Branch development in the lower part of stem is critical to both early stem growth and wood quality of the most valuable section of tree, and its regulation through planting density has always been greatly concerned. Here the effect of planting density on branch development was examined in a five-year-old plantation of Betula alnoides with six planting densities (625, 833, 1111, 1250, 1667, and 2500 stems per hectare (sph)) in Guangdong Province, South China. Branch quantity (number, proportion, and density), morphology (diameter, length, and angle), position (height and orientation), and branch status (dead or alive) were investigated for 54 dominant or co-dominant trees under six treatments of planting density after the growth of each tree was measured. Factors influencing branch development were also explored by mixed modelling. The results showed that the mean tree heights of 1250 and 1667 sph treatments were higher than those of other planting density treatments. The quantity of live branches decreased with increasing planting density. However, planting density had no significant effect on the number of all branches, and there existed no remarkable difference in branch number and proportion among four orientations. As for branch morphology, only the largest branch diameter had a significantly negative correlation with planting density. In addition, high planting density significantly increased the height of the largest branch within the crown. Mixed effects models indicated that branch diameter, length, and angle were closely correlated with each other, and they were all in positively significant correlation to the branch height at the stem section below six meters. It was concluded that properly increasing planting density will promote natural pruning, improve early branch control, and be beneficial for wood production from the most valuable section of the stem.

To investigate the effect of surface water and underground water drip irrigation on cotton yield, dry matter accumulation and nutrients uptake, two consecutive field experiments were conducted. The first experiment (different mixing ratio irrigation) comprised of five ratios of underground water to surface water including; 1:0 (U), 0:1 (S), 1:1 (U:S = 1:1), 1:2 (U:S = 1:2) and 1:3 (U:S = 1:3). Whereas, the second experiment (round irrigation) comprised of eight treatments including: 1:3 (T₁), 2:2 (T₂), 3:1 (T₃), {S:U 3:1 (T₄)}, 2:2 {S:U (T₅)}, 1:3 {S:U (T₆)}, 4:0 (T₇) and 0:4 (T₈). The average concentration of leaves dry matter after 8^th irrigation in different mixing ratio experiment was significantly increased by 131.2% (S), 34.4% (U: S = 1:1), 59.3% (U: S = 1:2), and 93.7% (U: S = 1:3), respectively, relative to U treatment. Likewise, the stem dry matter increased from 48.5 g (U), to 122.2 g (S) and 101.6 g (U:S = 1:3). The soil available N at 0–20 cm after 8^th irrigation recorded an average increase rate of 40.1%, 6.6%, 13.5%, and 29.5%, respectively. However, at 20-40cm an average increase rate of 37.4% (S), 7.1% (U: S = 1:1), 20.0% (U: S = 1:2), and 21.9% (U: S = 1:3) were noted (p < 0.05). The highest cotton yield of 6571 kg h^-1 was recorded in S treatment compared with the U treatment (5492 kg h^-1), U: S = 1:1 (5502 kg h^-1), U: S = 1:2 (5873 kg h^-1) and U: S = 1:3 (6111 kg h^-1). Contrastingly, in round irrigation experiment the highest leaves dry matter at various growth stages were recorded in T8 treatment. For instance, compared with T7 treatment an average increase rate of 50.6% (growth), 100.9% (boll) and 93.3% (boll opening), in stem dry matter were recorded in T8 treatment. Moreover, the concentration of N in round irrigation at 0–20 cm at different growth stages were 83.3±2.8 (growth stage), 79.01±1.84 (boll stage), and 96.16±3.83 (boll opening stage) in T8. Whereas, in T7 the concentration of N was 36.1±5.9 (growth), 54.51±2.81 (boll), and 53.9±3.83 (boll opening) (p < 0.05). Similarly, cotton yield were substantially higher in T8 applied treatment and follows the sequence of T8 > T1 > T4 > T2 > T5 > T3 > T6 > T7. Overall, our findings provide meaningful information to current irrigation practices in water scarce regions. Improving water use efficiency is a viable solution to the water scarcity. Therefore, surface water irrigation is recommended as an effective irrigation strategies to improve cotton yield and growth.

To address longstanding challenges in sea buckthorn harvesting—such as the absence of effective harvesting principles, inefficient traditional manual and semi-mechanised methods, and rising labour costs—this study developed a self-propelled harvester equipped with a reciprocating cutter. The harvester featured an optimised double-support reciprocating cutter driven by a swing ring mechanism, with its kinematic parameters and cutting speed determined through analytical analysis. A coordinated transport system, consisting of an arc-shaped branch dial wheel, a conveying device, and a hydraulic system, was also designed. Field experiments were conducted employing a three-factor, three-level Box–Behnken design of Response Surface Methodology (RSM), which enabled the establishment of a predictive mathematical model for harvesting performance. Numerical optimisation via the model yielded the optimal operational parameters: harvesting forward speed of 0.6 m·s⁻¹, a cutting speed of 1.2 m·s⁻¹, and a conveyor belt linear speed of 0.8 m·s⁻¹. With this parameter combination, the missed cutting rate was 6.72%, fruit breakage rate 4.06%, and conveyor failure rate 7.79%, all meeting mechanised harvesting standards. This research provides the essential theoretical foundation and technical solutions for harvesting equipment in the sea buckthorn industry, accelerating its mechanisation process.

