Optimizing planting date and cultivar choice is essential for balancing lint yield, fiber quality, and production stability in wheat-cotton double-cropping systems. However, their combined effects on yield-quality trade-offs and temporal stability (defined as the temporal mean divided by the temporal standard deviation) remain insufficiently quantified. A five-year field experiment (2019-2023) was conducted in the Yellow River Valley to evaluate four planting dates (PD1-PD4; May 10-June 10) and two contrasting short-season cotton cultivars (highquality JK707; high-lint yield LM2387) with respect to lint yield, fiber quality, temporal stability, and trait tradeoffs. Lint yield was significantly affected by both planting date and cultivar, peaking under the earliest planting (1229 kg ha-1 at PD1) and declined by 34.4 % under the latest planting (PD4). Fiber quality was predominantly genotype-driven, with cultivar significantly affecting all traits and planting date influencing only micronaire. JK707 achieved substantially higher Q-scores than LM2387 (66.84 vs. 46.97). Temporal stability indices revealed contrasting cultivar strategies: LM2387 showed high lint yield temporal stability under early planting (TS_LY=6.30 in PD1) but sharp declines under later dates (TS_LY=2.29 in PD4), whereas JK707 maintained relatively consistent lint-yield temporal stability across planting dates (TS_LY = 2.11-3.16) and greater stability in fiber quality traits. Yield-quality trade-offs were stronger in LM2387 (index 0.713 vs. 0.208) and were weakened by delayed planting due to concurrent reductions in yield and quality. A TOPSIS multi-criteria evaluation integrating lint yield, Q-score, and their temporal stability identified JK707 sown in PD2 as the optimal combination (C; = 0.57), followed by LM2387 in PD1 (C; = 0.531), whereas PD4 consistently produced the poorest performance. Aligning planting time with cultivar traits thus enhances production robustness and reduces climate-related risk, offering practical guidance for sustainable and climate-resilient intensification of wheat-cotton double-cropping systems and potentially benefiting similar systems worldwide.

Legume-based intercropping enhances asymbiotic biological nitrogen fixation (BNF); however, the underlying mechanisms remain unclear, including the roles of soil keystone diazotroph taxa with varying niche breadths. A field experiment was conducted to evaluate soil BNF variations between rhizosphere and bulk soils in peanut/cotton intercropping systems and monocultures. BNF activities were measured by nitrogen fixation rates, nitrogenase activity, and nifH gene abundance. Phylogenetic null models, co-occurrence networks, and niche breadth analysis were applied to investigate the roles of diazotrophic keystone taxa and their ecological niches. Rhizosphere soils exhibited 7.8-125.5% higher BNF potentials than bulk soils, whereas intercropping systems showed 11.6-323.0% increases over monocultures for nitrogen fixation rate, nitrogenase activity, and nifH gene abundance (all P<0.05). Diazotrophic community composition and diversity differed significantly, with Proteobacteria (excluding Alphaproteobacteria) enriched in intercropping and rhizosphere soils, while Cyanobacteria and Firmicutes were less abundant. Deterministic processes, particularly heterogeneous selection, dominated community assembly in the rhizosphere (91.9%) and intercropping soils (86.3%). The co-occurrence networks consistently revealed more complex and interconnected communities in intercropping and rhizosphere soils that were dominated by opportunistic diazotrophs (78.8-85.9%), followed by specialists (10.2-18.5%) and generalists (1.38-3.80%). Keystone taxa, including opportunists such as Azoarcus, Azohydromonas, and Steroidobacter, and generalists like Pseudomonas and Azotobacter, correlated positively with microbial biomass carbon and nitrate nitrogen, contributing to enhanced BNF. Peanut/cotton intercropping enhances BNF by selectively enriching the keystone diazotrophic taxa with varying ecological roles, particularly opportunists and generalists. Such targeted intercropping strategies can optimize BNF, improve soil fertility, and promote sustainable agricultural production.

