To address low productivity and water constraints in lucerne fields of China’s Gansu Yellow River Irrigation Region, this study optimized lucerne (Medicago sativa L.) cultivation through synergistic planting nitrogen regimes. A two-year field trial (2021–2022) evaluated three systems: ridge-furrow with ordinary mulch (PM), ridge-furrow with biodegradable mulch (BM), and conventional flat planting (FP), under four controlled-release N rates (0, 80, 160, 240 kg ha⁻¹). Multidimensional assessments included growth dynamics, dry matter yield, forage quality (crude protein [CP], acid/neutral detergent fiber [ADF/NDF], relative feed value [RFV]), and resource efficiency metrics (water use efficiency [WUE], irrigation WUE [IWUE], partial factor productivity of N [PFPN], agronomic N use efficiency [ANUE]). The results showed the following: (1) Compared with conventional flat planting, ridge planting with film mulching significantly promoted lucerne growth, with ordinary plastic film providing a stronger effect than biodegradable film. Plant height and stem diameter exhibited a quadratic response to elevated nitrogen (N) application rates under identical planting patterns, peaking at intermediate N levels before declining with further increases. (2) Ridge planting with both ordinary plastic film and biodegradable film combined with an appropriate N rate improved lucerne yield and quality. In particular, the PMN2 treatment reached the highest value of yield (14,600 kg ha⁻¹), CP (19.19%) and RFV (124.18), and the lowest value of ADF (29.63%) and NDF (48.86%), and all of them were significantly better than the other treatments (p < 0.05). (3) WUE, IWUE, PFPN, and ANUE followed the pattern PM > BM > FP. With increasing N application rates, WUE, IWUE, and ANUE initially rose and then declined, peaking under N2, whereas PFPN showed a decreasing trend and reached its maximum under N1. Principal component analysis revealed that ridge planting with ordinary plastic film combined with 160 kg·ha⁻¹ N (PMN2) optimized lucerne performance, achieving balanced improvements in yield, forage quality, and water–nitrogen use efficiency. This regimen is recommended as the optimal strategy for lucerne cultivation in the Gansu Yellow River Irrigation Region and analogous ecoregions.

Soil moisture plays a critical role in the global water cycle, the exchange of matter and energy within ecosystems, and the movement of water in plants. Accurate monitoring of soil moisture is essential for drought early warning systems, irrigation decision-making, and crop growth assessment. The use of drone-based multispectral remote sensing technology for estimating the soil moisture content offers advantages such as wide coverage, high accuracy, and efficiency. However, the soil background can often interfere with the accuracy of these estimations. In specific environments, such as areas with strong winds, removing soil background noise may not necessarily enhance the precision of estimates. This study utilizes unmanned aerial vehicle (UAV) multispectral imagery and employs a vegetation index threshold method to remove soil background noise. It systematically analyzes the response relationship between spectral reflectance, spectral indices, and the soil moisture content in the top 0–10 cm layer of alfalfa; constructs K-Nearest Neighbors (KNN), Random Forest Regression (RFR), ridge regression (RR), and XG-Boost inversion models; and comprehensively evaluates model performance. The results indicate the following: (1) The XG-Boost model validation set had the highest R² value (0.812) when spectral reflectance was used as the input variable, which was significantly better than the other models (R² = 0.465 to 0.770), and the RFR model validation set had the highest R² value when the spectral index was used as the input variable (0.632), which was significantly better than the other models (R² = 0.366 to 0.535). (2) After removing soil background noise, the accuracy of the soil moisture estimates for each model did not show significant changes; specifically, the R² value for the XG-Boost model decreased to 0.803 when using spectral reflectance as the input, and the R² value for the RFR model dropped to 0.628 when using spectral indices. (3) Before and after removing the soil background noise, the spectral reflectance can provide more accurate data support for the inversion of the soil moisture content than the spectral index, and the XG-Boost model is the most effective in the inversion of the soil moisture content when using the spectral reflectance as the input variable. The research findings provide both theoretical and technical support for the retrieval of the surface soil moisture content in alfalfa using drone-based multispectral remote sensing. Additionally, they offer evidence that validates large-scale soil moisture remote sensing monitoring.

