To address the insufficient characterization of vertical heterogeneity in potato canopy leaf nitrogen content (LNC), this study developed a layer-specific LNC estimation framework based on canopy hyperspectral reflectance, fractional-order derivative (FOD) transformation, and two-band and three-band optimized spectral indices. Partial least squares regression (PLSR) was then used to evaluate the predictive ability of the selected spectral indices for Top, Middle, and Bottom LNC. Field experiments were conducted from 2022 to 2023 in the semi-arid region of Yulin, Shaanxi Province, China. Canopy hyperspectral reflectance from 350 to 1830 nm and LNC measurements of upper (Top), middle (Middle), and lower (Bottom) leaves were synchronously acquired during the tuber formation stage. The results showed that potato canopy LNC exhibited a clear vertical gradient, following the order Top LNC > Middle LNC > Bottom LNC. Traditional vegetation indices were significantly correlated with LNC, but their correlations decreased with increasing canopy depth, with the highest correlation for Bottom LNC being only 0.524. Compared with traditional vegetation indices, FOD-based two-band indices showed stronger Pearson correlations with layer-specific LNC. Under FOD1.5, the maximum absolute Pearson correlation coefficients (|r|) between the selected two-band indices and LNC reached 0.855, 0.849, and 0.814 for Top, Middle, and Bottom LNC, respectively. The three-band optimized spectral indices further enhanced spectral information extraction, with maximum |r| values of 0.893, 0.885, and 0.852, respectively. However, cross-year validation produced substantially lower R2 values, indicating limited temporal transferability of the selected indices and the need for further validation before broader application. Compared with the traditional vegetation index model, it increased the testing-set R2 for Bottom LNC by 0.279 and reduced RMSE from 0.159 to 0.113. These results suggest that FOD1.5-integrated three-band optimized spectral indices can improve the indirect estimation of layer-specific LNC from canopy reflectance, particularly for Bottom LNC, where the reflectance-LNC association is affected by canopy signal attenuation and mixing. The findings provide a methodological reference for describing canopy vertical nitrogen status and functional heterogeneity in potato, while their broader applicability requires further validation across growth stages, cultivars, sites, and nitrogen management conditions.

To enable rapid and non-destructive estimation of potato plant nitrogen content (PNC), this study used hyperspectral data to derive three categories of spectral features, namely Empirical Vegetation Indices (VIs), Spectral Edge Parameters (EPs), and Three-Dimensional Optimal Spectral Indices (3D-OSI). Their correlations with measured PNC were calculated to identify candidate spectral variables sensitive to potato nitrogen status. To avoid relying solely on statistical significance, minimum redundancy maximum relevance (mRMR) analysis was additionally used as a supplementary redundancy check to support feature selection and interpretation. Based on these candidate variables, PNC estimation models were established using Random Forest (RF), Back Propagation Neural Network (BPNN), and Partial Least Squares Regression (PLSR). On the basis of the correlation analysis, the performance of different feature types and their combinations was further compared. The results showed that 3D-OSI had the strongest association with PNC, with the maximum absolute correlation coefficient reaching 0.749, followed by EPs (0.686), whereas the correlations of VIs were relatively weaker (0.510). When each feature category was used separately, 3D-OSI produced the best prediction results, and the RF model based on this feature set achieved an R2 of 0.749. Model performance improved further when different feature types were integrated. Among all input combinations, the RF model using VIs + EPs + 3D-OSI gave the best results, with an R2 of 0.800. Compared with the model developed using VIs alone, the fused-feature-set model increased R2 by 31.58

In arid and semi-arid regions, optimizing field mulching and nitrogen (N) management is crucial for winter oilseed rape production. This three-year study on the Chinese Loess Plateau investigated how mulching modes (film, straw, and no mulching) and N rates interact to influence yield and water productivity through coupled water-light-carbon processes. We developed a coupled "water-light" efficiency index to diagnose leaf physiological status and applied structural equation modeling to quantify driving mechanisms. Results showed that film mulching combined with moderate N (210 kg ha- 1) achieved a robust seed yield of 3762.7 kg ha-1, comparable to the highest N rate but with 25 % less fertilizer input, while attaining superior water productivity of 10.83 kg ha- 1 mm-1. Mechanistically, film mulching acted as a hydrological regulator by reducing evaporation and stabilizing root-zone soil water. This favorable environment allowed moderate N to function as a physiological activator, enhancing leaf chlorophyll content and photochemical efficiency without the excessive water depletion often caused by high N inputs. The proposed coupled index effectively captured this synergy, identifying physiological states with "low water cost and high photochemical return." Structural modeling confirmed that yield formation was driven by a cascade where stabilized soil water promoted leaf physiological function and canopy structure, ultimately maximizing carbon assimilation. Consequently, prioritizing film mulching with moderate N harmonizes water supply and physiological demand, offering a sustainable strategy for dryland agriculture.

