Crop nitrogen is an efficient index for estimating crop yield. Using hyperspectral information to monitor nitrogen in cotton information in real time can help guide cotton cultivation. In this study, we used drip-irrigation cotton in Xinjiang as the research object and employed various nitrogen treatments to explore the correlation between hyperspectral vegetation indexes and leaf nitrogen concentration (LNC) and the canopy nitrogen density (CND) of cotton in different growth periods and interannual. We employed 30 published hyperspectral vegetation indexes obtained through spectral monitoring in 2019 and 2020 to screen for hyperspectral vegetation indexes highly correlated with the nitrogen in cotton indexes. Based on the same group of hyperspectral vegetation indexes, interannual and multi-temporal nitrogen estimation models of cotton were established using three modeling methods: simple multiple linear regression (MLR), partial least-squares regression (PLSR), and support vector regression (SVR). The results showed the following: (1) The correlations between LNC and CND and vegetation index in individual growth periods of cotton were lower than those for the entire growth period. The correlations between hyperspectral vegetation indexes and cotton LNC, CND, leaf area index (LAI), and aboveground biomass (AGB), were significantly different between years and varieties. The relatively stable indexes between vegetation and LNC were TCARI, PRI, CCRI, and SRI-2, and the absolute values of correlation were 0.251~0.387, 0.239~0.422, 0.245~0.387, and 0.357~0.533. In addition, the correlation between CIred-edge and REIlinear and group indicators (CND, AGB, and LAI) was more stable. (2) In the models established by MLR, PLSR, and SVR, the R² value from the SVR method was higher in the estimation model based on the entire growth period data and LNC and CND. (3) Using the same group of selected hyperspectral vegetation indexes to estimate nitrogen in cotton in different growth stages, the accuracy of the estimation model of canopy nitrogen density (CND) was higher than that of the estimation model for leaf nitrogen concentration. The canopy nitrogen density most stable model was established by MLR at the flowering and boll stages and the full-boll stage with R² = 0.532~0.665. This study explored the application potential of hyperspectral vegetation indexes to the nitrogen of drip-irrigated cotton, and the results provide a theoretical basis for hyperspectral monitoring for crop nutrients and canopy structure.

Cotton photosynthetic efficiency and the root–shoot relationship are two important physiological indexes affecting the final yield, but the interactive effects of plastic film mulching and planting density on the cotton photosynthetic efficiency and root–shoot relationship have rarely been reported. We aimed to investigate the optimal plant density with or without plastic film mulching for improved seed cotton yield in southern Xinjiang. Therefore, a two-year field experiment was conducted to investigate the effects of plastic film mulching (with or without plastic film mulching) and planting density (D1: 9 × 10⁴ plants ha⁻¹; D2: 18 × 10⁴ plants ha⁻¹; D3, 22 × 10⁴ plants ha⁻¹, local conventional planting density; D4, 27 × 10⁴ plants ha⁻¹) on the cotton root–shoot relationship, photosynthetic parameters, and seed cotton yield. Our results showed that the seed cotton yield was improved under plastic film mulching at all planting densities, but economic income was significantly lower in comparison to without plastic film mulching in 2023. Compared with D3, seed cotton yield and economic income at D2 increased by 6.9% and 12.2%, either with or without plastic film mulching, respectively. The highest increase in the seed cotton yield in D2 under plastic film mulching was due to the greatest improvements in the root production capacity and photosynthesis. The boll capacity of the root system (BCR) and boll loading of the root system (BLR) in D2 were the highest among all treatments with film mulching, being 9.0% and 16.9% higher than that in D3 in 2022 and 2023. However, the root–shoot ratio (R/S) was 7.1% and 6.9% lower in D2 than D3, under film mulching, in 2022 and 2023. Moreover, moderate plant density (D2) improved the SPAD value, chlorophyll fluorescence (F_v/F_m and PI_abs), and photosynthetic parameter (Pn, Tr, and Gs) and decreased Ci compared with other planting density treatments in both years. Further analyses with correlation analysis showed that the seed cotton yield was highly positive correlated with BLR, BCR, and the photosynthetic parameter. In summary, suitable planting density (18 × 10⁴ plants ha⁻¹) combined with plastic film mulching has the potential to obtain high yields by enhancing the efficiency of photosynthetic assimilates, improving the capacity of cotton root production, providing a reference for suitable planting density under plastic film mulching.