Plant nitrogen content (PNC) is a core physiological parameter characterizing crop nitrogen nutrition status. Its precise and dynamic monitoring is crucial for crop growth diagnosis, optimizing nitrogen fertilizer management, enhancing fertilizer use efficiency, and reducing agricultural nonpoint source pollution. This study utilized multispectral imagery from unmanned aerial vehicles (UAVs) to extract vegetation indices (VIs) and texture feature values (TFVs) during critical growth stages of alfalfa. By combining TFVs to construct texture indices (TIs), variables exhibiting extremely significant correlations with alfalfa PNC (p < 0.001) were identified. We used VIs, TIs, and their combined features as model inputs. The performance of four machine learning models—random forest regression (RFR), Support Vector Regression (SVR), Backpropagation Neural Network (BPNN), and gradient boosting (XG-Boost)—was comprehensively assessed for estimating alfalfa PNC. Our results indicate the following: (1) The correlation coefficients |r| between VIs and alfalfa PNC ranged from 0.56 to 0.68; TIs constructed from TFVs significantly enhanced PNC correlation compared to raw texture values, with |r| exceeding 0.6. (2) Integrating VIs and TIs substantially improved the accuracy of PNC estimation models across growth stages. Compared to using VIs or TIs alone, the validation set R² increased by 5.4–19.7%, 1.7–16.4%, and 5.2–17.2% for the branching, budding, and initial flowering stages, respectively. (3) The XG-Boost model demonstrated optimal performance across all growth stages and input variables. Particularly during the budding stage, the VIs + TIs model achieved the highest fitting accuracy: training set R² = 0.81, RMSE = 0.15%; validation set R² = 0.80, RMSE = 0.12%. In summary, integrating multispectral vegetation indices and texture indices effectively enhances the accuracy of PNC estimation in alfalfa, providing scientific support for precision field management and fertilization decisions in alfalfa cultivation.

Timely access to soil moisture conditions in farmland crops is the foundation and key to achieving precise irrigation. Due to their high spatiotemporal resolution, unmanned aerial vehicle (UAV) remote sensing has become an important method for monitoring soil moisture. This study addresses soil moisture retrieval in alfalfa fields across different growth stages. Based on UAV multispectral images, a multi-source feature set was constructed by integrating spectral and texture features. The performance of three machine learning models—random forest regression (RFR), K-nearest neighbors regression (KNN), and XG-Boost—as well as two ensemble learning models, Voting and Stacking, was systematically compared. The results indicate the following: (1) The integrated learning models generally outperform individual machine learning models, with the Voting model performing best across all growth stages, achieving a maximum R² of 0.874 and an RMSE of 0.005; among the machine learning models, the optimal model varies with growth stage, with XG-Boost being the best during the branching and early flowering stages (maximum R² of 0.836), while RFR performs better during the budding stage (R² of 0.790). (2) The fusion of multi-source features significantly improved inversion accuracy. Taking the Voting model as an example, the accuracy of the fused features (R² = 0.874) increased by 0.065 compared to using single-texture features (R² = 0.809), and the RMSE decreased from 0.012 to 0.005. (3) In terms of inversion depth, the optimal inversion depth for the branching stage and budding stage is 40–60 cm, while the optimal depth for the early flowering stage is 20–40 cm. In summary, the method that integrates multi-source feature fusion and ensemble learning significantly improves the accuracy and stability of alfalfa soil moisture inversion, providing an effective technical approach for precise water management of artificial grasslands in arid regions.