Context: Improving crop yield prediction accuracy is crucial for precision agriculture, particularly for irrigation management. Unmanned aerial vehicle (UAV)-based multispectral imaging has become a key tool for crop phenotyping due to its high spatiotemporal resolution and cost-effectiveness. Method: In this study, field experiments were conducted in northwestern China over two consecutive growing seasons (2021-2022), incorporating different mulching practices and supplemental irrigation treatments, to systematically analyze the sensitivity of soybean seed yield to various physiological and growth indices measured at different phenological stages. Results: The results indicated that the full pod stage (R4) was the most sensitive window for yield prediction. At this stage, canopy cover (CC) and chlorophyll content reached their peak values. Most vegetation indices (VIs), texture features (TFs), and texture indices (TIs) extracted from the UAV imagery showed significant correlations (P < 0.05) with final seed yield. Among these, the ratio texture index (RTI, defined as DIS1/HOM3) exhibited the strongest correlation with yield (R = 0.69). A three-source data fusion framework combining VIs, TFs, and TIs was constructed, and an extreme gradient boosting (XGBoost) algorithm was applied to optimize feature weighting. This integrated model achieved optimal performance at the R4 stage, with coefficient of determination R-2 = 0.83 on the validation set, root mean square error (RMSE) = 280.80 kg ha(-)(1) , and mean relative error (MRE) = 6.32 %. Compared to a model based solely on spectral VIs (R-2 = 0.63), the multi-source XGBoost model improved R-2 by 31.7 % and reduced the error metrics (RMSE) by up to 17.5 %. Conclusions: These findings provides a theoretical basis for precise field management in arid areas and a technical framework for remote sensing monitoring of crop yield.

Unified and management-oriented nitrogen (N) diagnosis in mulched dryland crops requires a physiologically consistent baseline that can support consistent interpretation across tested management scenarios and be retrieved non-destructively. To address this, we conducted multi-season field experiments on winter oilseed rape (Brassica napus L.) on the Loess Plateau using three mulching treatments (no mulching, straw mulching, and film mulching) and five N rates (0-280 kg N ha(-1)). Specifically, we aimed to integrate Bayesian physiological modeling with hyperspectral sensing. Aboveground dry matter (DM), leaf N concentration (LNC), canopy hyperspectral reflectance, and seed yield were measured across key growth stages. A Bayesian framework was used to estimate the critical N dilution relationship and quantify parameter uncertainty, supporting pooling across years and mulching treatments (posterior probability of practically negligible differences > 0.95). The resulting unified critical curve was LNCc = 35.732 & times; DM-0.15, enabling a consistent nitrogen nutrition index (NNI) threshold interpretation across mulching scenarios. To enable rapid diagnosis, canopy spectra were transformed using 0.8-order fractional derivatives, and tri-band three-dimensional spectral indices were optimized to predict NNI with machine learning. The best model (XGBoost) achieved R-2 = 0.682 with RMSE = 0.092 on a held-out validation set. Agronomically, film mulching increased mean seed yield by similar to 31.6% relative to no mulching, and yield response exhibited a clear N plateau: FMN3 produced yields only 1.05%-1.74% lower than FMN4 while reducing N input by 25%. The spectral NNI showed a consistent linear-plateau relationship with relative yield, with the plateau occurring near NNI approximate to 1.0. Overall, combining Bayesian N-c-NNI standardization with hyperspectral NNI retrieval provides a unified, management-oriented pathway for N diagnosis within the tested multi-year mulching conditions; however, further external validation across sites, cultivars, and independent years is still required, together with additional assessment of phenology-specific spectral bias, canopy vertical N redistribution, and uncertainty associated with large 3D-OSI feature screening.