Incorporating green manure is a vital strategy for optimizing cropping systems and improving soil quality. However, it is unclear whether the effects of different types of green manure on subsequent cotton yield and soil fertility improvement are uniform. This study evaluated the effects of three green manure incorporation treatments over a two-year cropping cycle (Chinese violet-cotton-Chinese violet-cotton (T1), rapeseed-cotton-rapeseed-cotton (T2), and ryegrass-cotton-hairy vetch-cotton (T3)) on cotton yield and yield components. These treatments were also compared with the winter fallow-cotton (T0) to analyze differences in soil nutrients and net ecological–economic benefits. No significant differences in cotton yield or yield components were observed among the green manure incorporation treatments. However, averaged across two years, T1 produced a seed cotton yield 8.1% higher than T2 and 3.9% higher than T3. T2 and T3 significantly enhanced soil alkali-hydrolyzed nitrogen, organic matter, and total humus content compared to T0. Notably, T3 increased these parameters by 18.7, 23.9, and 26.8%, respectively. Additionally, T3 achieved the highest net ecological–economic benefit, exceeding T0 by $405/ha. This study highlights the potential of green manure to enhance soil fertility and ecological–economic sustainability in cotton fields. Further research is required to evaluate its long-term benefits and broader implications for sustainable agriculture.

With the advancement of agricultural information technology, sensors have become instrumental in monitoring soil water environments, opening new avenues for optimizing water management strategies. This study utilized high spatiotemporal resolution sensors and a grid sampling method to monitor soil moisture distribution during the squaring and flowering and boll developing stage of cotton (Gossypium hirsutum L.) under varying planting densities. Geostatistical methods were employed to calculate soil water consumption (SWC) distribution and dynamics, quantifying water competition patterns among cotton populations at different planting densities. The analysis integrated cotton growth dynamics and yield to examine the relationship between biomass, yield, and soil water utilization. Results showed significant interannual variability in cotton growth curves, with planting density notably affecting underground biomass, yield, and the distribution of SWC within the soil profile. A positive correlation was found between water consumption at depths of 30-50 cm and yield, even under low water consumption conditions. The three-dimensional efficiency map showed that a planting density of approximately 210,000 plants center dot hm-2 with SWC between 250.0 and 400.0 mm resulted in stable, high biomass and yield. The double Gaussian model indicated that with increasing SWC, a first yield peak was at the SWC of 255.9 mm, after which there was a decline in water use efficiency (WUE) (the slope of yield vs. SWC). The second yield peak was at a greater SWC of around 630.0 mm. This study also found that by controlling the soil water consumption of cotton at different densities, the biomass and yield of cotton can be quantitatively regulated, thereby reducing the yield differences caused by interannual effects and varying planting densities. These findings provide valuable insights into the spatial competition and efficient utilization of soil moisture in cotton populations, offering important guidance for achieving high, stable yields and precision water management in cotton production.

Climate change and market demands emphasize the importance of both the average quality and long-term stability of end-use fiber quality in cotton production. Intercropping, a sustainable agriculture practice, has demonstrated potential to enhance cotton fiber quality compared to monoculture. However, the impacts of intercropping on cotton fiber quality stability and key factors influencing quality variability remain poorly understood. This study analyzed five years of data to investigate the variability and controlling factors of cotton fiber quality in both monoculture and intercropping. The results indicated that intercropping maintained cotton fiber length within the long fiber range. While intercropping did not influence fiber quality temporal stability or the risk of quality decline, higher stability was associated with a reduced probability of quality deterioration. Trade-offs among fiber quality traits were observed, indicating that improving one trait often negatively impacted another. Intercropping had minimal impact on these trade-offs. Weather factors accounted for 17-33 % of fiber quality variability, with the effects varying by specific quality trait. Precipitation and photosynthetically active radiation positively influenced fiber length, strength, and uniformity index but negatively affected micronaire and elongation. In contrast, temperature positively influenced all measured fiber quality traits. Overall, our results demonstrate intercropping maintained relatively stable fiber quality over time. This study also reveals strong trade-offs among multiple cotton fiber quality traits, underscoring the need to close trade-offs and achieve synergistic improvements in future research.

