Innovative cropping systems designed to reach both environmental and production targets: Data set of biotic and abiotic variables from a twelve-year French field trial.

The data set describes variables collected from a French (N 48.84°, E 1.95°) field trial, over a twelve-year period (2009-2020), in which four innovative cropping systems designed to reach multiple environmental and production goals were assessed. The four cropping systems were designed with new combinations of agricultural practices; they differed in terms of pesticide uses, nitrogen inputs, tillage practices, and crop sequences. Both biotic and abiotic variables were measured. In a previous data paper, we focused on nitrogen fluxes collected from two systems, over eight years (2009-2016). In the present one, we enlarge the scope of the variables, including more crop descriptions and environmental indicators, from all four systems, and over a longer period (2009-2020). The biotic data are: growth stages; aboveground plant nitrogen content and biomass collected at different growth stages, depending on the species; yield components of all the crops; and yield harvested with a combine machine. No weed, crop disease, and pest data are described. The abiotic data are physical and chemical properties of the soil (i.e. texture, calcium carbonate content, pH, organic carbon contents, and nitrogen contents) collected at different assessment periods. All agricultural practices, and climate were regularly recorded, and the treatment frequency indexes and the energy consumptions were computed. These data could be used for benchmarking, to design low-input systems, to improve models for parameterization and validation, and to increase the predictive accuracy of models of crop growth and development, specifically for orphan species such as linseed, faba bean or hemp, and for soil carbon and soil nitrogen fluxes in various conditions.

The AMG model coupled with Rock-Eval® analysis accurately predicts cropland soil organic carbon dynamics in the Tuojiang River Basin, Southwest China.

Accurate soil organic carbon models are key to understand the mechanisms governing carbon sequestration in soil and to help develop targeted management strategies to carbon budget. The accuracy and reliability of soil organic carbon (SOC) models remains strongly limited by incorrect initialization of the conceptual kinetic pools and lack of stringent model evaluation using time-series datasets. Notably, due to legacy effects of management and land use change, the traditional spin-up approach for initial allocation of SOC among kinetic pools can bring substantial uncertainties in predicting the evolution of SOC stocks. The AMG model can fulfill these conditions as it is a parsimonious yet accurate SOC model using widely-available input data. In this study, we first evaluated the performance of AMGv2 before and after optimizing the potential mineralization rate (k0) of SOC stock following a leave-one-site-out cross-validation based on 24 long-term field experiments (LTEs) in the Southwest of China. Then, we used Rock-Eval® thermal analysis results as input variables in the PARTYSOC machine learning model to estimate the initial stable SOC fraction (CS/C0) for the 14 LTEs where soil samples were available. The results showed that initializing the CS/C0 ratio using PARTYSOC combined with the optimized k0 further improved the accuracy of model simulations (R2 = 0.87, RMSE = 0.25, d = 0.90). Combining average measured CS/C0 and k0 optimization across all 24 LTEs also improved the model predictive capability by 25% compared to using default parameterization, thus suggesting promising avenue for upscaling model applications at the regional level where only a few measurement data on SOC stability can be available. In conclusion, the new version of the AMG model developed in the Tuojiang River Basin context exhibits excellent performance. This result paves the way for further calibration and validation of the AMG model in a wider set of contexts, with the potential to significantly improve confidence in SOC predictions in croplands over regional scales.

Dataset of biomass and chemical quality of crop residues from European areas.

This dataset presents the chemical characteristics of plant biomass and crop residues from agrosystems in European areas (carbon and nitrogen contents and biochemical composition). These data have been collected from the scientific literature. The specific data and their origins are presented. The mean values from these data are also provided by major production type (main crops, forage and pasture crops, green manure and cover crops, vegetable crops and energy crops), species and litter type. These data were collected as part of the framework of the European project ResidueGas (ERA-GAS, 2017-2021), which aims to improve the estimation of greenhouse gas emissions associated with crop residues.

Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils.

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.