The northern foothills of Yinshan Mountain are situated in northern China’s agricultural and pastoral ecotone, serving as a crucial ecological barrier. To comprehensively assess the impact of grassland resource restoration in this region since the initiation of the Grain-for-Green conversion project in 2000, this study analyzes the spatiotemporal characteristics of precipitation use efficiency (PUE) and investigates climate-driven factors during 2001–2021. The results showed that the grassland types at the north foot of Yinshan could be divided into four categories: warm-arid, warm subtropical semidesert (WSS), warm temperate-arid, warm temperate zonal semidesert (WZS), warm temperate-semiarid, warm temperate typical steppe (WTS), and warm temperate-subhumid forest steppe (WFT). The NPP of the four grassland species were 151.34 (WSS), 196.72 (WZS), 283.33 (WTS), and 118.06 gC·m⁻² (WFT), and correspondingly, the PUE of the four grassland species were 0.66 (WSS), 0.66 (WZS), 0.80 (WTS), and 0.57 gC·m⁻²·mm⁻¹ (WFT). From 2001 to 2021, PUE in grassland showed an overall upward trend, rising from 0.57 to 0.99 gC·m⁻²·mm⁻¹. The trend analysis found that the vegetation ecological area of the northern foot of Yinshan became better, of which 54.36% was improved and 15.72% was degraded. It is worth pointing out that WSS had the highest degree of improvement, while WFT was in a degraded state. The climate driving force analysis shows that the regional contribution of precipitation is 19.57%, temperature is 28.33%, potential evapotranspiration is 13.65%, wind speed is 10.79%, and saturated vapor pressure is 27.66%.

The analysis of common properties of growth for crops is the basis for further understanding crop growth in different regions. We used four typical crops of China, winter wheat, summer maize, rice, and cotton, to build an integrated model suitable for simulating the growth of different crops. The rates and characteristics of crop growth were systematically analysed based on semirelative and fully relative logistic models of crop growth, and a comprehensive, fully relative logistic model for the four crops was established. The spatial distributions of the maximum leaf area index (LAI_max) and maximum dry-matter accumulation (DMA_max) for the four crops were analysed. The semirelative and fully relative growth models exhibited different characteristics of crop growth. The essential characteristics of growth and the characteristics of the crops at each stage of growth were better represented by the fully relative logistic growth model than by the semirelative model. The comprehensive, fully relative logistic model fitted the growth of all four crops well. LAI_max and DMA_max varied greatly amongst the four crops and were strongly regionally distributed. These indicators for the same crop were differentially spatially variable, and the two indicators were not significantly correlated, except for rice. LAI_max and DMA_max in different regions could be obtained using a binary quadratic equation of water consumption and growing degree days for the crops. This study provides a novel method for quantitatively judging the status of crop growth, predicting crop yields, and planning for regional agricultural planting.

To address the challenges associated with kiwi fruit detection methods, such as low average accuracy, inaccurate recognition of fruits, and long recognition time, this study proposes a novel kiwi fruit recognition method based on an improved lightweight network S-YOLOv4-tiny detection algorithm. Firstly, the YOLOv4-tiny algorithm utilizes the CSPdarknet53-tiny network as a backbone feature extraction network, replacing the CSPdarknet53 network in the YOLOv4 algorithm to enhance the speed of kiwi fruit recognition. Additionally, a squeeze-and-excitation network has been incorporated into the S-YOLOv4-tiny detection algorithm to improve accurate image extraction of kiwi fruit characteristics. Finally, enhancing dataset pictures using mosaic methods has improved precision in the characteristic recognition of kiwi fruits. The experimental results demonstrate that the recognition and positioning of kiwi fruits have yielded improved outcomes. The mean average precision (mAP) stands at 89.75%, with a detection precision of 93.96% and a single-picture detection time of 8.50 ms. Compared to the YOLOv4-tiny detection algorithm network, the network in this study exhibits a 7.07% increase in mean average precision and a 1.16% acceleration in detection time. Furthermore, an enhancement method based on the Squeeze-and-Excitation Network (SENet) is proposed, as opposed to the convolutional block attention module (CBAM) and efficient channel attention (ECA). This approach effectively addresses issues related to slow training speed and low recognition accuracy of kiwi fruit, offering valuable technical insights for efficient mechanical picking methods.

