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• Sobol' method is used to evaluate the sensitivity of APSIM-Wheat yield predictions. • Yield is most sensitive to initial nitrogen amount, soil parameters, precipitation. • Estimated yield is influenced most by the limiting resource under given conditions. • Scenarios for high fertilization level or dry seasons have lower yield variability. The performance of cropping system models is often limited by uncertainties in model parameters and inputs. This study aims to explore how model yield prediction responds to these uncertainties under various environmental and management conditions. The Sobol' method was used to investigate the sensitivity of the Agricultural Production Systems SIMulator (APSIM)-Wheat yield prediction to the following factors: air temperature (maximum and minimum), precipitation, initial soil nitrogen content, nitrogen fertilization amount, and soil hydraulic parameters. Eighteen scenarios were defined to consider a combination of three weather conditions (wet, normal, and dry growing season, based on the historical climatology of the Wimmera district of Victoria, Australia), three soil types (sandy, loamy, and clayey soils), and two nitrogen fertilization applications (50 and 100 kg N/ha). Eight thousand APSIM simulations were used to calculate the first order and total sensitivity indices for each scenario. The effects of weather, soil water characteristics and nitrogen availability were estimated by measuring their impacts on sensitivity indices. Our results show that yield was more sensitive either to variables that control water availability (precipitation and soil type) or variables that control nitrogen availability, depending on which was more limiting to wheat growth under the scenario. For example, in case of low nitrogen fertilization scenario, the initial nitrogen content sensitivity ranked first. Variation of this factor contributed 64% to 79% to the variance in simulated yield for clayey soils, nearly four times higher than the second-ranked factor (nitrogen fertilization amount). Soil hydraulic parameters and precipitation were most important when the crop growth was more constrained by water availability than by nitrogen availability. In the case of sandy soils in dry years with high nitrogen fertilization level, soil parameters and precipitation accounted for 83% of the yield variability. Minimum temperature consistently ranked as the least important factor under all scenarios. This work will help researchers better understand the model inputs' impacts on the simulated yield variability under various soil, management, and weather conditions. The results also support agronomists and practitioners in the optimal use of the APSIM-Wheat model as a management tool. [Display omitted] [ABSTRACT FROM AUTHOR]

• Eighteen spectral indices are compared for their sensitivity to CNC and biomass. • Of these indices, PRI best correlates with CNC but it also correlates with biomass. • Regression relationships between CNC and all indices shift with growth stages. • CCCI exhibits stronger correlation to biomass than CNC. • A wide range of growth conditions are needed to develop a robust indicator for CNC. In precision farming, accurate estimation of canopy nitrogen concentration (CNC) is valuable for effective crop growth monitoring and nitrogen (N) fertiliser management. To date, many canopy multispectral indices have been proposed as indicators for CNC; however, many of these indices have also shown sensitivity to biomass and their performance drops at high biomass levels. Dependence on growth stage, season, or other environmental conditions limits their efficacy as generalized CNC indices. The objectives of this study were to assess the robustness of popular CNC indices across a wide range of biomass levels and fertiliser application levels; and for two contrasting seasons – winter and summer. To achieve this, we analysed the efficacy of seven canopy nitrogen indices, including canopy chlorophyll content index (CCCI), together with eleven other commonly used spectral indices. We used canopy level solar-induced hyperspectral reflectance data acquired using a hand-held optical spectroradiometer across four growth stages in winter (May-June 2018) and four in summer (January-February 2019) from an experimental field of irrigated perennial ryegrass with variable N application in Victoria, Australia. The field contained 40 plots, each with one of eight different N treatments. Almost all the indices exhibited similar correlation to CNC (%) when applied to individual stages (days) in both winter and summer; however, relationships between CNC and individual indices varied significantly between stages. We obtained similar results for canopy biomass. When the data across the entire range of growth stages and seasons were combined, the correlations between most canopy nitrogen indices and CNC became weak (R2 < 0.25, 0.9% ≤ RMSE ≤ 1.0%). PRI exhibited the highest correlation with CNC (R2 = 0.58, RMSE = 0.7%) for the combined data set. Even so, PRI's association with CNC and canopy biomass changed with the season. Most indices responded to both CNC and biomass simultaneously, and this confounds the estimation of CNC due to strong but growth stage-specific relationships between CNC and canopy biomass. This study shows that it is important to consider a wide range of conditions when evaluating multispectral CNC indices. [ABSTRACT FROM AUTHOR]