The research aimed to understand farmers’ willingness to adopt (WTA) and willingness to pay (WTP) for precision pesticide technologies and analyzed the determinants of farmers’ decision-making.  We used a two-stage approach to consider farmers’ WTA and WTP for precision pesticide technologies.  A survey of 545 apple farmers was administered in Bohai Bay and the Loess Plateau in China.  The data were analyzed using the double-hurdle model.  The results indicated that 78.72% of respondents were willing to apply precision pesticide technologies provided by service organizations such as cooperatives and dedicated enterprises, and 69.72% were willing to buy the equipment for using precision pesticide technologies.  The results of the determinant analysis indicated that farmers’ perceived perceptions, farm scale, cooperative membership, access to digital information, and availability of financial services had significant and positive impacts on farmers’ WTA precision pesticide technologies.  Cooperative membership, technical training, and adherence to environmental regulations increased farmers’ WTP for precision pesticide technologies.  Moreover, nonlinear relationships between age, agricultural experience, and farmers’ WTA and WTP for precision pesticide technology services were found.

Sensitivity analysis (SA) is an effective tool for studying crop models; it is an important link in model localization and plays an important role in crop model calibration and application.  The objectives were to (i) determine influential and non-influential parameters with respect to above ground biomass (AGB), canopy cover (CC), and grain yield of winter wheat in the Beijing area based on the AquaCrop model under different water treatments (rainfall, normal irrigation, and over-irrigation); and (ii) generate an AquaCrop model that can be used in the Beijing area by setting non-influential parameters to fixed values and adjusting influential parameters according to the SA results.  In this study, field experiments were conducted during the 2012–2013, 2013–2014, and 2014–2015 winter wheat growing seasons at the National Precision Agriculture Demonstration Research Base in Beijing, China.  The extended Fourier amplitude sensitivity test (EFAST) method was used to perform SA of the AquaCrop model using 42 crop parameters, in order to verify the SA results, data from the 2013–2014 growing season were used to calibrate the AquaCrop model, and data from 2012–2013 and 2014–2015 growing seasons were validated.  For AGB and yield of winter wheat, the total order sensitivity analysis had more sensitive parameters than the first order sensitivity analysis.  For the AGB time-series, parameter sensitivity was changed under different water treatments; in comparison with the non-stressful conditions (normal irrigation and over-irrigation), there were more sensitive parameters under water stress (rainfall), while root development parameters were more sensitive.  For CC with time-series and yield, there were more sensitive parameters under water stress than under no water stress.  Two parameters sets were selected to calibrate the AquaCrop model, one group of parameters were under water stress, and the others were under no water stress, there were two more sensitive parameters (growing degree-days (GDD) from sowing to the maximum rooting depth (root) and the maximum effective rooting depth (rtx)) under water stress than under no water stress.  The results showed that there was higher accuracy under water stress than under no water stress.  This study provides guidelines for AquaCrop model calibration and application in Beijing, China, as well providing guidance to simplify the AquaCrop model and improve its precision, especially when many parameters are used.