Crop growth monitoring technology holds great potential to enable timely management adjustments, optimize resource use, and support sustainable agriculture practices, achieving efficient intelligent agriculture for data-driven cultivation. Traditional field measurement and monitoring methods are often inefficient and provide limited, outdated information. The photon sensor-based fraction of intercepted photosynthetically active radiation (FIPAR) monitoring system was demonstrated to provide accurate real-time tracking of crop growth. It was designed to capture spatial variations in FIPAR across the canopy profile throughout the entire crop growth season. Subsequently, spatiotemporal models were applied to simulate variations in FIPAR across the entire canopy throughout the crop's growth. Finally, leveraging these model simulations, spatiotemporal variations in specific FIPAR values were derived to effectively characterize and describe crop growth dynamics. The technology was proved in a two-year monoculture cotton experiment. Results demonstrated that the post-simulation R² values of the dynamic spatiotemporal model were 0.940 for 2020 and 0.749 for 2021. Common agronomic traits used to measure cotton growth, including plant height (PH), aboveground biomass (AGB), and leaf area index (LAI), showed the highest correlations with FIPAR at 0.2 and 0.3 for PH, 0.5 and 0.6 for AGB, and 0.4 and 0.5 for LAI, all exhibiting significant positive relationships. Spatial variations of these FIPAR values within the canopy structure exhibited a linear relationship with PH, AGB, and LAI. This study demonstrated the feasibility of using photometric sensors as a non-destructive technology for real-time crop growth monitoring. The technology was developed to provide reasonably accurate crop growth information while balancing cost requirements for applications in both scientific research and agricultural production, offering high potential for guiding smart crop management to enhance agricultural productivity.

Crop yield is primarily determined by physiological parameters, including light interception by the canopy (IPAR), radiation use efficiency (RUE), and harvest index (HI). However, little information is available about how these physiological parameters are impacted by planting date and what their contributions are to cotton yield loss. To evaluate the relative contribution of each physiological parameter to planting date-associated yield loss in cotton, an experiment was conducted during the 2019 to 2021 growing seasons at a field site in Anyang, Henan, China. Two contemporary cotton cultivars were grown in the field with four different planting dates (PDMAY10, PDMAY20, PDMAY30 and PDJUN10) for the growing seasons from 2019 to 2021. The measurements included climatic parameters, seed cotton yield, yield components, HI and biweekly leaf area index, light interception and biomass. First, seed cotton yield was significantly affected by the planting dates, with yields ranging from 1984 to 3388 kg ha-1, averaged across cultivars and years, while a significant difference between the first three planting dates was observed only in 2021. Second, the planting date significantly impacted the total light interception during the growing season (IPARtotal) and HI, while IPARtotal and HI decreased with the delayed planting date in all the years of the study. Compared to PDMAY10, IPARtotal was 0.34%, 13.2%, and 19.9% lower for PDMAY20, PDMAY30 and PDJUN10, respectively. Averaged across cultivars and years, PDMAY10 resulted in the highest HI value (0.28). The RUE gradually decreased as the planting date was delayed, but a significant difference in RUE between planting dates was observed only in 2021. Overall, among the three physiological yield-driving parameters, IPARtotal was a stronger contributor (46.9-47.0%) to the yield loss for PDJUN10 than PDMAY10 and PDMAY20. However, when compared to PDMAY30, HI was the greatest contributor, accounting for approximately 72.5% of the seed cotton yield loss for PDJUN10, followed by IPAR. We found that when cotton sowing in the Yellow River basin was postponed until June 10, the cotton yield was significantly reduced, and IPAR and HI were the main factors responsible for PDJUN10 yield loss on average. This study identified the most important functional traits for seed cotton yield response to planting date and has important reference value for stable cotton production.