Soil and plant analyser development (SPAD) is a key indicator of plant nutritional status and nitrogen stress, reflecting crop growth. During potato tuber formation, multispectral and thermal infrared sensors were used to monitor leaf chlorophyll content. Four data sources—texture indices (TIs), vegetation indices (VIs), thermal infrared vegetation indices (TVIs), and texture features (TFs)—were analysed for correlation with SPAD values. The correlation coefficient was calculated, and the feature variables were screened. Then, the selected features were randomly combined with the ground measured data to construct random forest (RF), support vector machine (SVM), and partial least squares regression (PLSR) models. Results showed that among TIs, the ratio texture index (RTI) had the highest correlation with SPAD (R = 0.703). Among VIs, the visible light difference vegetation index (VDVI) correlated best (R = 0.576). Among TVIs, normalised canopy temperature (NRCT) showed the strongest correlation (R = 0.640). Nearly half of TFs reached significant levels (P < 0.01). VIs provided the highest accuracy (R2 = 0.741) in chlorophyll monitoring, with TIs improving prediction accuracy by up to 17.81

Timely acquisition of the leaf Area Index (LAI) and Aboveground Biomass (AGB) is essential for accurately assessing crop growth and yield. This study focuses on the remote sensing monitoring of potato LAI and AGB using spectral parameters derived from multispectral imagery captured by unmanned aerial vehicles (UAVs). Potatoes in the tuber formation stage serve as the research subject, and machine learning techniques are applied to Reinforce the precision of LAI and AGB estimation. A novel three-dimensional texture indices (TTIs) was developed by incorporating crop dimensional information. Prediction models for LAI and AGB were constructed using Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), integrating texture features (TFs), vegetation indices (VIs), and texture indices (TIs). The results indicated that TTIs improves estimation accuracy compared to using TFs, VIs, or TIs individually. The highest level of predictive accuracy was achieved by combining VIs, TFs, and TTIs, with the XGBoost model demonstrating superior performance. Under the optimal input combination, the coefficient of determination (R2) for LAI and AGB reached 0.868 and 0.855, respectively. The root mean square errors (RMSE) were 0.135 and 0.322 kg hm⁻2, while the mean relative errors (MRE) were 7.578

Assessing crop nitrogen status is crucial for optimizing fertilization strategies and promoting sustainable production. Although hyperspectral data offer significant advantages for monitoring subtle physiological changes in crops, accurately determining nitrogen status based on spectral information remains challenging. In this study, field experiments were conducted during the jointing stage of winter wheat on the Loess Plateau from 2018 to 2020. Concurrent measurements of leaf nitrogen concentration (LNC) and hyperspectral reflectance were collected to derive three types of spectral parameters: traditional vegetation indices, two-dimensional optimal spectral indices, and three-dimensional optimal spectral indices. Spectral parameters exhibiting a significant correlation with LNC (p < 0.05) were selected and combined as inputs for three machine learning models—extreme learning machine (ELM), back-propagation neural network (BPNN), and random forest (RF)—to develop LNC estimation models. The results demonstrated that, among the traditional indices, the Double Difference Index (DDn) showed the strongest correlation with LNC (r = 0.674). Within the multidimensional optimal indices, the differential three-dimensional scattering index (DTSI) exhibited the highest sensitivity to LNC (r = 0.721) at wavelength combinations of 833 nm, 755 nm, and 802 nm. Moreover, Model Input Combination 5 (comprising empirical indices plus three-dimensional optimal indices) further enhanced estimation accuracy. The RF model using Combination 5 achieved the best performance on the validation set (R2 = 0.827, RMSE = 2.803 mg g−1, MRE = 7.664%), significantly outperforming other model–input combinations. This study confirms the feasibility and high accuracy of winter wheat LNC inversion using novel multidimensional spectral indices and provides a new approach for real-time, non-destructive monitoring of nitrogen status in winter wheat.