Jujubes (Ziziphus jujuba) are a crucial industry in the arid region of Xinjiang, facing challenges such as water scarcity and low water use efficiency. This study focuses on jujube orchards located at the edge of the Taklimakan Desert to investigate whether applying trace elements can effectively enhance jujube growth, development, and fruit quality. By foliar spraying boron and zinc micro-fertilizers onto jujube leaves, we analyzed the effects of different doses on growth parameters, photosynthetic activity, crop yield, water use efficiency, and fruit quality. The results revealed that the length of the fruit branch, leaf area index, and fruit longitudinal/transverse diameter increased by 19.35%, 25.72%, and 32.9%/2.28%, respectively; net photosynthetic rate, transpiration rate, and stomatal conductance increased by 105.51%, 91.43%, and 75.3%, respectively, while intercellular CO₂ concentration decreased by 13.09%; yield and water use efficiency improved by 16.95% and 12.68%, respectively; soluble sugar content, titratable acid content, and flavonoid content increased by 13.56%, 51.63%, and 86.12%, respectively. Based on these findings, the optimal application rate for boron micro-fertilizer was determined to be 3.51~3.59 kg/hm², and for zinc micro-fertilizer, it was 3.16~3.32 kg/hm². This study provides practical methods along with theoretical support for applying micro-fertilizers in arid regions.

The Chinese Loess Plateau (CLP) is ecologically fragile, and water resources are extremely scarce. Soil thermal conductivity (λ) is a vital parameter for controlling surface heat transfer and is the key to studying the energy exchange and water balance of the soil surface. The objective of this study is to investigate the spatial distribution characteristics of soil thermal conductivity on the Loess Plateau. The research primarily employed soil heat transfer models and the Google Earth Engine (GEE) platform for remote sensing cloud computing, compares and analyzed the suitability of six models (Cambell model, Lu Yili model, Nikoosokhan model, LT model, LP1 model, and LP2 model), and utilized the selected improved model (LT model) to analyze the spatiotemporal characteristics of thermal conductivity on the CLP, examining the impacts of soil particle composition, bulk density, elevation, moisture content, and land use on thermal conductivity. The results show that the LT model is the best in the relevant evaluation indices, with a determination coefficient (R²) of 0.84, root mean square error (RMSE) of 0.18, and relative error (RE) of 0.16. Furthermore, the λ on the CLP shows an overall trend of increasing from northwest to southeast, with a lower λ between May and August and a higher one between September and October. The λ of different land use types is as follows: built-up land > cropland > grassland > forest land > barren. The bulk density (ρ_b) and altitude mainly influence λ in the CLP. The results of this study can provide a theoretical basis for studying hydrothermal variation in the CLP, model application, energy development, and land resource use.

Land use change could significantly affect soil organic carbon (SOC) and other soil chemical properties. However, the responses of soil labile C fractions at different soil depths to land-use change are not still clear. The aim of this study was to investigate the effect of paddy field conversion on woodlands or corn fields on total soil organic C (TOC) and its labile C fractions including particulate organic C (POC), microbial biomass C (MBC), and potassium permanganate-oxidizable C (KMnO₄–C) along a 0–100 cm soil profile. Our results indicate that soil TOC concentrations increased by 3.88 g kg⁻¹ and 3.47 g kg⁻¹ in the 0–5 cm soil layer and 5.33 g kg⁻¹ and 4.68 g kg⁻¹ in the 5–20 cm soil layer during 13 years after the conversion from paddy fields to woodlands and corn fields, respectively. In the 20–40 cm soil layer, the woodlands had the highest TOC concentration (12.3 g kg⁻¹), which was 5.13 g kg⁻¹ and 3.5 g kg⁻¹ higher than that of the paddy and corn fields, respectively. The increase in TOC was probably due to the absence of soil disturbance and greater root residue input into the woodland soil. In corn fields, pig manure addition contributed to the increase in soil organic C concentrations. In addition, the proportion of soil KMnO₄–C increased after conversion from paddy fields to woodlands or corn fields in the 0–40 cm soil layer, ranging from 39.9–56.6% for the woodlands and 24.6–32.9% for the corn fields. The soil POC content was significantly higher in woodland and corn field soils than in paddy field soils at lower soil depths (5–40 cm). However, there were no differences in MBC contents in the whole soil profile between the woodlands and paddy fields. The KMnO₄–C and MBC was the most important factor affecting the CMI values through the whole 0–100 cm soil profile. Overall, converting paddy fields to woodlands or corn fields increased the TOC and labile C fractions in the 0–40 cm soil layer. Future studies should focus on the response of the deeper soil C pool to land-use change.