State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau

Water is vital for plant growth and development. Water-deficit stress, permanent or temporary, limits the growth and the distribution of natural vegetation and the performance of cultivated plants more than any other environmental factors do. Although research and practices aimed at improving water-stress resistance and water-use efficiency have been carried out for many years, the mechanism involved is still not clear. Further understanding and manipulating plant-water relations and water-stress tolerance at the scale of physiology and molecular biology can significantly improve plant productivity and environmental quality. Currently, post-genomics and metabolomics are very important to explore anti-drought gene resource in different life forms, but modern agricultural sustainable development must be combined with plant physiological measures in the field, on the basis of which post-genomics and metabolomics will have further a practical prospect. In this review, we discussed the anatomical changes and drought-tolerance strategies under drought condition in higher plants.

The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.

Soil aggregate stability influences several aspects of soil physical behavior, such as water infiltration and soil erosion (Amézketa, 1999). We investigated the soil aggregate stability characteristics in the framework of the ‘Grain for Green’ vegetation rehabilitation project at the Loess Plateau (China) by using the Le Bissonnais (1996) and the modified Yoder (1936) methods. Both non-grazed grassland and afforestation revegetations were considered. The size distribution mode was always > 2 mm for the fast wetting test (FW) in the non-grazed grassland communities. This fraction accounted for approximately 40% to 80% of the total soil weight. The wet stirring (WS) test showed a distribution similar to that determined by the FW test. For the slow wetting (SW) test, 80% or more of the non-grazed grassland soil fragments was > 2 mm. The mean weighted diameter (MWD) that was determined by the Le Bissonnais' method was different among the tests and land uses. For the FW test, all the plant communities were significantly different from that of the recently abandoned grazing on grassland at the 0–20 and 20–40 cm depths. In the Artemisia sacrorum community, the maximum MWD was approximately 3 mm for the 0–20 cm depth. There were no significant differences among the plant communities after 3 years of afforestation and 7 years of non-grazing of the grassland. The MWDs were lower in the afforestation area than in the non-grazed grassland area. The maximum MWD value from the FW test was approximately 1.8 mm and was significantly lower (< 0.5 mm) for cropland. The MWD of the modified Yoder method was positively related to the slow wetting and wet stirring (WS) tests of the Le Bissonnais' method (n = 20 and r = 0.83 and 0.87, respectively). In the Loess Plateau, revegetations by non-grazed grassland and afforestation are efficient means of increasing aggregate stability and decreasing soil erodibility. The aggregate stability under non-grazed grasslands is higher than that under afforestation. The effect of revegetation is persistent, which makes it a suitable long-term management practice. Compared with the modified Yoder's method, the FW test of the Le Bissonnais' method is better at determining aggregate stability differences among land uses and is recommended for future studies.

Abstract The Soil Conservation Service curve number (CN) method commonly uses three discrete levels of soil antecedent moisture condition (AMC), defined by the 5‐day antecedent rainfall depth, to describe soil moisture prior to a runoff event. However, this way may not adequately represent soil water conditions of fields and watersheds in the Loess Plateau of China. The objectives of this study were: (1) to determine the effective soil moisture depth to which the CN is most related; (2) to evaluate a discrete and a linear relationship between AMC and soil moisture; and (3) to develop an equation between CN and soil moisture to predict runoff better for the climatic and soil conditions of the Loess Plateau of China. The dataset consisted of 10 years of rainfall, runoff and soil moisture measurements from four experimental plots cropped with millet, pasture and potatoes. Results indicate that the standard CN method underestimated runoff depths for 85 of the 98 observed plot‐runoff events, with a model efficiency E of only 0·243. For our experimental conditions, the discrete and linear approaches improved runoff estimation, but still underestimated most runoff events, with E values of 0·428 and 0·445 respectively. Based on the measured CN values and soil moisture values in the top 15 cm of the soil, a non‐linear equation was developed that predicted runoff better with an E value of 0·779. This modified CN equation was the most appropriate for runoff prediction in the study area, but may need adjustments for local conditions in the Loess Plateau of China. Copyright © 2006 John Wiley &amp; Sons, Ltd.

Formation of ultra-small nanoclusters with an extremely high oxyanion adsorption capacity in natural and engineered systems with and without ferritin.

