Yongping Zhang

The traditional spring wheat–sunflower relay intercropping system in the Hetao Irrigation District of Inner Mongolia is constrained by restrictions on sowing time, relatively low resource use efficiency, and suboptimal strip configurations. Based on the winter-seeding technique for spring wheat, this study established an annual double-cropping system of winter-seeded spring wheat relay intercropped with sunflower. The objectives were to evaluate crop photosynthetic performance, grain yield, resource use efficiency, and economic benefits under different strip configurations and to identify a suitable strip arrangement. A two-year fixed-site field experiment was conducted from 2023 to 2025. Four winter-seeded spring wheat/sunflower relay-intercropping treatments (W9S2, W9S4, W18S2, and W18S4) were compared with sole-cropped winter-seeded spring wheat and sole-cropped sunflower. Relay intercropping increased leaf area index, SPAD values, and the net photosynthetic rate of both crops at key growth stages and also improved grain yield and resource use efficiency. Among the strip configurations, W18S2 showed the best overall performance. Its two-year average grain yields of wheat and sunflower were 13.2% and 32.2% higher, respectively, than those of the corresponding sole-cropping treatments. The land equivalent ratio and nitrogen uptake equivalent ratio of all relay-intercropping treatments were greater than 1, with the highest values observed under W18S2. In addition, W18S2 had higher light use efficiency, water use efficiency, and nitrogen partial factor productivity than the sole-cropping treatments and the other relay-intercropping configurations. The two-year average net profit of W18S2 was 46.8% and 8.0% higher than that of sole-cropped wheat and sole-cropped sunflower, respectively. Overall, the superior performance of W18S2 was associated with a more favorable canopy structure and photosynthetic performance, greater dry matter accumulation, and coordinated improvements in grain yield, resource use efficiency, and economic benefits. These findings provide a reference for the high-yield and resource-efficient cultivation of winter-seeded spring wheat relay intercropped with sunflower in the Hetao Irrigation District.

Soil salinization and alkalization caused by global climate change have become major constraints on crop production worldwide. Quinoa (Chenopodium quinoa Willd.), owing to its exceptional adaptability to adverse environmental conditions, is considered a promising crop for saline–alkali agriculture. Successful screening and evaluation of saline–alkali-tolerant germplasm at the germination stage is essential for breeding quinoa varieties with enhanced tolerance to compound saline–alkali stress. In this study, 23 quinoa accessions originating from different regions were evaluated for saline–alkali tolerance by exposing seeds to compound saline–alkali solutions at different concentrations in a Petri dish germination assay. During the germination stage, relative vigor index, germination rate, germination energy, shoot length, root length, and fresh weight were determined, and saline–alkali tolerance was assessed using multivariate statistical analyses. The germination ability and seedling growth potential of quinoa germplasm gradually declined with increasing saline–alkali stress. Based on the comprehensive saline–alkali tolerance index at the germination stage, eight accessions were identified as highly tolerant and four accessions as highly sensitive to saline–alkali stress. Among them, LL1 and Z27 exhibited outstanding tolerance and were identified as elite saline–alkali-tolerant germplasm resources. The results indicated that vigor index and fresh weight at the germination stage can serve as key parameters for evaluating saline–alkali tolerance in quinoa germplasm. Overall, this study provides valuable germplasm resources and a practical basis for breeding new quinoa varieties with enhanced saline–alkali tolerance, as well as for the identification and utilization of stress-resistance-related genes in quinoa.

The Hetao Irrigation District in Inner Mongolia is a major spring wheat production region in China. To synergize high wheat yield, water conservation, and carbon emission reduction in this region, a 2023 and 2024 field experiment was conducted. This study systematically analyzed the effects of organic fertilizer substitution for chemical nitrogen (T1:0%, T2:25%, T3:50%, T4:75%, T5:100%) on soil carbon emissions dynamics and carbon footprint of wheat fields, under two irrigation regimes: water-saving irrigation (twice at jointing and heading stages, 2W) and conventional irrigation (four times at tillering, jointing, heading, and grain-filling stages, 4W). The results showed that during the wheat-growing season, soil CO₂ emission rate exhibited a single-peak trend (peak at flowering stage), while cumulative soil CO₂ emission showed a “decrease-increase-decrease” pattern (peak at jointing to heading). At different growth stages, both CO₂ emission and its rate increased with higher organic fertilizer substitution ratios, and were higher under 4W than 2W. Irrigation and substitution treatments significantly affected the total carbon emissions, carbon sequestration, and carbon footprint: total emissions increased with substitution ratios, while sequestration and footprint first increased then decreased; all three indices were higher under 4W than 2W. Regression analysis revealed that maximum net carbon budget was achieved at 21.6–31.7% substitution (1402.3–1879.9 kg ha⁻¹) under 2W, and 31.0–33.8% substitution (2295.5–2822.0 kg ha⁻¹) under 4W. In conclusion, water-saving irrigation (900 m³ ha⁻¹ per application at jointing and heading stages) combined with an optimal organic-nitrogen ratio (1008.0 kg ha⁻¹ organic fertilizer, 193.1 kg ha⁻¹ chemical nitrogen) effectively coordinates water conservation and carbon emission reduction. This study provides a basis for synergizing these goals in Hetao’s wheat production.