ABSTRACTPlanting green manure to improve cash crop yield and soil health has been widely recognized, and understanding cash crop performance after green manure integration is pivotal for determining its potential to bolster and enhance crop productivity and sustainable production. However, it is unclear whether the effects of different types of green manure on subsequent cash crops are uniform. In order to clarify this issue, we systematically analyzed the effects of green manure types and nitrogen (N) application rates on succeeding cotton agronomic performance, yield, biomass, yield stability, and nutrient uptake. A split‐plot experiment with two factors was designed, main factor includes four cover cropping systems monoculture cotton (MC), February orchid/cotton cover cropping (FoC), hairy vetch/cotton cover cropping (HvC), and a mixture of February orchid and hairy vetch/cotton cover cropping (FHC), and sub‐main factor include four N application levels (0 (N0), 112.5 (N1), 168.75 (N2), and 225 (N3) kg N ha−1). Results suggests that nonlegume green manure (February orchid) accumulated more biomass, N, P, and K nutrients than the legume green manure (hairy vetch) and green manure mixture. Compared with cotton yield of MC, the FoC, HvC, and FHC system increased by 5.8%, 7.6%, and 15%, respectively. N use efficiency was more significantly influenced by the N application rates than by cropping systems. Specifically, as N application rates increased, N use efficiency decreased under MC, HvC, and FHC systems, while it increased under the FoC system. Additionally, we observed a trade‐off between cotton yield and yield stability, with the highest yield stability when cotton yield reached 2633 kg ha−1. This study provides evidence that nonlegume green manure (February orchid) with greater advantages on cotton vegetative organ growth, legume green manure (hairy vetch) can promote nutrient uptake compared to other green manure, while green manure mixture (February orchid and hairy vetch mixture) significantly increased cotton yield and yield stability. These findings provide evidence‐based insights highlighting the respective benefits of incorporating diverse species of green manure into cotton‐based cropping systems in the Yellow River Basin of China.

Purpose Phosphorus (P) is important for production in double-cropped wheat and cotton systems. The spatial distribution of soil Olsen-P has not been examined in these systems, and was quantified in the study reported here. Methods A two-year field experiment was conducted comprising mono-cotton as control (CM), and wheat-cotton 3:1 (3 rows of wheat, 1 row cotton), and wheat-cotton 6:2 (6 rows of wheat, 2 rows of cotton) cropping systems. Soil samples were collected spatially using the spatial grid method. The distribution of soil Olsen-P was analyzed using the Kriging method. Results The contour map could reflect the distribution of soil Olsen-P. The Olsen-P were concentrated near its application onto the soil surface and decreased with depth. And this distribution was changed by the uptake and interception of crop roots. The P concentrated in the cotton rows were quickly reduced, with little change in the middle of the two rows in the CM. When wheat existed, soil Olsen-P accumulated under wheat roots, and marginally moved horizontally to the cotton rows after wheat was harvested. Conclusions The geo-statistical method provided an effective method to display the distribution and movement of soil nutrients. The spatial and temporal distribution of Olsen-P was significantly affected by fertilization and crop uptake. In different cropping systems, difference in the competitiveness of crop also had significant impacts on the distribution of soil Olsen-P. The present study provided favorable information for exploring the relationship between plant and soil.

Sowing date optimization is used to achieve a high crop yield and efficient resource utilization, and it is an important strategy for the adaptation of crop production to climate change. However, there is still little field evidence on the coupling effect of multiple environmental resources on cotton and its response to climate change under sowing date management. In this study, the changes in light, temperature, and water resources under different cotton sowing dates were monitored in 2021 (when an extreme rainfall occurred) and 2022. Ridge regression and structural equation models were used to analyze the responses of cotton biomass and yield formation under different sowing dates over two years to the availability and use efficiency of multiple resources, and a strategy for adapting cotton production to climate was discussed. The interaction between sowing date and climate change had significant effects on cotton yield components, as did light, temperature, and water resource use efficiency. The extreme rainstorm in 2021 reduced seed cotton yield by 52.75 % at most, and the yield reduction was alleviated through sowing date management. Growth degree day (GDD), photosynthetically active radiation (PAR), and soil water consumption (SWC) accumulation had positive coupling effects on cotton biomass formation, and the magnitude of their effects decreased. The PAR and GDD at squaring stage and SWC at flowering and boll development stage affected aboveground biomass and thus seed cotton yield. The heat use efficiency (HUE) and water use efficiency (WUE) had a positive coupling effect on seed cotton yield. Improving HUE and WUE through precise management of temperature and light resources during squaring stage and water resources during flowering and boll development stage may promote efficient production and high yields of cotton under climate change. This study portrays climate-smart agriculture and has important reference value for adapting cotton production to climate change.

