A sound understanding of a cropping system model with the global sensitivity analysis

The capability of cropping system models of depicting the crop and soil-related processes implies a high number of parameters. The aim of this work was to detect the key parameters, and the associated processes, of the ARMOSA cropping system model, considering two target outputs, crop yield and nitrogen leaching. A global sensitivity analysis (SA) was carried out in two steps: (1) the Morris method considering the whole set of parameters; (2) Sobol analysis was applied to the Morris outcome. The simulation was run on winter wheat in four soil types in Marchfeld (Austria, 2010–2018). Parameters affecting crop yield was the critical nitrogen concentration, the potential CO2 assimilation rate, and the drought sensitivity parameter. Nitrogen leaching was mainly affected by the decomposition of litter and the early aboveground biomass growth. The parameters ranking did not appreciably change across soil types. This study offers a quick and replicable methodology for model calibration

Modelling of Soil Organic Carbon Dynamics in Kazakhstan

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Deliberative processes for comprehensive evaluation of agro-ecological models

Biophysical models are acknowledged for examining interactions of agro-ecological systems and fostering communication between scientists, managers and the public. As the role of models grows in importance, there is an increase in the need to assess their quality and performance (Bellocchi et al., 2010). However, the heterogeneity of factors influencing model outputs makes it difficult a full assessment of model features. Where models are used with or for stakeholders then model credibility depends not only on the outcomes of well-structured statistical evaluation but also less tangible factors may need to be addressed using complementary deliberative processes. To expand our horizons in the evaluation of crop and grassland models, approaches have been reviewed with emphasis on using combined metrics. Comprehensive evaluation of simulation models was developed to integrate expectations of stakeholders via a weighting system where lower and upper fuzzy bounds are applied to a set of evaluation metrics. A questionnaire-based survey helped understanding the multi-faceted knowledge and experience required and the substantial challenges posed by the deliberative process. MACSUR knowledge hub holds potential to advance in good modelling practice in relation with model evaluation (including access to appropriate software tools), an activity which is frequently neglected in the context of time-limited projects

Protocol for model evaluation

This deliverable focuses on the development of methods for model evaluation in order to have unambiguous indications derived from the use of several evaluation metrics. The information about model quality is aggregated into a single indicator using a fuzzy expert system that can be applied to a wide range of model estimates where suitable test data are available. This is a cross-cutting activity between CropM (C1.4) and LiveM (L2.2)

Interrelationship between evaluation metrics to assess agro-ecological models

When evaluating the performances of simulation models, the perception of the quality of the outputs may depend on the statistics used to compare simulated and observed data. In order to have a comprehensive understanding of model performance, the use of a variety of metrics is generally advocated. However, since they may be correlated, the use of two or more metrics may convey the same information, leading to redundancy. This study intends to investigate the interrelationship between evaluation metrics, with the aim of identifying the most useful set of indicators, for assessing simulation performance. Our focus is on agro-ecological modelling. Twenty-three performance indicators were selected to compare simulated and observed data of four agronomic and meteorological variables: above-ground biomass, leaf area index, hourly air relative humidity and daily solar radiation . Indicators were calculated on large data sets, collected to effectively apply correlation analysis techniques. For each variable, the interrelationship between each pair of indicators was evaluated, by computing the Spearman’s rank correlation coefficient. A definition of “stable correlation” was proposed, based on the test of heterogeneity, allowing to assess whether two or more correlation coefficients are equal. An optimal subset of indicators was identified, striking a balance between number of indicators, amount of provided information and information redundancy. They are: Index of Agreement, Squared Bias, Root Mean Squared Relative Error, Pattern Index, Persistence Model Efficiency and Spearman’s Correlation Coefficient. The present study was carried out in the context of CropM-LiveM cross-cutting activities of MACSUR knowledge hub

Soil organic carbon under conservation agriculture in Mediterranean and humid subtropical climates: Global meta‐analysis

