While plastic film mulching and proper high-density planting are important methods that can improve maize yield, years of accumulated residual film have created soil pollution and degraded soil, and thus has impeded sustainable agriculture development. Here, we compared the stalk and root lodging resistances of three maize cultivars grown at two planting densities both with (FM) and without (NM) plastic film mulch. Our aim was to provide a theoretical basis that may help assure a future of successful no-film planting with increased planting density. The results showed that, compared with FM, the average dry weight per unit length and bending strength of basal internode decreased for all cultivars at both planting densities in the NM treatment. At 9.0 × 10⁴ plants ha⁻¹, the stalk breaking force (SFC) of Xinyu77, KWS9384, and KWS2030 in the NM treatment decreased by 4%, 21%, and 22%, respectively. At 12.0 × 10⁴ plants ha⁻¹, SFC of Xinyu77 and KWS2030 increased by 14% and 1%, respectively, while KWS9384 decreased by 10%. Additionally, the root diameter, length, volume, width, depth, and the vertical root-pulling force of maize decreased. Although the lodging resistance of maize grown without film mulch was lower than that of maize grown with it, those adverse effects can be mitigated by selecting suitable cultivars and by using proper high-density planting and appropriate cultivation measures.

Analyzing the dominant drivers of the Food-Water-Land-Ecosystem (FWLE) nexus in the future is important for improving sustainable development in dryland ecosystems. However, the future trajectories of food–water–land–ecosystem interactions in typical drought-prone regions remain poorly understood. To address this gap, this study coupled the Gray Multi-Objective Programming with Patch-generating Land Use Simulation (GMOP-PLUS) model and applied spatial analysis methods (including longitudinal and zonal statistical analysis, trade-off synergy analysis, and redundancy analysis) to examine the spatiotemporal differentiation patterns of the FWLE nexus in Xinjiang under different development scenarios. Over the past two decades, water yield in Xinjiang’s agricultural landscapes has declined by 57.4%, primarily due to land-use and land-cover changes. Under the 2030 sustainable development scenario, a custom optimization developed via the GMOP model that balances economic and ecological objectives, crop production and habitat quality are projected to increase by 47.9% and 55.1%, respectively. Moreover, redundancy analysis results indicate that the driving contribution of precipitation on the FWLE nexus is expected to reach 76.9% by 2030. These findings provide a clear delineation of priority spatial units for improvement within Xinjiang agro-ecosystem and offer a strategic pathway for balancing ecological conservation and economic development.

To simultaneously consider mean yield and interannual stability in cotton variety evaluation, this study integrated yield level and stability into a unified framework. Ten cotton varieties were evaluated using four years (2022–2025) of field trial data. Mean seed cotton yield, coefficient of variation (CV), and Shukla’s stability variance characterized yield level, interannual fluctuation, and variety × year interaction stability, respectively. The Finlay–Wilkinson regression coefficient assisted in analyzing varietal responses to year-specific environments, and entropy weight–TOPSIS was used for comprehensive ranking. Year, variety, and variety × year interaction significantly affected seed cotton yield (p < 0.001). Weights of CV, mean yield, and Shukla’s stability variance were 42.3%, 32.1%, and 25.7%, respectively. Mean-yield ranking was generally consistent with the TOPSIS ranking, though some varieties changed. CCRI 100 had a mean yield of 3413 kg ha⁻¹ and a CV of 5.4%. It ranked first in both the mean-yield ranking and the entropy weight–TOPSIS comprehensive ranking. SCRC 28 ranked first in stability but lower in yield, ranking second overall. This study demonstrates that entropy weight–TOPSIS can integrate yield level, interannual fluctuation, and variety × year interaction stability into a single evaluation framework, providing an effective method for shifting from “high-yield screening” to “coordinated high-yield and stable-yield screening” in multi-year variety trials.