Abstract Microplastic (MP) pollution likely affects global soil carbon (C) dynamics, yet it remains uncertain how and to what extent MP influences soil respiration. Here, we report on a global meta‐analysis to determine the effects of MP pollution on the soil microbiome and CO 2 emission. We found that MP pollution significantly increased the contents of soil organic C (SOC) (21%) and dissolved organic C (DOC) (12%), the activity of fluorescein diacetate hydrolase (FDAse) (10%), and microbial biomass (17%), but led to a decrease in microbial diversity (3%). In particular, increases in soil C components and microbial biomass further promote CO 2 emission (25%) from soil, but with a much higher effect of MPs on these emissions than on soil C components and microbial biomass. The effect could be attributed to the opposite effects of MPs on microbial biomass vs. diversity, as soil MP accumulation recruited some functionally important bacteria and provided additional C substrates for specific heterotrophic microorganisms, while inhibiting the growth of autotrophic taxa (e.g., Chloroflexi , Cyanobacteria ). This study reveals that MP pollution can increase soil CO 2 emission by causing shifts in the soil microbiome. These results underscore the potential importance of plastic pollution for terrestrial C fluxes, and thus climate feedbacks.

Biological soil crusts (BSCs, or biocrusts) have important positive ecological functions such as erosion control and soil fertility improvement, and they may also have negative effects on soil moisture in some cases. Simultaneous discussions of the two-sided impacts of BSCs are key to the rational use of this resource. This study focused on the contribution of BSCs while combining with specific types of vegetation to erosion reduction and their effects on soil moisture, and it addressed the feasibility of removal or raking disturbance. Twelve plots measuring 4 m × 2 m and six treatments (two plots for each) were established on a 15° slope in a small watershed in the Loess Plateau using BSCs, bare land (as a control, BL), Stipa bungeana Trin. (STBU), Caragana korshinskii Kom. (CAKO), STBU planted with BSCs (STBU+BSCs) and CAKO planted with BSCs (CAKO+BSCs). The runoff, soil loss and soil moisture to a depth of 3 m were measured throughout the rainy season (from June to September) of 2010. The results showed that BSCs significantly reduced runoff by 37.3% and soil loss by 81.0% and increased infiltration by 12.4% in comparison with BL. However, when combined with STBU or CAKO, BSCs only made negligible contributions to erosion control (a runoff reduction of 7.4% and 5.7% and a soil loss reduction of 0.7% and 0.3%). Generally, the soil moisture of the vegetation plots was lower in the upper layer than that of the BL plots, although when accompanied with a higher amount of infiltration, this soil moisture consumption phenomenon was much clearer when combining vegetation with BSCs. Because of the trivial contributions from BSCs to erosion control and the remaining exacerbated consumption of soil water, moderate disturbance by BSCs should be considered in plots with adequate vegetation cover to improve soil moisture levels without a significant erosion increase, which was implied to be necessary and feasible.

Microbial carbon (C) use efficiency (CUE) describes the proportion of organic C used by microorganisms for anabolic processes, which increases with soil organic C (SOC) content on a global scale. However, it is unclear whether a similar relationship exists during natural vegetation restoration in terrestrial ecosystems. Here, we investigated the patterns of CUE along a 160-year vegetation restoration chronosequence (from farmland to climax forest) estimated by stoichiometric modeling; additionally, we examined the relationship between CUE and SOC content and combined these results with a meta-analysis. The combination indicated that vegetation restoration decreased CUE from 0.35 to 0.28. Surprisingly, SOC content increased with decreasing CUE during vegetation restoration because forest soils have low pH values and high microbial phosphorus limitations compared to early ecosystems, implying that climax forests may not sequester as much soil C as expected. The shift in soil pH was the most important predictor of CUE compared to climate, plant, and microbial factors. CUE changes were directly induced by soil pH and not by the pH-induced microbial community. Alkaline soil acidification tended to decrease CUE. This first large-scale estimate of the relationship between CUE and SOC during natural restoration highlights the need to strengthen C sink management in mature forests to sustain their C sequestration potential.

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot’s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red–green–blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.

