To analyze the global sensitivity of winter wheat parameters using the AquaCrop model on a global scale, the extended Fourier amplitude sensitivity test (EFAST) was utilized to identify parameter sensitivity differences in different regions and meteorological conditions represented by eight stations in Henan Province, including Zhengzhou, Anyang, Shangqiu, Luanchuan, Nanyang, Xuchang, Zhumadian, and Xinyang. The results showed that: (1) the sensitivity of crop parameters is little affected by meteorological conditions for biomass, and the sensitivity parameters of the eight regions were consistent; there were minimum growing degrees required for total biomass production (stbio), normalized water productivity (wp), maximum canopy cover in fraction soil cover (mcc), crop coefficient when the canopy was complete but prior to senescence (kcb), Growing degree-days (GDD)-from sowing to emergence (eme), and GGD-increase in canopy cover (cgc); (2) for canopy cover, the most sensitive parameters were mcc, cgc, soil surface covered by an individual seedling at 90% emergence (ccs), and other parameters were more sensitive in early growth stage of winter wheat; (3) for yield, GDD-from sowing to flowering (flo) was the most sensitive parameter. The results of this study will provide support for the use of the AquaCrop model to investigate crop management at the local level.

Current research on the relationship between land use change and habitat quality and the driving factors, interactions, and future changes under multi-scenarios remains limited, especially in the ecologically fragile Yellow River Basin (YRB). Using the 2000, 2010, and 2020 land use data, we coupled FLUS with the InVEST model to analyze the relationship between land use change and habitat quality and predict habitat quality under 4 development scenarios. The driving factors were quantitatively identified by their interactive effects by utilizing Geodetector. The results showed that: (1) Overall, habitat quality of the YRB was moderate with an improving trend but exhibited significant spatial heterogeneity. The spatial distribution of habitat quality aligned closely with land use patterns. Habitat degradation showed a spatial pattern of “central-high, peripheral-low; eastern-high, westernlow”. (2) Land use was the primary driver of habitat quality differentiation (q > 0.8), with population density and GDP gaining influence. Factor interactions, especially between land use and other variables, exceeded individual effects. (3) Under the ecological space priority scenario, habitat quality improves significantly, especially in fragile zones where policy interventions are effective. Conversely, the production space priority scenario risks habitat degradation, necessitating measures to mitigate ecological pressure caused by industrial mining and urban expansion.