Background: Amidst the pressing need to balance global food security and climate governance, achieving synergistic optimisation between crop yield enhancement and agricultural greenhouse gas reduction has become the central imperative for advancing the transition to green agriculture. Purpose: To investigate the effects of cropping systems and nitrogen fertiliser application on goji berry production systems in arid regions. Method: This study employed two cropping systems (goji berry–alfalfa intercropping (I), goji berry monocropping (M)), and four nitrogen application rates (N0 (0 kg ha⁻¹), N1 (150 kg ha⁻¹), N2 (300 kg ha⁻¹), N3 (450 kg ha⁻¹)). The effects of planting patterns and nitrogen fertiliser regulation on the physicochemical properties of goji berry farmland soil, greenhouse gas emissions, and yield were analysed. Result: (1) Soil temperatures under I were significantly lower than under M, and nitrogen application levels, cropping systems, and the interaction between nitrogen application and cropping systems significantly influenced soil nutrients; (2) Cultivation patterns and nitrogen application levels exerted a highly significant influence on soil greenhouse gas emission fluxes in goji berry fields. CO₂ emission flux peaked under IN3 treatment (annual average: 342.45 mg m⁻² h⁻¹), while N₂O emissions peaked under MN3 (annual average 0.23 mg m⁻² h⁻¹). CH₄ absorption was highest under MN0 (annual average −0.25 mg m⁻² h⁻¹); (3) Cropping systems and nitrogen application rates significantly influence greenhouse gas indicators including cumulative CO₂ emissions, cumulative N₂O emissions, and GWP. At the same nitrogen application level, GWP decreased by 5.63% on average in M compared to I, while under the same cropping system, N3 increased by 62.45% on average in N3 compared to N0; (4) Cropping systems and nitrogen application levels significantly influenced goji berry yield and economic returns. Under the same cropping system, N2 yielded the highest goji berry production and return on investment, with I and M yielding 2768.99 kg ha⁻¹ and 4.06 and 3067.78 kg ha⁻¹ and 3.15, respectively. Conclusions: The IN2 reduced soil greenhouse gas emission fluxes, cumulative emissions, and global warming potential while simultaneously increasing goji berry yield, net revenue, and return on investment. This approach minimises land resource wastage and represents a management model for achieving high yields with reduced emissions in goji berry fields within the Yellow River diversion irrigation districts of Gansu Province and similar ecological zones.

Rapid and accurate monitoring of soil total nitrogen (STN) is fundamental to optimising nitrogen fertiliser management, improving nitrogen use efficiency, and reducing agricultural non-point source pollution; however, traditional methods struggle to provide timely, large-scale, and continuous monitoring. Based on the relationship between maize canopy spectral characteristics and soil nitrogen distribution, this study acquired multi-spectral drone imagery during the jointing, tasselling, and grain filling stages, whilst simultaneously collecting soil samples from the 0–20, 20–40, and 40–60 cm soil layers. Using canopy reflectance (Band), vegetation indices (VIs), textural features (TF), and a fusion of these three features (Band+VIs+TF) as input variables, machine learning models, including Random Forest Regression (RFR), Gradient Boosted Decision Trees (GBDT), K-Nearest Neighbours (KNN), and XGBoost, were constructed to estimate STN. The results indicate that (1) STN is significantly correlated with spectral reflectance, vegetation indices, and textural features, with absolute correlation coefficients ranging from 0.60 to 0.89; (2) the accuracy of STN estimation by the four models showed a decreasing trend with increasing soil depth, with the highest accuracy observed in the 0–20 cm soil layer; (3) the validation accuracy of STN estimation followed the order: grain filling stage (R² = 0.833) > tasselling stage (R² = 0.794) > jointing stage (R² = 0.761); (4) under feature fusion conditions, the STN estimation accuracy of all four models was superior to that of using a single feature, and the prediction accuracy of the XGBoost model (0.833) was higher than that of the other three models (0.701–0.734). In summary, during the grain filling stage of maize, using feature fusion as input variables and the XGBoost model can significantly improve the accuracy of STN estimation in maize fields, with the 0–20 cm soil layer yielding the best results.