The leaf area index (LAI) is a key parameter for describing the canopy structure of apple trees. This index is also employed in evaluating the amount of pesticide sprayed per unit volume of apple trees. Hence, numerous manual and automatic methods have been explored for LAI estimation. In this work, the leaf area indices for different types of apple trees are obtained in terms of multispectral remote-sensing data collected with an unmanned aerial vehicle (UAV), along with simultaneous measurements of apple orchards. The proposed approach was tested on apple trees of the “Fuji”, “Golden Delicious”, and “Ruixue” types, which were planted in the Apple Experimental Station of the Northwest Agriculture and Forestry University in Baishui County, Shaanxi Province, China. Five vegetation indices of strong correlation with the apple leaf area index were selected and used to train models of support vector regression (SVR) and gradient-boosting decision trees (GBDT) for predicting the leaf area index of apple trees. The best model was selected based on the metrics of the coefficient of determination (R2) and the root-mean-square error (RMSE). The experimental results showed that the gradient-boosting decision tree model achieved the best performance with an R2 of 0.846, an RMSE of 0.356, and a spatial efficiency (SPAEF) of 0.57. This demonstrates the feasibility of our approach for fast and accurate remote-sensing-based estimation of the leaf area index of apple trees.

Background and aims - Livestock grazing exclusion was widely used to manage degraded grassland ecosystems, but little is known on the effects of long-term grazing exclusion on aboveground and belowground species diversity of the steppe vegetation in China. Material and methods - The species composition of the aboveground vegetation and the soil seed bank were examined on sites after a 25-year grazing exclusion in a typical steppe on the Loess Plateau, NW China. Key results - Results showed that long-term grazing exclusion significantly improved vegetation cover, biomass and aboveground species evenness. Long-term grazing exclusion significantly increased species richness and seed density in the soil seed bank, but significantly decreased belowground species evenness. The seeds were mainly present in the litter and the topsoil (0-5 cm), accounting for about 76% of the total seed number. Exclusion of grazing significantly decreased seed depletion in soil seed bank from April to July as compared to grazed sites. The Sørensen similarity index between aboveground and belowground species composition was low in the typical steppe, and long-term grazing exclusion did not significantly improve this similarity. Conclusion - Our results suggest that long-term grazing exclusion can significantly improve both aboveground and belowground species diversity in the steppe vegetation of the Loess Plateau, but has little or no effect on the similarity in composition between the two compartments. © 2011 National Botanic Garden of Belgium and Royal Botanical Society of Belgium.

Continuous leaf area index (LAI) observations from global ground stations are an important reference dataset for the validation of remotely sensed LAI products. In this study, a pragmatic approach is presented for evaluating the spatial representativeness of station-observed LAI dataset in the product pixel grid. Three evaluation indicators, including dominant vegetation type percent (DVTP), relative absolute error (RAE) and coefficient of sill (CS), were established to quantify different levels of spatial representativeness. The DVTP was used to evaluate whether the station-observed vegetation type was the dominant one in the pixel grid, and the RAE and CS were applied to quantify the point-to-area consistency for a given station observation and the spatial heterogeneity caused by the different density of vegetation within the pixel, respectively. The proposed approach was applied to 25 stations from the Chinese Ecosystem Research Network, and results show significant differences of representativeness errors at different levels. The spatial representativeness for different stations varied seasonally with different vegetation growth stages due to temporal changes in heterogeneity, but the spatial representativeness remained consistent at interannual timeframes due to the relatively stable vegetation structure and pattern between adjacent years. A large error can occur in MOD15A2 product validation when the representativeness level of station LAI observations is low. This approach can effectively distinguish various levels of spatial representativeness for the station-observed LAI dataset at the pixel grid scale, which can consequently improve the reliability of LAI product validation by selecting LAI observations with a high degree of representativeness.

To address the problems of difficult leveling and poor stability of hill crawler tractors, an attitude adjustment device based on a parallel four-bar mechanism was designed, and the mechanical reasons for the sideslip instability of hill crawler tractors were analyzed. On this basis, a posture adjustment mechanism based on a parallel four-bar mechanism was proposed, and the structure of the complete attitude adjustment device was designed. To ensure that this device meets the strength requirements during operation, a mechanical analysis of the key components (active rocker and slave rocker) was carried out to accommodate the load during leveling. Based on ANSYS software, a finite element simulation analysis was used to determine the maximum stress position of the active and slave rockers. Finally, to verify the accuracy of the above simulation analysis results and determine the influence rules of the lateral slope angle, longitudinal slope angle and loading quality on the abovementioned maximum stress, a physical model test bench of the attitude adjustment device was built. An orthogonal regression experiment was carried out with the maximum stresses of the active and slave rockers as the test indices. The experimental data were analyzed by Design-Expert 10 software, and the results show that the order of the primary and secondary factors influencing the maximum stress of the active rocker was the loading mass, lateral slope angle and longitudinal slope angle. The order of the factors influencing the maximum stress of the slave rocker was the longitudinal slope angle, lateral slope angle and loading mass. The active and slave rockers meet the strength requirements. This work provides technical support for the production of hill crawler tractor physical prototypes.