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.

Drought stress seriously threatens human food security, and enhancing crops’ drought tolerance is an urgent problem to be solved in breeding. Quinoa is known for its high nutritional value and strong drought tolerance, but its molecular mechanism in response to drought stress is still unclear. In this study, we used drought-tolerant (D2) and drought-sensitive (ZK1) quinoa varieties, and PEG-6000 was used to simulate drought stress in quinoa seedlings. Phenotypic and physiological biochemical indicators were measured during the seedling stage, and LC-MS was used for a metabolite analysis of drought stress to explore the drought tolerance mechanism of quinoa under drought stress. With the intensification of drought stress, chlorophyll content gradually increased, and D2 reached its maximum at W4, an increase of 49.85% compared with W1. The total chlorophyll content, photosynthesis rate, and stomatal conductance of ZK1 were significantly lower than D2 under moderate and severe drought stress. Metabolomic results showed that a total of 1295 positive ion mode (pos) metabolites and 914 negative ion mode (neg) metabolites were identified. Of these, 12(R)-HETE, phosphatidylcholine, monogalactose diester (MGDG), and stachyose up-regulated expression under drought stress. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that unsaturated fatty acid biosynthesis and glycerophospholipid metabolism pathways were significantly enriched. In summary, our results elucidate that quinoa responds to drought stress by accumulating chlorophyll and sugars, activating unsaturated fatty acid metabolism, and protecting the photosynthetic system. These findings provide new insights for the breeding of drought-tolerant quinoa varieties and the study of drought tolerance mechanisms.

Quinoa (Chenopodium quinoa Willd.) is an annual broadleaf plant belonging to the Amaranthaceae family. It is a nutritious food crop and is considered to be drought-tolerant, but drought is still one of the most important abiotic stress factors limiting its yield. Quinoa responses to drought are related to drought intensity and genotype. This study used two different drought-responsive quinoa cultivars, LL1 (drought-tolerant) and ZK1 (drought-sensitive), to reveal the important mechanisms of drought response in quinoa by combining physiological, transcriptomic, and metabolomic analyses. The physiological analysis indicated that Chla/Chlb might be important for drought tolerance in quinoa. A total of 1756 and 764 differentially expressed genes (DEGs) were identified in LL1 and ZK1, respectively. GO (Gene Ontology) enrichment analysis identified 52 common GO terms, but response to abscisic acid (GO:0009737) and response to osmotic stress (GO:0006970) were only enriched in LL1. KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis revealed that glycerophospholipid metabolism (ko00564) and cysteine and methionine metabolism (ko00270) ranked at the top of the list in both cultivars. A total of 1844 metabolites were identified by metabolomic analysis. “Lipids and lipid-like” molecules had the highest proportions. The DEMs in LL1 and ZK1 were mainly categorized 6 and 4 Human Metabolome Database (HMDB) superclasses, respectively. KEGG analysis revealed that the ‘α-linolenic acid metabolism’ was enriched in both LL1 and ZK1. Joint KEGG analysis also revealed that the ‘α-linolenic acid metabolism’ pathway was enriched by both the DEGs and DEMs of LL1. There were 17 DEGs and 8 DEMs enriched in this pathway, and methyl jasmonate (MeJA) may play an important role in the drought response of quinoa. This study will provide information for the identification of drought resistance in quinoa, research on the molecular mechanism of drought resistance, and genetic breeding for drought resistance in quinoa.