为进一步探明青稞新品种甘青 10 号栽培技术,支撑青稞产业化标准种植,特进行不同肥料与播量处理对其生长及产量的影响.结果表明:磷酸二铵施用量 300 kg/hm2、尿素施用量 150 kg/hm2、播量 223.5 kg/hm2时,其产量表现最优,农艺性状适中;其次为磷酸二铵施用量 225 kg/hm2、尿素施用量 150 kg/hm2、播量 223.5 kg/hm2或 256.5 kg/hm2 的处理.即青稞新品种甘青 10 号施肥水平为尿素 150 kg/hm2、磷酸二铵 225～300 kg/hm2,播量为 223.5～256.5 kg/hm2 时产量表现较好,可实现较好的增产效果.

Taking Longyou No. 7 as the test variety, through the analysis of the overwintering rate, growth characteristics, economic characters and yield performance of winter rape under five different sowing dates, it is considered that August 1 to August 13 is the suitable sowing date in the 2 800 m altitude area of Gannan plateau, especially the two sowing dates of August 1 and August 7 showed good adaptability and agronomic characters. The growth period is 314～328 d, and the yield is 1 281～ 1 961 kg/hm~2.

Regulation of plant population density is crucial for optimizing cotton fiber quality. However, the relationship between plant density and fiber quality stability under contrasting climatic conditions remains unclear, and the compromise between fiber quality and temporal stability is unknown. In this study, based on a long-term field experiment with various plant densities (1.5-10.5 plants m-2) conducted from 2008 to 2021, cotton fiber quality variability, including fiber length, fiber strength, elongation, uniformity index, and micronaire, and their temporal stability, were evaluated. We determined the optimal plant density for cotton cultivation and elucidated the contributions of plant density and climatic conditions to the variability of cotton fiber quality and its temporal stability in China's Yellow River Valley. The results revealed that cotton maintained its upper-intermediate quality throughout the study period, with fiber length, strength, elongation, uniformity index, and micronaire values ranging between 28.04 and 30.03 mm, 26.15-30.05 cN tex-1, 5.90-6.83%, 83.85-85.44%, and 4.24-5.14, respectively. An increase in plant density improved fiber quality but impaired temporal stability (P < 0.01), and slight increases in stability at lower plant densities resulted in substantially decreased probabilities of years with major declines in fiber quality class. Plant density and climatic conditions regulated the quality traits (23.5% and 69.3% of the explained variance, respectively), including fiber length, fiber strength, and micronaire, while climate was the most important factor (75.9% of the explained variance) in determining their temporal stability. Photosynthetically active radiation and maximum and mean temperature exhibited significant positive effects on fiber quality, whereas mean diurnal temperature range had the opposite effect. A plant density of 3.3-5.1 plants m-2 ensures the highest temporal stability of cotton fiber quality without declines in the fiber quality class. This study first provides insights into balancing cotton fiber quality and temporal stability through agronomic interventions, with implications for the quantitative prediction of future crop quality changes owing to climatic variation.