Leaf area index (LAI) serves as a critical indicator for evaluating crop growth and guiding field management practices. While spectral information (vegetation indices and texture features) extracted from multispectral sensors mounted on unmanned aerial vehicles (UAVs) holds promise for LAI estimation, the limitations of single-texture features necessitate further exploration. Therefore, this study conducted field experiments over two consecutive years (2021–2022) to collect winter oilseed rape LAI ground truth data and corresponding UAV multispectral imagery. Vegetation indices were constructed, and canopy texture features were extracted. Subsequently, a correlation matrix method was employed to establish novel randomized combinations of three-dimensional texture indices. By analyzing the correlations between these parameters and winter oilseed rape LAI, variables with significant correlations (p < 0.05) were selected as model inputs. These variables were then partitioned into distinct combinations and input into three machine learning models—Support Vector Machine (SVM), Backpropagation Neural Network (BPNN), and Extreme Gradient Boosting (XGBoost)—to estimate winter oilseed rape LAI. The results demonstrated that the majority of vegetation indices and texture features exhibited significant correlations with LAI (p < 0.05). All randomized texture index combinations also showed strong correlations with LAI (p < 0.05). Notably, the three-dimensional texture index NDTTI exhibited the highest correlation with LAI (R = 0.725), derived from the spatial combination of DIS5, VAR5, and VAR3. Integrating vegetation indices, texture features, and three-dimensional texture indices as inputs into the XGBoost model yielded the highest estimation accuracy. The validation set achieved a determination coefficient (R2) of 0.882, a root mean square error (RMSE) of 0.204 cm2cm−2, and a mean relative error (MRE) of 6.498%. This study provides an effective methodology for UAV-based multispectral monitoring of winter oilseed rape LAI and offers scientific and technical support for precision agriculture management practices.

Accurately assessing root-zone soil moisture is crucial for precision irrigation, as it directly influences crop yield. The Temperature-Vegetation Index (Ts-VI) Feature Space, which combines land surface temperature (Ts) and vegetation index (VI), is widely used to evaluate root-zone soil moisture in vegetated areas. However, its effectiveness in estimating crop yield remains unclear. Therefore, the objectives of this study are: (1) to collect multispectral and thermal infrared remote sensing data from a two-year (2021-2023) field experiment on winter oilseed rape (Brassica napus L.), and to optimize and evaluate the fitting methods of the dry and wet edges of the Ts-VI feature space based on the selected vegetation indices; (2) to analyze the spatiotemporal patterns of the Temperature Vegetation Dryness Index (TVDI) derived from the optimized Ts-VI feature space and estimate root-zone soil moisture (SM) and crop yield; and (3) to precisely invert the SM and yield of winter oilseed rape in the 0-60 cm root-zone using three machine learning algorithms-Support Vector Regression (SVR), Extreme Gradient Boosting Regression (XGBR), and Random Forest Regression (RFR)-based on the optimized TVDI. Results indicate that, among the various fitting methods, the polynomial fitting method shows the best performance. The performance of the root-zone soil moisture prediction models across different growth stages follows the order of budding stage > seedling stage > flowering stage, and with the increase of soil depth, the performance of the model gradually deteriorates.In the yield inversion of winter oilseed rape, TVDI effectively predicts yield, with the coefficient of determination (R-2) ranging from 0.430 to 0.480 and RMSE ranging from 213.399 to 267.212 kg ha(-1) during the seedling stage, R-2 ranging from 0.640 to 0.747 and RMSE ranging from 110.712 to 178.133 kg ha(-1) during the budding stage, and R-2 ranging from 0.680 to 0.773 and RMSE ranging from 83.815 to 147.301 kg ha(-1) during the flowering stage. The flowering stage effectively reflects crop yield trends and allows for accurate yield prediction of winter oilseed rape up to two months in advance. A comparison of the modeling results from XGBR, SVR, and RFR shows that XGBR provides the best fit for both root-zone soil moisture and yield predictions. Compared to linear regression models, the three machine learning models significantly improve accuracy and fit, providing more precise evaluations of root-zone soil moisture and yield. In addition, through the comparison and verification of this method in other regions, it shows that the results also have certain reference value. The combination of the Ts-VI feature space and machine learning algorithms not only enables precise monitoring of root-zone soil moisture conditions but also predicts future crop yield trends, offering valuable insights for water resource management and irrigation decision-making in precision agriculture.

