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The Forest Vegetation Simulator (FVS) growth and yield model is widely used throughout the United States, but recent studies have reported unexpectedly large bias for some regional model variants. Here we propose a general framework for model evaluation, designed to highlight model strengths and weaknesses and inform calibration efforts. We apply the framework to evaluate the Lodgepole Pine (LP) model of the FVS Central Rockies Variant (FVS-CR), which has rarely been evaluated in the literature despite its widespread use in the western US. We started with a qualitative verification of the structure and logic of the FVS-CR LP model against a modified Bakuzis matrix, determining that it adequately reproduces known patterns of stand dynamics. We then compared stand-level growth simulations to a chronosequence developed from 554 Forest Inventory and Analysis (FIA) plots measured in Colorado and Wyoming lodgepole pine forests. This quantitative validation exercise revealed that the default model settings substantially over-predict basal area and total stand carbon after 50 years for both pure lodgepole pine stands and mixed lodgepole pine stands containing a minor proportion of Engelmann spruce, subalpine fir, and quaking aspen. Using equivalence testing to validate the large-tree diameter increment model against a separate dataset of 3,790 remeasured trees on 124 lodgepole pine FIA plots, we found that the default FVS–CR LP model adequately predicts lodgepole diameter growth, though we observed large variation in model errors. Equivalence tests also revealed systematic under-estimation of spruce–fir and over-estimation of aspen productivity. Finally, we conducted a sensitivity analysis to identify the most important model parameters and data inputs driving simulated stand structure and carbon accumulation in both the short- (50 years) and long-term (200 years). The model shows the greatest sensitivity to initial species composition; a small proportion of aspen, spruc

Improved management practices should be implemented in croplands in sub-Saharan Africa to enhance soil organic carbon (SOC) storage and/or reduce losses associated with land-use change, thereby addressing the challenge of ongoing soil degradation. DayCent, a process-based biogeochemical model, provides a useful tool for evaluating which management practices are most effective for SOC sequestration. Here, we used the DayCent model to simulate SOC using experimental data from two long-term field sites in western Kenya comprising of two widely promoted sustainable agricultural management practices: integrated nutrient management (i.e. mineral fertilizer and crop residues/farmyard manure incorporation) and conservation agriculture (i.e. minimum tillage and crop residue retention). At both sites, correlations between measured and simulated SOC were low to moderate (R2 of 0.25−0.55), and in most cases, the model produced fairly accurate prediction of the SOC trends with a low relative root mean squared error (RRMSE < 7%). Consistent with field measurements, simulated SOC declined under all improved management practices. The model projected annual SOC loss rates of between 0.32 to 0.35 Mg C ha-1 yr-1 in continuously tilled maize (Zea mays) systems without fertilizer or organic matter application over the period 2003–2050. The most effective practices in reducing the losses were the combined application of 4 Mg ha-1 of farmyard manure and 2 Mg ha-1 of maize residue retention (reducing losses up to 0.22 Mg C ha-1 yr-1), minimum tillage in combination with maize residue retention (0.21 Mg C ha-1 yr-1), and rotation of maize with soybean (Glycine max) under minimum tillage (0.17 Mg C ha-1 yr-1). Model results suggest that response of the passive SOC pool to the different management practices is a key driver of the long-term SOC trends at the two study sit

We demonstrate the use of a surrogate-based optimization framework for large-scale and high-resolution landscape management optimization, using irrigated corn production systems in eastern Colorado, USA as a case study. An artificial neural network was employed to create a surrogate of the DayCent biogeochemical simulation model. Our optimization considered trade-offs among seven different objectives at different scales, including farm profits, irrigation water use, corn grain, corn stover, soil organic carbon (SOC), greenhouse gas (GHG) emissions, and nitrogen leaching. The results show that the surrogate captured greater than 99% of the variations in the DayCent's simulated outputs and was 6.2 million times faster than the DayCent model for our analysis. Farm-level optimization increased farm profits by 83%–150%, SOC by 16%–53%, grain yield by 10.1–11.3%, and reduced GHG emissions by 19%–55% compared to the ‘business-as-usual’ scenario.