Citizen observatory based soil moisture monitoring – The GROW example

GROW Observatory is a project funded under the European Union’s Horizon 2020 research and innovation program. Its aim is to establish a large scale (more than 20,000 participants), resilient and integrated ‘Citizen Observatory’ (CO) and community for environmental monitoring that is self-sustaining beyond the life of the project. This article describes how the initial framework and tools were developed to evolve, bring together and train such a community; raising interest, engaging participants, and educating to support reliable observations, measurements and documentation, and considerations with a special focus on the reliability of the resulting dataset for scientific purposes. The scientific purposes of GROW observatory are to test the data quality and the spatial representativity of a citizen engagement driven spatial distribution as reliably inputs for soil moisture monitoring and to create timely series of gridded soil moisture products based on citizens’ observations using low cost soil moisture (SM) sensors, and to provide an extensive dataset of in situ soil moisture observations which can serve as a reference to validate satellite-based SM products and support the Copernicus in situ component. This article aims to showcase the initial steps of setting up such a monitoring network that has been reached at the mid-way point of the project’s funded period, focusing mainly on the design and development of the CO monitoring network

Promoting sustainable agricultural intensification and crowdsourcing plot information

Provision of adequate and timely information to farmers on the ground means optimizing crop production decisions, reducing costs and eliminating adverse effects of overuse of agricultural inputs, e.g. fertilizer. AgroTutor aims to support farmers across Mexico with benchmarking information, including historical and potential yield on the area where the plot is located, historical costs, income and profit as well as agronomical recommendations. Location and limits of parcels can be saved, and agronomical activities including costs, pictures and videos can be then added to document the cropping system

The Global Gridded Crop Model Intercomparison phase 1 simulation dataset

The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives. © 2019, The Author(s)

Quantifying carbon for agricultural soil management: from the current status toward a global soil information system

The importance of building/maintaining soil carbon, for soil health and CO2 mitigation, is of increasing interest to a wide audience, including policymakers, NGOs and land managers. Integral to any approaches to promote carbon sequestering practices in managed soils are reliable, accurate and cost-effective means to quantify soil C stock changes and forecast soil C responses to different management, climate and edaphic conditions. While technology to accurately measure soil C concentrations and stocks has been in use for decades, many challenges to routine, cost-effective soil C quantification remain, including large spatial variability, low signal-to-noise and often high cost and standardization issues for direct measurement with destructive sampling. Models, empirical and process-based, may provide a cost-effective and practical means for soil C quantification to support C sequestration policies. Examples are described of how soil science and soil C quantification methods are being used to support domestic climate change policies to promote soil C sequestration on agricultural lands (cropland and grazing land) at national and provincial levels in Australia and Canada. Finally, a quantification system is outlined – consisting of well-integrated data-model frameworks, supported by expanded measurement and monitoring networks, remote sensing and crowd-sourcing of management activity data – that could comprise the core of a new global soil information system

Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications

Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark

Uncertainty in soil data can outweigh climate impact signals in crop yield simulations

Paper contact with cynthia festin: [email protected]ïments: C.F. was partly supported by a Research Fellowship of the Center for Advanced Studies of LMU Munich. We thank Joshua Elliott from the Global Gridded Crop Model Intercomparison (GGCMI) project for processing climate input data and the GGCMI and ISI-MIP project teams for providing various input data used in this study.Global gridded crop models (GGCMs) are increasingly used for agro-environmental assessments and estimates of climate change impacts on food production. Recently, the influence of climate data and weather variability on GGCM outcomes has come under detailed scrutiny, unlike the influence of soil data. Here we compare yield variability caused by the soil type selected for GGCM simulations to weather-induced yield variability. Without fertilizer application, soil-type-related yield variability generally outweighs the simulated inter-annual variability in yield due to weather. Increasing applications of fertilizer and irrigation reduce this variability until it is practically negligible. Importantly, estimated climate change effects on yield can be either negative or positive depending on the chosen soil type. Soils thus have the capacity to either buffer or amplify these impacts. Our findings call for improvements in soil data available for crop modelling and more explicit accounting for soil variability in GGCM simulations

Global Homogeneous Response Units

The concept of homogenous response units (HRU) was designed as a general concept for the delineation of basic spatial units. Only those characteristics of landscape, which are relatively stable over time (even under climate change) and largely unsusceptible to anthropogenic influence, were selected. The HRU can be seen as a basic spatial framework for the implementation of climate change and land management alternative scenarios into global modeling and therefore is a basic input for delineation of landscape units. HRUs are defined based on classifications of altitude (five classes: 1 (0 - 300m), 2 (300 - 600m), 3 (600 - 1100m), 4 (1100 - 2500m), 5 (> 2500m)), slope (seven classes(degrees): 1 (0 - 3), 2 (3 - 6), 3 (6 - 10), 4 (10 - 15), 5 (15 - 30), 6 (30 - 50), 7 (> 50)) and soil composition (five classes: 1 (sandy), 2 (loamy), 3 (clay), 4 (stony), 5 (peat)). e.g. HRU111 refers to Altitude class 1: 0-300m; Slope class 1: 0-3 degrees; and Soil class 1: sandy. Areas of non-soil are assigned 88. HRUs have a spatial resolution of approximately 10 km**2

The global cropland-sparing potential of high-yield farming

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