Introduction: Farmland shelterbelts serve as important ecological barriers that protect agricultural systems. Investigating the effects of shelterbelts with different structural configurations on farmland microclimate is of great significance for optimizing shelterbelt structure and improving farmland ecological stability. Methods: In this study, structural characteristics including the number of rows, length, spacing, and porosity were comprehensively considered. Three types of shelterbelt configurations (Grade I, II, and III) were selected using a typical sampling method in the 12th Regiment of Alar City, Xinjiang, with open farmland serving as the control (CK). Microclimate factors, including wind speed (WS), air temperature (TA), air humidity (RH), light intensity (LI), and soil temperature (ST), were measured at different horizontal distances from the shelterbelt during four cotton growth stages (Seedling Stage, Budding Stage, Flowering Stage, Boll Opening Stage). Results: The results showed that the microclimate-regulating effect of shelterbelts increased with the improvement of shelterbelt grade, generally following the order Grade I > Grade II > Grade III. Among them, Grade I shelterbelts showed the most pronounced wind-speed reduction and hydrothermal regulation effects throughout the whole cotton growth period. The relative wind-speed reduction rate ranged from 72.50% to 97.78%, while the relative interaction index (RII) ranged from −0.164 to 0.010 for LI, from −0.063 to 0.166 for RH, from −0.055 to 0.005 for ST, and from −0.066 to 0.012 for TA. Spatially, wind speed under all three shelterbelt grades reached its minimum at 0.5H and then gradually increased with distance from the shelterbelt. TA was relatively low at 0.5H, and the cooling effect was more pronounced at noon. RH was higher at 1H and gradually approached the control level with increasing distance. LI increased with height in the vertical direction, while its horizontal distribution was relatively stable, with only a slight decrease near the shelterbelt. ST at the 10–15 cm soil layer was lower than that at the 0–10 cm layer, and a low-temperature center occurred horizontally at 2H–3H, with ST generally decreasing as the cotton growth period progressed. Discussion: This study indicates that shelterbelt structures with more rows, better belt continuity, richer tree species composition, and moderate porosity are more conducive to improving the farmland microclimate. These findings provide a theoretical basis for optimizing farmland shelterbelt structure and regulating microclimate in agroforestry systems in arid regions.

The flat peach, an important commercial crop in the 143rd Regiment of Shihezi, China, is overwintered using plastic film mulching. Flat peaches are cultivated to boost the local temperate rural economy. The development of accurate maps of the spatial distribution of flat peach plantations is crucial for the intelligent management of economic orchards. This study evaluated the performance of pixel-based and object-based random forest algorithms for mapping flat peaches using the GF-1 image acquired during the overwintering period. A total of 45 variables, including spectral bands, vegetation indices, and texture, were used as input features. To assess the importance of different features on classification accuracy, the five different sets of variables (5, 15, 25, and 35 input variables and all 45 variables) were classified using pixel/object-based classification methods. Results of the feature optimization suggested that vegetation indices played a key role in the study, and the mean and variance of Gray-Level Co-occurrence Matrix (GLCM) texture features were important variables for distinguishing flat peach orchards. The object-based classification method was superior to the pixel-based classification method with statistically significant differences. The optimal performance was achieved by the object-based method using 25 input variables, with an overall accuracy of 94.47% and a Kappa coefficient of 0.9273. Furthermore, there were no statistically significant differences between the image-derived flat peach cultivated area and the statistical yearbook data. The result indicated that high-resolution images based on the overwintering period can successfully achieve the mapping of flat peach planting areas, which will provide a useful reference for temperate lands with similar agricultural management.

Farmland shelterbelts are important linear vegetation infrastructures in oasis agricultural landscapes. Their accurate extraction is essential for shelterbelt inventory and farmland management, but remains challenging because shelterbelts are narrow, elongated, locally discontinuous, and spectrally similar to croplands, orchards, roadside vegetation, bare soil, and irrigation-related features. This study developed a GF-2-based deep learning workflow for farmland shelterbelt extraction in the 11th Regiment of Alar City, Xinjiang, China. Four representative semantic segmentation models, namely U-Net, U-Net with scSE attention, U-Net++, and DeepLabV3+, were trained and evaluated using four-band GF-2 optical imagery under a unified experimental setting. Model performance was assessed using Precision, Recall, F1-score, Intersection over Union (IoU), overall accuracy, and Kappa coefficient. Patch-level statistical comparison and visual interpretation were further conducted to examine performance differences, shelterbelt continuity, boundary integrity, omission errors, and background confusion. The results showed that U-Net achieved the best overall performance, with a Precision of 94.58%, Recall of 94.77%, F1-score of 94.67%, IoU of 89.88%, overall accuracy of 99.71%, and Kappa coefficient of 0.9452. Compared with U-Net with scSE attention, U-Net++, and DeepLabV3+, U-Net better preserved the continuity and boundary integrity of narrow shelterbelts in regular field-boundary networks. The other models showed varying degrees of omission, boundary fragmentation, or confusion with spectrally similar agricultural objects. The best-performing U-Net model was then applied to the complete study area, and the extracted shelterbelt area was approximately 6.5 km², accounting for about 4.37% of the cultivated land area. These results indicate that GF-2 optical imagery combined with semantic segmentation can support fine-scale farmland shelterbelt mapping in oasis agricultural landscapes. They also show that model evaluation for narrow linear vegetation features should consider not only pixel-level accuracy but also spatial continuity, boundary integrity, and typical error patterns. The proposed workflow provides a practical reference for GF-2-based farmland shelterbelt inventory, high-resolution linear vegetation mapping, and shelterbelt monitoring in arid oasis agricultural landscapes.