【Objective】The aim of this study was to optimize the water and fertilizer management of peppers, and to investigate the effects of the coupling of irrigation frequency and nutrient solution supply on the quality, yield, water use efficiency and fertilizer partial productivity of peppers grown in substrate bags.【Method】Kailai (37-83) RZ F1 pepper was chosen as the material in the study, the irrigation amount (IA) required to maintain the water content of the substrate at 55%-60% was set as the total daily IA of single plant, and three irrigation frequencies (IF) of single plant were to supply IA according to 1 time (IF1), 2 times (IF2) and 4 times (IF3), respectively, and two nutrient solution supply amounts (NS) were the standard Yamazaki pepper nutrient solution (NS1, i.e. 250 mL/plant per day and 500 mL/plant per day during the flowering to triple layer harvesting period and after the triple layer harvesting, respectively) and the increasing nutrient solution (NS2, i.e. the initial nutrient solution supply was 250 mL/plant per day, after each layer of pepper was harvested, the nutrient supply of single plant was increased by 50 mL, and did not increase until it increased to 500 mL/plant), for a total of six coupled treatments. The principal component analysis-technique for order preference by similarity to an ideal solution (PCA-TOPSIS), membership function analysis and grey relational degree analysis were used to comprehensively evaluate fruit quality, yield, water use efficiency and fertilizer partial productivity.【Result】The IF had a significant effect on all quality indicators except shoulder length (P<0.01); the NS had a significant effect on vitamin C, soluble protein, capsaicin and dihydrocapsaicin (P<0.01), but had no significant effect on other quality indicators; the coupling of IF and NS showed highly significant effect on the quality indicators, except the thickness of the peel (P<0.01). At the same time, IF, NS and their coupling showed extremely significant effects on pepper yield and water use efficiency (P<0.01). The evaluation results were consistent by PCA-TOPSIS, fuzzy membership function and grey relational degree, and the top two were IF1NS1 and IF2NS2. IF1NS1 treatment had the best fruit quality of pepper, for yield, water use efficiency, and N, P, K fertilizer partial productivity were the highest, with the value of 74 482.24 kg∙hm-2, 34.21 kg∙m-3, 625.95 kg∙kg-1, 679.54 kg∙kg-1, and 367.23 kg∙kg-1, respectively. Therefore, IF1NS1 was the optimal water-fertilizer coupling treatment.【Conclusion】The optimal IF and NS management of peppers grown in substrate bags were as follows: IF of single plant was to maintain the water content of the substrate at 55%-60% required IA was supplied according to 1 time, the standard Yamazaki formula nutrient solution of 250 mL/plant per day and 500 mL/plant per day was supplied from the flowering to triple layer harvesting period and after the triple layer harvesting, respectively.

为合理优化设计寒区弧底梯形渠道的形体结构,基于冻土水热力三场耦合及冻土-衬砌相互作用的渠道冻胀数值模型,结合分层序列法将渠道断面水力参数最优解集作为变量空间,以衬砌结构的强度、刚度及几何构造为约束条件,以衬砌适应冻胀变形能力为目标函数,建立寒区弧底梯形渠道水力-抗冻胀双优数学模型.随后对寒区各类典型渠道工程进行双优结构标准化设计,得到弧底梯形渠道在不同负温、地下水埋深、土质、断面规模下的双优边坡系数、实佳比及衬砌厚度的标准参数,供工程设计参考.研究结果表明:双优结构尺寸参数均随地下水埋深的增大而减小、随基土冻胀率的增大而增大、随冬季负温的降低而增大、随断面规模的增大而增大.以新疆某渠道工程为例,优化后的断面实佳比为1.02,较原设计值增大1.8%;最大法向冻胀量为2.52 cm,较原设计值增大20.0%;最大拉应力为0.95 MPa,较原设计值减小60.4%;衬砌整体柔度为305.47 cm/MPa,较原设计值增大42.3%,水力性能及抗冻胀性能均达到最优.