Drought can impact local vegetation dynamics in a long term. In order to predict the possible successional pathway of local community under drought, the responses of some drought resistance indices of six successional seral species in the semi-arid Loss Hilly Region of China were illustrated and compared on three levels of soil water deficits along three growing months (7, 8 and 9). The results showed that: 1) the six species had significant differences in SOD, POD activities and MDA content. The rank correlations between SOD, POD activities and the successional niche positions of the six species were positive, and the correlation between MDA content and the niche positions was negative; 2) activities of SOD, CAT and POD, and content of proline and MDA had significant differences among the three months; 3) there existed significant interactions of SOD, CAT, POD activities and MDA content between months and species. With an exception, no interaction of proline was found. Proline in leaves had a general decline in reproductive month; 4) SOD, CAT, POD activities and proline content had negative correlations with MDA content. Among which, the correlation between SOD activity and MDA content was significant. The results implied that, in arid or semiarid region, the species at later successional stage tend to have strong drought resistance than those at early stage. Anti-drought indices can partially interpret the pathway of community succession in the drought impacted area. SOD activity is more distinct and important on the scope of protecting membrane damage through the scavenging of ROS on exposure to drought.

□ Root systems play vital roles, inter alia in the acquisition of water and nutrients in all plants, and hence in their growth, physiology and metabolism. However, much remains to be learned about the mechanisms affecting root distributions and uptake efficiencies. These are important aspects to understand in order to optimize water and fertilizer applications, especially in arid areas such as the Loess Plateau of China, where a key crop is winter wheat. Therefore, we have studied the effects of spatial coupling of water, nitrogen and phosphorus applications on winter wheat root growth patterns at different growth stages and in different soil layers in soil columns. Observations by minirhizotrons showed root length, surface area, volume and number to be respectively 18.9, 25.3, 29.8 and 8.0% higher under dry treatments than under wet treatments. Simultaneous application of nitrogen and phosphorus in the 0–90 or 0–30 cm layers promoted extensive spatial distributions of roots, especially in the 0–30 cm layer. In addition, water use efficiency (WUE) was found to be strongly correlated with root length, surface area, volume and number (r 2 = 0.72–0.80, n = 26), but weakly correlated with root biomass (r 2 = 0.3).

Despite field mulching has been widely adopted to improve crop productivity, agro-ecosystem sustainability of this technology in arid and semi-arid areas is inconclusive. We examined the response of yield stability, changes in soil water storage (ΔSWS), nitrogen (N) budget, soil total nitrogen (TN) content and soil organic carbon (SOC) content to furrow-ridge tillage and film or straw field mulching practices in a 6-year field experiment of rain-fed spring maize (Zea mays L.) on the Loess Plateau from 2016 to 2021. Five tillage practices included conventional tillage (CT), ridge tillage (RT), flat planting with plastic film mulching (FM), ridge tillage with plastic film mulching on ridge (RTF) and maize straw mulching (SM). Nitrogen application rates were 0 and 225 kg N ha−1. Maize yield and yield sustainability index (SYI) of plastic film mulching increased by 26.8%− 73.4% and 4.4%− 13.9%, respectively, compared with CT, and coefficient of variation (CV) of plastic film mulching declined by 0.7%− 10.7% relative to that of CT. The average ΔSWS of all tillage patterns were positive, and the rainwater use potential was 88.3–96.9%. The N budget in all practices was positive under N applied treatments but negative without N input. Without N application, N consumption was substantially exacerbated by plastic film mulching, whereas it was mitigated to some extent by SM. The TN and SOC contents were maintained under plastic film mulching. Soil TN and SOC contents in SM increased by 27.1–29.8% and 22.3–33.3%, respectively, in tested stage. However, its yield varied substantially, particularly under no N application. Small yield increase and higher CV and lower SYI occurred in RT practice. The SOC declined similar to CT. Overall, there was higher variation of yield in SM practice. The SOC appeared to decline in RT practice. The plastic film mulching was a promising practice to increase and stabilize maize yields, improve the rainfall use potential, increase or maintain the TN and SOC contents in semi-arid regions. However, attention should be paid to the impact of microplastic contamination in future research.