By quantifying the soil water movement (SWM) in crop planting systems, we better understood the soil water consumption (SWC) and crop yield relationship; this finding is significant for determining the field water cycle and reducing agricultural water waste.In this paper, a case study in cotton production was conducted. Soil moisture sensors were set at depths of 10–110 cm under three cotton cropping patterns (monoculture cotton (MC), wheat (Triticum aestivum L.)/delayed intercropped cotton (WIC), and wheat/direct-seeded cotton (WDC)) based on spatial grid methods; a geostatistical grid calculus method was used to calculate SWM; and the crop and meteorological influence mechanisms on cotton lint yield were analyzed comprehensively.At the squaring stage, SWC and vertical SWM were significantly correlated with light, temperature and water conditions. At the flowering and boll development stage, SWC and vertical SWM were collectively affected by meteorological conditions and crops, and they were positively correlated with lint yield. The aboveground and belowground biomass accumulation at the flowering and boll development stage positively affected vertical SWM in and between cotton rows. Vertical SWM in cotton rows promoted SWC in cotton rows. SWC in cotton rows and aboveground biomass positively impacted lint yield formation; SWC between rows negatively impacted lint yield. The SWC and vertical SWM between rows in the MC seedling stage exceeded those in cotton rows, and more precise irrigation at the seedling stage reduced water waste. The WIC horizontal SWC at the squaring and flowering and boll opening stages were relatively large, moving from the row midlines to cotton rows.The better SWC distributions in and between cotton rows promoted water utilization in the cotton rows; this method was feasible for improving cotton yield in diverse planting systems.The results could optimize precision irrigation management at different cotton growth stages and provide a theoretical reference for promoting sustainable agricultural production and climate adaptation.

Context: Temperature changes and cultivar shifts (cultivar renewal or cultivar replacement) seriously affect cotton phenology and production under climate change but the specific effects remain uncertain. Objective: Here, we combined cotton phenological observation data and corresponding meteorological data for approximately twenty years from 56 sites to explore the effect of cultivar shifts on cotton phenology across China. Methods: Phenological growth models were used to investigate how these factors influenced cotton phenology in the Yellow River basin, Yangtze River basin and Northwest inland three major cotton regions in China. Results: As a result, the duration of the whole cotton growing period (GPw) was prolonged at 87.5% of stations, although the mean temperature (Tmean) during GPw increased at 98.2% of stations. We demonstrated that once the cultivar effect was fixed, the increase in temperature alone produced a general advancement regarding the dates of emergence, squaring, flowering, boll opening and harvesting, leading to a shortening of the corresponding growth period of cotton by 1.74, 2.2, 1.74 and 2.31 days/decade, respectively, and a shortened duration of the whole growth period (GPw) by 4.06 days/decade. In contrast, cultivar shifts prolonged the duration of emergence-squaring, boll opening-harvest and GPw at 32 (57.1%), 46 (82.1%), and 33 (58.9%) stations by an average of 5.13, 11.61 and 6.29 days/decade, respectively, although the durations of the squaringflowering and flowering-boll opening periods were reduced. However, the difference was that cultivar shifts reduced the length of the GPw of cotton in the Yellow River basin by 1.35 days/decade. Conclusions and significance: This result indicated that, differences in the effect of cultivar shifts on cotton phenology in different cotton regions. Over the last decades, the introduction of new varieties requiring longer heat times in the Yangtze River basin and the Northwest inland cotton region compensated for some of the cotton phenological changes caused by increased temperatures, while early maturing and resistant cotton varieties were more suitable for selection in the Yellow River basin.