Water is the key factor limiting crop production in irrigation water areas with high electric conductivities (ECs) in Northwestern China, where water scarcity and poor quality challenge food security and environmental sustainability. The accurate and efficient management of irrigation and fertilizer is important to obtain oriental melon ( Cucumis melo L.) yield and to satisfy ecological water requirements in irrigation water areas with high-ECs. In this study, we evaluated the effects of irrigation amount ( W ), nutrient solution EC ( E ), and irrigation frequency ( F ) on melon growth and biomass, nutrient absorption ability, nutrient utilization efficiency, water use efficiency (WUE), fertilizer use efficiency (FUE), fruit quality, and yield. Specifically, we applied a quadratic orthogonal rotation combination design with three experimental factors at five levels (− 1.68, − 1, 0, 1, and 1.68) for a total of 23 treatments over two growing seasons during spring (2021S) and autumn (2020A). An entropy weight method was constructed, including 7 factors and 20 subfactors, to confirm representative indexes. After the values of representative indexes were normalized, the comprehensive evaluation system of oriental melon was established by the technique for order of preference by similarity to ideal solution method and the construction of structural equation models for the representative indexes of different factor pairs. W is the first factor for oriental melon’s integrated growth. Integrated growth, WUE, and FUE increased with W and exhibited a rise and subsequent fall with EC and F in 2020A, as well as with irrigation and nitrogen levels in 2021S. The nutrient utilization efficiency and fruit quality showed an “increase–decrease” trend with the increase in W and F and a decrease trend with the increase in E in 2020A and 2021S. In areas with high-EC irrigation water, a low-EC nutrient solution was found to be more suitable than a high-EC nutrient solution. When E was 3.53 mS/cm, F of seven times per day and the W values of 0.73 L/plant/day for autumn and 1.68 L/plant/day for spring were suitable for melon production. When E was 3.2 mS/cm, W of 1.2 L/plant/day and F of seven times per day were suitable for annual production. These irrigation schedules will greatly improve the WUE and yield of oriental melon, supporting the rational allocation of water resources of high-EC irrigation water regions in China.

This study aimed to obtain an irrigation strategy for high-quality melon production in a high electric conductivity (EC) value of irrigation areas. The experiment was conducted in Yan'an Shaanxi Province, China, from August 2020 to October 2020. The effects of different irrigation amounts (W), E (EC of a nutrient solution), and frequencies (F) of a nutrient solution on the yield and flavor quality of the oriental melon cultivar 'Qingnong Cuibao' cultivated in plastic bags were investigated. Random forest was used to analyze the aromatic substances of melon in the whole growth period and principal component analysis (PCA) was used to obtain the best treatment for melon yield and aromatic substances. Results showed that the main aromatic substances were ethyl acetate, ethyl butyrate, hexanal, octanal, and 2-nonenal during the growth period of melon. The melon fruit ripening stage was the main period of the accumulation of aromatic substances. The W, E and their interaction had substantial effects on the content of esters and yield (P < 0.05). The W had greater an effect on aromatic substances and yield than E and F. PCA showed that the optimal water and fertilizer strategy was to irrigate 1.68 L/plant nutrient solution with 3.53 mS/cm EC for 7 times one day. The results of this experiment could provide a reference for melon production in irrigation areas with a high E value.

Parabolic canals are commonly used in seasonally frozen regions because of their excellent hydraulic characteristics and frost heave resistance. However, severe frost damage can still occur, mainly because the design of canals is based on experience, and the analytical mechnical models and design methods are limited. First, the frost heave characteristics of parabolic canals were analysed, and the canal lining was simplified as an arched thin shell structure under the normal frost heave force, tangential freezing force, and gravity. Second, an analytical mechanical model of frost damage to the lining was established, and the equations for the axial force, bending moment, tensile stress were derived; the accuracy of the model was verified by field measurements and numerical simulations of a canal case. Third, the effects of groundwater depth and canal size were analysed, and the frost damage mechanism was investigated. Fourth, the anti-frost heave design method of a canal based on the hydraulic optimal solution set was proposed, and the method was encapsulated into digital design software with Python language. This mechanical model can explain the frost damage of the canal, and the nonuniform frost heave of foundation soil caused by different groundwater depths in different parts of the canal is the cause of lining damage. The bi-objective optimization design method can consider the hydraulic performance and anti-frost heave performance of the canal, and the digital design software can assist engineers with developing a quick and accurate design approach. The research results can provide a theoretical basis and technical means for cold-region canal design.