Soil salinization is a global agro-ecological problem and a major factor impeding agricultural development. Planting salt-tolerant plants to improve saline soils offers both ecological and economic benefits. Currently, there are few studies addressing the combined effects of salt and alkali stress. Quinoa is known for its salinity tolerance. However, research has predominantly focused on the effects of salinity stress on quinoa’s morphology and physiology, with its molecular mechanisms remaining unclear. To better understand quinoa’s response mechanisms to salinity and alkali stress, we employed RNA-seq technology to analyze transcriptomes under these conditions. We identified 1833 differentially expressed genes (DEGs) under salt stress and 2233 DEGs under alkali stress. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations revealed that quinoa responds to salt and alkali stress through similar mechanisms. Both stresses promoted sucrose synthesis, starch synthesis and catabolism, which increased the osmotic potential of quinoa leaves. Additionally, there was a regulation of the down-regulated expression of the abscisic acid receptor PYR/PYL and the up-regulated expression of the serine/threonine protein kinase (PP2C) gene in the ABA signaling pathway. Contrasting with salt tolerance, the mechanism specific to quinoa’s alkalinity tolerance involves the up-regulation of the citric acid cycle via an active γ-aminobutyric acid (GABA) branch, enhancing quinoa’s energy metabolism. In summary, our transcriptome analysis revealed key regulatory mechanisms in quinoa’s response to saline and alkaline stress. This study deepens the understanding of quinoa’s stress response mechanisms and provides theoretical references for the biological improvement of salinized soils.

The timely and accurate mapping of crop types is crucial for agricultural insurance, futures, and assessments of food security risks. However, crop mapping is currently focused on the post-harvest period, and less attention has been paid to early crop mapping. In this study, the feasibility of using Sentinel-1 (S1) and Sentinel-2 (S2) data for the earliest identifiable time (EIT) for major crops (sunflower, maize, spring wheat, and melon) was explored in the Hetao Irrigation District (HID) of China, based on the Google Earth Engine (GEE) platform. An early crop identification strategy based on the Random Forest (RF) model for HID was proposed, and the performance of the model transfer was evaluated. First, the median synthesis, linear shift interpolation, and the Savitzky–Golay (SG) filter methods were used to reconstruct the time series of S1 and S2. Subsequently, the sensitivity of different input features, time intervals, and data integration to different early crop identifications was evaluated based on the RF model. Finally, the model with optimal parameters was evaluated in terms of its transfer capacity and used for the early mapping of crops in the HID area. The results showed that the features extracted from S2 images synthesized at 10-day intervals performed well in obtaining crop EITs. Sunflower, maize, spring wheat, and melon could be identified 90, 90, 70, and 40 days earlier than the harvest date. The identification accuracy, measured by the F1-score, could reach 0.97, 0.95, 0.98, and 0.90, respectively. The performance of the model transfer is good, with the F1-score decreasing from 0 to 0.04 and no change in EIT for different crops. It was also found that the EIT of crops obtained using S1 data alone was 50–90 days later than that obtained using S2 data alone. Additionally, when S1 and S2 were used jointly, S1 data contributed little to early crop identification. This study highlights the potential of early crop mapping using satellite data, which provides a feasible solution for the early identification of crops in the HID area and valuable information for food security assurance in the region.

The maximum quantum efficiency of photosystem II (Fv/Fm) is a widely used indicator of photosynthetic health in plants. Remote sensing of Fv/Fm using MS (multispectral) and RGB imagery has the potential to enable high-throughput screening of plant health in agricultural and ecological applications. This study aimed to estimate Fv/Fm in spring wheat at an experimental base in Hanghou County, Inner Mongolia, from 2020 to 2021. RGB and MS images were obtained at the wheat flowering stage using a Da-Jiang Phantom 4 multispectral drone. A total of 51 vegetation indices were constructed, and the measured Fv/Fm of wheat on the ground was obtained simultaneously using a Handy PEA plant efficiency analyzer. The performance of 26 machine learning algorithms for estimating Fv/Fm using RGB and multispectral imagery was compared. The findings revealed that a majority of the multispectral vegetation indices and approximately half of the RGB vegetation indices demonstrated a strong correlation with Fv/Fm, as evidenced by an absolute correlation coefficient greater than 0.75. The Gradient Boosting Regressor (GBR) was the optimal estimation model for RGB, with the important features being RGBVI and ExR. The Huber model was the optimal estimation model for MS, with the important feature being MSAVI2. The Automatic Relevance Determination (ARD) was the optimal estimation model for the combination (RGB + MS), with the important features being SIPI, ExR, and VEG. The highest accuracy was achieved using the ARD model for estimating Fv/Fm with RGB + MS vegetation indices on the test sets (Test set MAE = 0.019, MSE = 0.001, RMSE = 0.024, R² = 0.925, RMSLE = 0.014, MAPE = 0.026). The combined analysis suggests that extracting vegetation indices (SIPI, ExR, and VEG) from RGB and MS remote images by UAV as input variables of the model and using the ARD model can significantly improve the accuracy of Fv/Fm estimation at flowering stage. This approach provides new technical support for rapid and accurate monitoring of Fv/Fm in spring wheat in the Hetao Irrigation District.