SSPM and the ACTM at three different forward speeds, i.e., 1.8, 2.7, and 3.6 km/h. The results indicated that owing to the predisturbance of the uncultivated soil caused by the soil-loosening plow, the ACTM demonstrated lower power takeoff (PTO) torque requirement and total power consumption than the SSPM under identical operating parameters, with average reductions of 30.97 % and 8.42 % in the PTO torque and total power consumption, respectively. Furthermore, the ACTM exhibited superior performance than the SSPM in terms of tillage depth stability coefficient and soil crushing rate. In particular, at a forward speed of 3.6 km/h in field 2, improvements of 18.73 % and 19.65 % in tillage depth stability coefficient and soil crushing rate, respectively, were observed for the ACTM compared with the SSPM. The straw burial rate of SSPM and ACTM in both experimental fields decreased with increasing forward speed, whereas the seedbed surface evenness showed an opposite trend. These findings suggest that integrating soil-loosening plow with SSPM enhances the quality of seedbed for rapeseed in rice stubble fields while reducing PTO torque and total power consumption.</div></div>","PeriodicalId":49503,"journal":{"name":"Soil & Tillage Research","volume":"251 ","pages":"Article 106544"},"PeriodicalIF":6.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}