Wheat/maize intercropping has been widely practiced in northwestern China. It is crucial to optimize the canopy structure and geometric configurations to enhance the performance of the system. This research determined the responses of intercrops to the different canopy structures created by the different wheat/maize intercropping systems. Field experiments were carried out in 2012, 2013, and 2014 at Wuwei, Gansu. Three intercropping patterns—six rows of wheat alternated with two rows of maize (6W2M), six rows of wheat alternated with three rows of maize (6W3M), and six rows of wheat alternated with four rows of maize (6W4M)—were compared with sole wheat and sole maize. The results showed that maize plant heights differed between the inner rows and the border rows, and the difference was greater for the 6W3M system than for the 6W4M system. The three intercropping systems had an average land-use equivalent ratio (LER, calculated based on grain yield) of 1.25, indicating an increase in land-use efficiency by 25% compared to the corresponding sole crops. The shape of maize strips in 6W3M optimized the canopy structure and increased the productivity of wheat and maize. The wheat in 6W3M had significantly more grain yield compared with the sole wheat and the 6W2M due to the maize belt shape, which resulted in the soil evaporation negatively affecting the intercropped wheat grain yield of the 6W3M. Similarly, the maize belt shape facilitated the light penetration and enhanced the reproductive growth by increasing the two cobs per plant rate (TCR) of the maize. The highest TCR of the 6W3M produced a higher maize grain yield than the 6W2M and sole maize in the three growing seasons. The maize belt width in the strip intercropping system had a significant effect on the grain yield of both wheat and maize, which reduced water evaporation, harmonized light distribution, and increased productivity.

Cotton root systems sustain photosynthesis by nutrient uptake and coordinate with above-ground growth to influence yield. This study explored the effects of fulvic acid (FA) and phosphorus (P) fertilizers on the relationships between cotton photosynthetic capacity (CAP) and root carbohydrate metabolism. A field experiment was conducted including five treatments: no P fertilizer (CK), 105 kg P₂O₅ ha⁻¹ (P1), 150 kg P₂O₅ ha⁻¹ (P2), 105 kg P₂O₅ ha⁻¹ + FA (FP1), and 150 kg P₂O₅ ha⁻¹ + FA (FP2). Results found that FP2 showed the most significant advantage, ensuring a suitable leaf area index (LAI) and cotton fractional interception of photosynthetically active radiation (IPAR) and consequently maintaining a high CAP. Compared with FP2, FP1 resulted in an increase in the boll loading of the root system (BLR) by 8.1% and the boll capacity of the root system (BCR) by 9.3%. From the peak flowering stage to the peak boll setting stage, sucrose and starch contents in FP1 were 6.2–19.2% and 26.5–27.9% lower than those in FP2, respectively. Conversely, fructose and glucose contents in FP1 were 6.4–10.8% and 7.2–8.8% higher than in FP2. The cotton reproductive organ biomass was increased by 11.1% and 14.7% relative to FP2. Moreover, FP1 achieved the highest yield, with an increase of 8.5% and 11.0% compared with P2 and FP2, respectively. Taken together, our study suggests that application of FP1 (105 kg P₂O₅ ha⁻¹ + FA) could be a proper P fertilization method in cotton production of saline-alkali and arid regions.