Optimising fertilisation is an important part of maximising vegetable yield and quality whilst minimising environmental hazards. An accurate and efficient scheme of irrigation and fertiliser based on plants’ nutrient requirements at different growth stages is essential for the effective intensive production of greenhouse pepper (Capsicum annuum L.). In this study, the effects of reducing fertilisation rate by 20%, 40%, 60% and 80% from the day 6 to day 0 before harvest for each layer of peppers on growth, yield, quality and nutrient utilisation were evaluated. The results showed that the morphological indicators (plant height and stem diameter) and biomass of plants decreased gradually with the increase in fertiliser reduction rate. Compared with control (CK) plants, the 20–40% reduction in fertiliser application rate did not cause a significant decrease in biomass and stem diameter but significantly increased the accumulation of N (13.52–15.73%), P (23.09% in 20% reducted-treatment) and K (13.22–14.21%) elements in plants. The 20–80% reduction in fertiliser application before harvest had no significant effects on the nutrient agronomic efficiency of N, P and K elements. However, it decreased the physiological nutrient efficiency and significantly improved the nutrient harvest index of N, P and K. Appropriate reduction in fertiliser application significantly increased the nutrient recovery efficiency (20–40% reduction) and nutrient partial-factor productivity (40% reduction) of N (3.35–6.00% and 12.87%), P (2.47–2.92% and 14.01%) and K (7.49–15.68% and 14.01%), respectively. Furthermore, reducing the fertilisation rate by 20–40% before each harvest had a certain positive effect on the C and N metabolism of pepper leaves and fruits. In particular, the activities of N metabolism-related enzymes (nitrate reductase, nitrite reductase, glutamine synthase, glutamate synthase and glutamate dehydrogenase) and C metabolism-related enzymes (sucrose phosphate synthase, sucrose synthetase, acid invertase and neutral invertase) in leaves and fruits did not significantly different or significantly increased compared with those in CK plants. The results of the representative aromatic substance contents in the fruit screened by the random forest model showed that compared with the CK plants, reducing the fertiliser application by 20–40% before harvest significantly increased the content of capsaicin and main flavour substances in the fruit on the basis of stable yield. In summary, in the process of pepper substrate cultivation, reducing the application of nutrients by 40% from the day 6 to day 0 before each harvest could result in stable yield and quality improvement of the pepper. These results have important implications for institutional precision fertilisation programs and the improvement of the agroecological environment.

[目的]筛选适宜在陕北地区进行有机基质栽培的优质高产薄皮甜瓜品种,为该地区的甜瓜产业发展提供参考.[方法]以14个薄皮甜瓜品种(青农甜宝、玲珑黄、日本甜宝、青农翠宝、京玉352、凌甜绿冠、gk-03、ze-04、中原9016、金帝、冰糖王子、泽甜四号、吉创20B、绿博特)为试材,比较分析不同品种甜瓜果实的可溶性蛋白、维生素C、可溶性总糖、有机酸、游离氨基酸、可溶性固形物、固酸比、糖酸比、果肉厚度、单果质量、果形指数、坐果数等12项指标,并对各指标的相关性进行Pearson分析,运用主成分分析法综合评价甜瓜品质指标,确定薄皮甜瓜品质性状的决定因子和指标;采用优劣解距离法(TOPSIS)对薄皮甜瓜产量、代表性品质指标进行综合评判,筛选适合陕北地区有机基质栽培的薄皮甜瓜品种.[结果]不同品种薄皮甜瓜营养品质指标均存在较大差异,利用主成分分析法提取4个主成分,其累计方差贡献率达86.65％;薄皮甜瓜营养品质的主要决定因子为风味因子、抗氧化因子、食用因子和合成因子,对应的指标分别为固酸比,糖酸比,维生素C,果肉厚度和游离氨基酸含量.通过主成分分析筛选得分排名前5的品种分别为中原9016(1.92)、吉创20B(1.28)、金帝(0.62)、泽甜四号(0.59)、日本甜宝(0.24);通过 TOPSIS综合分析产量品质,筛选出排名前5的甜瓜品种分别为中原9016(0.77)、吉创20B(0.61)、金帝(0.47)、泽甜四号(0.41)和冰糖王子(0.36).[结论]中原9016、吉创20B、金帝和泽甜四号薄皮甜瓜品种产量、品质综合性状较优,适宜在陕北地区进行有机基质栽培.