Conservation agriculture (CA) is an agronomic system based on minimum soil disturbance (no-tillage, NT), permanent soil cover, and species diversification. The effects of NT on soil organic carbon (SOC) changes have been widely studied, showing somewhat inconsistent conclusions, especially in relation to the Mediterranean and humid subtropical climates. These areas are highly vulnerable and predicted climate change is expected to accentuate desertification and, for these reasons, there is a need for clear agricultural guidelines to preserve or increment SOC. We quantitively summarized the results of 47 studies all around the world in these climates investigating the sources of variation in SOC responses to CA, such as soil characteristics, agricultural management, climate, and geography. Within the climatic area considered, the overall effect of CA on SOC accumulation in the plough layer (0–0.3 m) was 12% greater in comparison to conventional agriculture. On average, this result corresponds to a carbon increase of 0.48 Mg C ha−1 year−1. However, the effect was variable depending on the SOC content under conventional agriculture: it was 20% in soils which had ≤ 40 Mg C ha−1, while it was only 7% in soils that had > 40 Mg C ha−1. We proved that 10 years of CA impact the most on soil with SOC ≤ 40 Mg C ha−1. For soils with less than 40 Mg C ha−1, increasing the proportion of crops with bigger residue biomasses in a CA rotation was a solution to increase SOC. The effect of CA on SOC depended on clay content only in soils with more than 40 Mg C ha−1 and become null with a SOC/clay index of 3.2. Annual rainfall (that ranged between 331–1850 mm y−1) and geography had specific effects on SOC depending on its content under conventional agriculture. In conclusion, SOC increments due to CA application can be achieved especially in agricultural soils with less than 40 Mg C ha−1 and located in the middle latitudes or in the dry conditions of Mediterranean and humid subtropical climates

Modeling of soil organic carbon and carbon balance under conservation agriculture in Kazakhstan

Traditional farming systems, involving intensive tillage, returning the low amounts of organic matter to field and frequently monoculture, lead to a decrease in soil organic carbon (SOC) and land degradation. In contrast, conservation agriculture (CA) has a large potential for carbon sequestration. However, the efficacy of no-till agriculture for increasing C in soils has been questioned in recent studies. These doubts stem from the facts that previous literature on soil C stocks has often discussed effects of tillage, rotations, and residue management separately. The objectives of this study are (1) to assess the potential of each CA component for soil C sequestration in Almaty state (Kazakhstan), proposing a methodology that could be extended to other conditions in Kazakhstan; and (2) to estimates CO2 balance and possibility to obtain carbon credits. Modeled results showed that no tillage with crop rotation and residue retained and/or cover crop increased SOC by about 300–1 000 kg-1 ha-1 yr-1 in the ploughing layer. It seems that the contribution of each CA element into SOC stock decreased in the following order: cover crops > residues > rotation. In particular, attention should be paid to cover crops, which seem to have significant role in C sequestration, but are not yet widely spread in practical farming in Kazakhstan. Conservation agricultural practices involving, in addition to no-tillage, crop rotation, residues retained and/or cover crops allowed achieving the objective of 4 per 1 000 initiatives. The initiative claims that an annual growth rate of 0.4 percent in the soil carbon stocks, or 4‰ per year, would halt the increase in the CO2 concentration in the atmosphere related to human activities. In addition, these CA practices had the negative total carbon balance indicating reduction of GHG emissions and indicating possibility to obtain carbon credits.202

Exploding the myths: An introduction to artificial neural networks for prediction and forecasting

Artificial Neural Networks (ANNs), sometimes also called models for deep learning, are used extensively for the prediction of a range of environmental variables. While the potential of ANNs is unquestioned, they are surrounded by an air of mystery and intrigue, leading to a lack of understanding of their inner workings. This has led to the perpetuation of a number of myths, resulting in the misconception that applying ANNs primarily involves "throwing" a large amount of data at "black-box" software packages. While this is a convenient way to side-step the principles applied to the development of other types of models, this comes at significant cost in terms of the usefulness of the resulting models. To address these issues, this inroductory overview paper explodes a number of the common myths surrounding the use of ANNs and outlines state-of-the-art approaches to developing ANNs that enable them to be applied with confidence in practice

Agricultural Production and Externalities Simulator (APES) prototype to be used in Prototype 1 of SEAMLESS-IF

Production Economics,