Background: Dry matter accumulation and distribution constitute the essential material basis for crop yield formation, directly impacting both yield and quality development. Nonetheless, the effects of planting density on dry matter accumulation, distribution, and nutrient uptake in cotton across various row spacing configurations remain ambiguous. Methods: This study conducted a two-year field experiment utilizing the cotton cultivar “Tahe 2”. In 2024, a single-factor trial was adopted with three row spacing treatments: same row spacing (S3), wide-ultra-narrow row spacing (S4), and wide-narrow row planting (S6). In 2025, a two-factor split-plot design was used: main plots were the three row spacing patterns (S3, S4, S6), and subplots were three planting densities: 135 000 plants ha ^-1 (D1), 180 000 plants ha ^-1 (D2), 225 000 plants ha ^- ¹ (D3). We investigated leaf area index (LAI), dry matter accumulation and distribution, nutrient content, and yield. Results: In 2024, S6 showed the highest leaf area index (LAI) and dry matter accumulation ( p < 0.05). In 2025, LAI and dry matter accumulation increased significantly with increasing plant density ( p < 0.05). The dry matter partitioning ratio to reproductive organs increased with growth. Row spacing and planting density significantly affected leaf nutrient contents; upper-leaf nitrogen (N) and potassium (K) first increased then decreased, while middle- and lower-leaf N and leaf phosphorus (P) at all positions declined gradually. Boll number per plant decreased with increasing density, whereas yield was significantly affected by their interaction ( p < 0.05). The highest yield was found in S6 in 2024 and S6D2 in 2025. Two-year experiments indicated that S6 combined with 180,000 plants ha ^- ¹ optimized population structure, improved resource use efficiency, and increased cotton yield. Conclusion: Among the three row spacing configurations tested, the medium planting density of 180,000 plants ha ^-1 under the wide-narrow row planting pattern (S6) optimized the leaf area index, enhanced population dry matter accumulation, and improved distribution to reproductive organs, thereby maximizing seed cotton yield, which reached up to 6420.47 kg·ha ^-1 .

Cotton aphid is the most common and harmful insect in the process of cotton growth and development, and it is the most serious pest that restricts the high yield and high quality of cotton in China and even in the world. The field-scale experiments were conducted for two years to estimate the population of cotton aphids model on cotton leaves using remote sensing. Hyperspectral data from single leaves and aphid populations were obtained at different growth stages of various cotton varieties. Preprocessing techniques including savitzky-golay (SG), first derivative (FD), second derivative (SD), logarithm transformation (LG), and reciprocal transformation (RG) were applied to the original spectral data. The successive projections algorithm (SPA) combined with the Pearson correlation coefficient (Pearson) method were used to construct three types of hyperspectral monitoring models: linear regression (LR), Extreme gradient boosting (XGBoost), and partial least squares regression (PLSR). Results indicated that LG significantly improved model accuracy, while SPA effectively reduced the number of bands required for analysis. Among the three models constructed using selected feature bands, the XGBoost model outperforms LR and PLSR models in terms of prediction accuracy. LG-Person-XGBoost identified ten feature bands with a coefficient of determination (R2), root mean square error (RMSE), and relative percent deviation (RPD) values reaching 0.76, 65.74 heads/leaf, and 0.91 respectively for the modeling set; whereas predicted R2, RMSE, and RPD values were found to be 0.36, 57.62 heads/leaf, and 0.99 respectively. LG-SPA-XGBoost achieved superior prediction accuracy by selecting eight feature bands with R2, RMSE, and RPD values reaching 0.87, 46.7 heads/leaf, and 1.43 respectively for the modeling set; whereas predicted R2, RMSE, and RPD values were found to be 0.77, 42.66 heads/leaf, and 1.27 respectively. This indicates that the hyperspectral remote sensing model can be used to estimate the population of cotton aphids on cotton leaves based on the selection of feature bands providing are reference for the nondestructive monitoring of cotton aphids in cotton fields and offering a significant supplement to the traditional methods of crop pest quantity monitoring.