In the vision system of apple-picking robots, the main challenge is to rapidly and accurately identify the apple targets with varying halation and shadows on their surfaces. To solve this problem, this study proposes a novel, multi-feature, patch-based apple image segmentation technique using the gray-centered red-green-blue (RGB) color space. The developed method presents a multi-feature selection process, which eliminates the effect of halation and shadows in apple images. By exploring all the features of the image, including halation and shadows, in the gray-centered RGB color space, the proposed algorithm, which is a generalization of K-means clustering algorithm, provides an efficient target segmentation result. The proposed method is tested on 240 apple images. It offered an average accuracy rate of 98.79%, a recall rate of 99.91%, an F1 measure of 99.35%, a false positive rate of 0.04%, and a false negative rate of 1.18%. Compared with the classical segmentation methods and conventional clustering algorithms, as well as the popular deep-learning segmentation algorithms, the proposed method can perform with high efficiency and accuracy to guide robotic harvesting.

Poplar plantations play an active role in windbreak and sand-fixation and timber production in water-limited areas, but the large-scale plantations are experiencing degradation, characterized by short trees, small size, and dieback. Moreover, the potential impacts of plantation degradation on ecohydrological processes in soil-plant systems remain unclear. We continuously measured soil water content (SWC), hydrogen and oxygen isotopic compositions in the soil water and plant xylem water, carbon isotopic compositions in the leaf, and sap flow velocity of poplar trees under various degraded plantations (no degraded, ND; lightly degraded, LD; severely degraded, SD) during the 2021 growing season (May–September). We also investigated tree root systems at a depth of 0–200 cm. Our results showed that as plantation degradation intensified, the root weight density at different depths decreased and the root proportion of the shallow layer (0–40 cm) increased. Although the SWC of the shallow layer did not change in the degraded plantations, the SWCs at middle layer (40–80 cm) and deep layer (80–200 cm) were higher in the LD and SD plantations than in the ND plantations, which might be related to reduced transpiration of degraded plantations. The Bayesian mixing model showed that all plantations can shift the water source from shallow to deep layers in the process of soil wetting to drying. Evidence from leaf carbon isotopes suggested that degraded plantations increased the sensitivity of intrinsic water-use efficiency to SWC. Our findings demonstrate that the normal growth of poplar plantations is prone to soil desiccation of deep layers due to high transpiration demand in water-limited areas, and degraded poplar plantations alleviate deep soil water depletion due to low transpiration. For rain-fed poplar plantations, proper thinning and measures of reducing soil evaporation may be necessary to avoid water excess consumption from deep soils.

Abstract Perennial forage legumes, particularly lucerne ( Medicago sativa L.), play a significant role in crop/livestock mixed farming systems in the semiarid region of the L oess P lateau of C hina as stock feed and a source of nitrogen for subsequent crops. However, there is evidence that lucerne reduces soil water deep in the soil profile, thereby reducing subsequent crop productivity. From 2004 to 2010, this study evaluated the forage productivity and water use of two locally adapted perennial legume species, milk vetch ( A stragalus adsurgens P all.) and bush clover ( L espedeza davurica S .), compared with lucerne. The 7‐year total and average annual forage yield of milk vetch were 56 and 8 t ha −1 and bush clover was 42 and 6 t ha −1 , respectively, significantly lower than lucerne at 91 and 13 t ha −1 . However, despite lower water‐use efficiencies (16 and 12 kg ha −1 mm −1 for milk vetch and bush clover, respectively, compared to 22 kg ha −1 mm −1 for lucerne), the total 7‐year water use in milk vetch and bush clover was 3500 mm and 3490 mm, respectively, which was 135–140 mm less than lucerne. After 7 years, lucerne had extracted water from the upper 5 m soil, whereas bush clover used water mainly from the upper 2 m of the soil profile and milk vetch still had some water available below 3 m. We conclude that while the locally adapted forage legumes were not as productive as lucerne as a source of fodder in mixed cropping/livestock system in this region, they use less water, which may be advantageous in drier regions.

is 1 : 2, and the microwave power is 1000 W for 60 s. After 20 min of electrolysis at 20 V, the ammonium nitrogen removal rate can reach 95.30%.