Qi Gao

The Changbai Mountain–Liaodong region is a crucial component of the global black soil belt in Northeast China and a significant national grain production base. However, like many high-latitude agricultural regions worldwide, it faces persistent challenges during the spring sowing period, including low soil temperatures and excessive moisture. Therefore, developing region-specific, effective methods of reducing soil moisture and increasing temperature while improving soil fertility is essential for improving agricultural productivity. To this aim, a field experiment was conducted with two factors: a main plot subjected to ridge tillage (RT) and flat tillage (FT) and subplots with biochar (BC) and straw (ST) amendments. A subplot with no amendment (CK) was used as a control. During maize growth, the daily soil temperature and moisture were monitored, and the soil water evaporation rates and physical structure, as well as the maize yield performance, were evaluated. The results showed that biochar and straw application significantly decreased the soil monthly water content by 1.69–2.22% (p < 0.05) in the surface soil layer (0–15 cm) from May to June, with a more pronounced effect under RT. In contrast, biochar application increased soil moisture and water storage from July to September, indicating that the influence of biochar on soil moisture depends on time and field aging processes. Biochar amendment raised the soil maximum temperature by 0.32–0.79 °C in the top 0–15 cm layer, while straw incorporation decreased the minimum soil temperature by 0.11–0.52 °C. The increase in soil temperature was primarily due to the biochar’s darker color, which facilitated solar radiation absorption, while the decrease in soil temperature was caused by the “Wind Leakage Effect” induced by the large particle size of the straw. Biochar and straw incorporation effectively enhanced maize dry matter accumulation by an average of 15.8% and 8.2%, respectively, and grain yield by 13.0% and 7.8%, respectively. Correlation analysis indicates that these increments are primarily due to enhanced soil moisture and available N content during the middle to late stages of maize growth. Therefore, the integration of straw and biochar with high-ridge cultivation is an effective strategy for excessive moisture reduction and warming in spring soil and it also contributes positively to maize yield.

Non-destructive monitoring of chlorophyll content through Soil Plant Analysis Development (SPAD) values is essential for precision agriculture in water-limited regions. However, current estimation methods using spectral information alone face significant limitations in sensitivity and transferability under variable irrigation conditions. While integrating canopy structural parameters with spectral data represents a promising solution, systematic investigation of this approach throughout the entire growth cycle of spring wheat under different irrigation regimes remains limited. This study evaluated three machine learning algorithms (Random Forest, Support Vector Regression, and Multi-Layer Perceptron) for SPAD estimation in spring wheat cultivated in the Hetao Irrigation District. Using a split-plot experimental design with two irrigation treatments (conventional: four irrigations; limited: two irrigations) and five nitrogen levels (0–300 kg·ha⁻¹), we analyzed ten vegetation indices derived from Unmanned Aerial Vehicle (UAV) multispectral imagery, with and without Leaf Area Index (LAI) integration, across six growth stages. Results demonstrated that incorporating LAI significantly improved SPAD estimation accuracy across all algorithms, with Random Forest exhibiting the most substantial enhancement (R² increasing from 0.698 to 0.842, +20.6%; RMSE decreasing from 5.025 to 3.640, −27.6%). Notably, LAI contributed more significantly to SPAD estimation under limited irrigation conditions (R² improvement: +17.6%) compared to conventional irrigation (+11.0%), indicating its particular value for chlorophyll monitoring in water-stressed environments. The Green Normalized Difference Vegetation Index (GNDVI) emerged as the most important predictor (importance score: 0.347), followed by LAI (0.213), confirming the complementary nature of spectral and structural information. These findings provide a robust framework for non-destructive SPAD estimation in spring wheat and highlight the importance of integrating canopy structural information with spectral data, particularly in water-limited agricultural systems.

Weedy rice is an important germplasm source of rice, which has the characteristics of cold-, drought-, and barren tolerance. Taking 88 accessions of weedy rice and 4 varieties of cultivated rice in northern China as test materials, this paper studied the photosynthetic characteristics (photosynthetic rate, transpiration rate, and stomatal conductance), water physiological characteristics, and their interrelationships of weedy rice in northern China. There existed greater differences in the photosynthetic and water physiological characteristics among the weedy rice accessions, possessing abundant diversity. The photosynthetic rate of the accessions was from 12.47 micromol CO2 x m(-2) x s(-1) to 28.67 micromol CO2 X m(-2) x s(-1), and the instantaneous water use efficiency was from 1.39 mg x g(-1) to 3.40 mg x g(-1). Among the photosynthetic parameters, intercellular CO2 concentration had the smallest variable coefficient, while stomatal conductance had the largest one. The photosynthetic rate had significant conic relationships with transpiration rate and stomatal conductance, and had a linear relationship with intercellular CO2 concentration. The significant conic relationships also existed between the instantaneous water use efficiency and the transpiration rate and stomatal conductance. The excellent features of weedy rice could be used to improve the cultivated rice varieties.