Context: Cotton (Gossypium hirsutum L.) yield is determined by whole-plant and within-boll yield components. Little information exists regarding within-boll yield component variations related to the planting date. Objective: This study aimed to investigate whether the differences in within-boll yield components will largely explain yield variation across planting dates under a double cropping system of wheat (Triticum aestivum L.) and short-season cotton.Methods: Two contemporary cotton cultivars were grown in the field with four different planting dates (PDMAY10, PDMAY20, PDMAY30 and PDJUN10) for the 2019-2021 growing seasons. Climatic variables and cotton growth pe-riods, plant mapping, extensive yield components and fiber quality assessments were recorded each year.Results: Compared to PDMAY10, the lint yields of PDMAY20, PDMAY30 and PDJUN10 were reduced by 3.8%, 14.7% and 44.1%, respectively. The significant yield loss in PDJUN10 could be explained by the variations in yield components. At the canopy level, boll density was the greatest contributor (75.6-90.8%) to yield loss. Moreover, within bolls, lint mass per seed (11.2-18.2%) and lint mass per unit seed surface area (11.9-16.2%) accounted for the most yield loss. In addition, seed number per boll, lint mass per seed and lint mass per unit seed surface area decreased by 1.0-6.2%, 7.3-15.0% and 5.5-13.4% for PDJUN10 relative to PDMAY20, PDMAY30 and PDJUN10, respectively. The number of fibers per seed and the seed index remained relatively stable among the planting dates across all three years. No significant differences in fiber length and fiber strength were observed among planting dates in 2019 and 2020, while shorter and weaker fibers were observed for the earliest planting date in 2021, possibly because of extreme rainfall that occurred at the end of July, indicating that a deteriorated environment could reduce the effect of the planting date. Among all climatic variables, the daily maximum temperature during the flowering and boll development stage had the strongest correlation with the lint yield.Conclusions: The first three planting dates generally produced comparable lint yields and fiber qualities. Therefore, it might be important to plant short-season cotton before May 30th in the Yellow River Valley to maintain relatively high yield and fiber quality. Implications: This study provided new results to assist decision making regarding cotton planting date in double cropping of wheat and cotton in the Yellow River Valley.

甘青10号(原代号0033-1)是甘南州农业科学研究所2003年以农家品种肚里黄为母本、以甘南州农业科学研究所杂交选育的中间材料9669为父本配置杂交组合,通过系谱法选育而成的高产早熟抗病青稞新品种,2022年3月通过农业农村部非主要农作物品种登记并定名,登记编号为GPD 大麦(青稞)(2022)620004.

Analyzing the nitrogen footprint (NF) and the potential reduction of reactive nitrogen in crop production and proposing higher N utilization efficiency strategies are important approaches to sustainable crop development. Cotton is an important cash crop in China. However, only a few studies have systematically quantified the NF of cotton production in China, analyzed its temporal and spatial varieties, clarified its main constituent factors and influential factors, and explored its nitrogen emission reduction potential. This study adopts the life cycle method and analyzes the temporal-spatial varieties in the NF of cotton production in China's main cotton planting areas from 2004 to 2018 based on statistical data, analyzes the main components of the nitrogen footprint of China's cotton production, studies the main driving factors of China's cotton nitrogen emissions based on principal component analysis, simulates the nitrogen emissions of China's cotton production combined with the STIRPAT model, and comprehensively analyzes the potential nitrogen emission reduction of China's cotton production. The results indicated that the average nitrogen footprint per unit area (NFa) of cotton planting districts in Northwest China (Nw.CPD), the Yellow River Basin (Yel.RCPD) and the Yangtze River Basin (Yan.RCPD) was 112 kg Neq center dot ha(-1), 65 kg Neq center dot ha(-1) and 155 kg Neq center dot ha(-1), respectively, and the NFa of cotton production in Hunan was the highest. In terms of spatial distribution, the NFa, NFy and NFv of cotton production in Yan.RCPD were higher than those in Yel.RCPD and Nw.CPD, and the NF of cotton planted in Yel.RCPD was the lowest. The NFa of cotton planting in China increased continuously, and the NFa in Nw.CPD showed an increasing trend, while the NFa in Yel.RCPD and Yan.RCPD showed a decreasing trend. Principal component analysis (PCA) showed that fertilizer and the associated reactive nitrogen losses were the main components of the nitrogen footprint. The prediction results of the STIRPAT model showed that compared with 2018, China's cotton production can reduce nitrogen emissions by 54.43 kilotons in 2050 at most. Improved fertilizer utilization efficiency, optimized field management measures and effective policy intervention are effective strategies to reduce the nitrogen footprint of cotton production.