为了构建陕北地区日光温室袋培辣椒精准水肥管理模式,本试验以拉菲78-9为试材,采用基质袋培方式,以单株需水量为标准,设置每天供应1次、每2 d供应1次、每3 d供应1次、每3 d供应6次和每3 d供应4次5个营养液供应频率,研究不同营养液供应频率对日光温室越冬茬袋培辣椒产量、果实品质及水分利用效率的影响.结果表明,每3 d供应6次营养液处理和每天供应1次营养液处理的辣椒产量较高,分别为10161.66、10062.98 kg·hm-2;每天供应1次营养液处理的果实还原糖、VC、游离氨基酸含量均高于其他处理,且该处理的水分利用效率亦最高(77.99 kg·m-3).采用Topsis综合评价法对辣椒产量、果实品质、水分利用效率进行评价,结果表明贴合度以每天供应1次营养液处理最高,达到0.858.综上,陕北地区日光温室越冬茬袋培辣椒,每天供应1次营养液的综合效果最好.

[目的]筛选适合陕北地区基质栽培的高品质樱桃番茄品种,解决该地区适宜基质栽培的樱桃番茄品种缺乏的问题.[方法]以15个樱桃番茄品种为试材,测定各品种果实的横径、纵径、果形指数、硬度、单果质量、单株产量、糖酸比及可溶性固形物、还原糖、可滴定酸、维生素C、番茄红素、可溶性总糖含量等13项指标,比较各品种间上述指标的差异,并对各指标的相关性进行Pearson分析,运用主成分分析法、模糊隶属函数综合评价法,对樱桃番茄各产量和品质指标进行评价,从中筛选出适合陕北地区基质栽培的樱桃番茄品种.[结果]不同樱桃番茄品种间各指标均存在显著差异,变异系数为13.60％～54.15％.可溶性固形物含量与可溶性总糖、还原糖含量呈极显著正相关,与果实纵径、横径呈极显著负相关;维生素C含量与番茄红素、可滴定酸含量呈极显著正相关;可滴定酸含量与糖酸比、果实纵径、果形指数呈极显著负相关;单果质量与果实硬度呈显著正相关,与可溶性固形物含量、糖酸比呈极显著负相关;单株产量与可滴定酸含量呈显著正相关.利用主成分分析法提取出4个主成分,其累积方差贡献率达83.408％o,其中影响樱桃番茄综合品质的因子分别为质量因子(单果质量、单株产量)、果形因子(纵径、果形指数)、营养因子(维生素C和番茄红素含量)和口感因子(可滴定酸含量).运用主成分分析法筛选出排名前5的品种为黄金贝(0.662)、浙樱粉1号(0.526)、红玉(0.483)、粉贝贝(0.374)和粉佳人(0.334);运用模糊隶属函数综合评价法筛选出综合排名前5的品种为红玉(0.638)、粉佳人(0.587)、紫贝贝(0.551)、浙樱粉1号(0.539)和黄金贝(0.504).主成分分析和模糊隶属函数综合评价结果基本一致.[结论]黄金贝、粉佳人、浙樱粉1号、红玉等4个樱桃番茄品种的综合品质优于其他品种,适于陕北地区基质栽培.

基于1998—2013年的SPOTVEG NDVI数据，利用像元二分法和面板校正标准误（PCSE）估计方法，分析陕西省植被覆盖时空变化特征以及退耕还林工程、降雨水平等自然、社会经济因素对植被覆盖变化的相对贡献，探究退耕还林工程对植被恢复的驱动效应。结果表明：① 陕西省植被覆盖总体呈增加趋势，1998—2013年年均归一化植被指数（NDVI）值增长0.98%，且在退耕还林工程实施阶段（1998—2009年），植被恢复效果更为明显。② 陕西省植被恢复效果空间分异显著，16a间，各地区NDVI值上升幅度不同，40.61%的区域提升约0.1，29.05%的区域提升约0.2，23.69%的区域提升0.25以上。其中，位于陕北退耕还林区的延安市植被恢复效果最为明显，NDVI年增长约为1.12%。③ 退耕还林工程对植被恢复影响显著，退耕还林面积占土地总面积每提升1%将使NDVI值增加1.97%，相当于降水量增加了4.29%，这对于降水量少且不均的陕西省生态环境建设具有重要参考意义。