Assessment of Maize Yield Response to Agricultural Management Strategies Using the DSSAT-CERES-Maize Model in Trans Nzoia County in Kenya

Maize production in low-yielding regions is influenced by climate variability, poor soil fertility, suboptimal agronomic practices, and biotic influences, among other limitations. Therefore, the assessment of yields to various management practices is, among others, critical for advancing site-specific measures for production enhancement. In this study, we conducted a multiseason calibration and evaluation of the DSSAT-CERES-Maize model to assess the maize yield response of two common cultivars grown in Trans Nzoia County in Kenya under various agricultural strategies, such as sowing dates, nitrogen fertilization, and water management. We then applied the Mann-Kendall (MK), and Sen's Slope Estimator (SSE) tests to establish the yield trends and magnitudes of the different strategies. The evaluated model simulated long-term yields (1984-2021) and characterized production under various weather regimes. The model performed well in simulating the growth and development of the two cultivars, as indicated by the model evaluation results. The RMSE for yield was 333 and 239 kg ha(-1) for H614 and KH600-23A, respectively, representing a relative error (RRMSE) of 8.1 and 5.1%. The management strategies assessment demonstrated significant feedback on sowing dates, nitrogen fertilization, and cultivars on maize yield. The sowing date conducted in mid-February under fertilization of 100 kg of nitrogen per hectare proved to be the best strategy for enhancing grain yields in the region. Under the optimum sowing dates and fertilization rate, the average yield for cultivar KH600-23A was 7.1% higher than that for H614. The MK and SSE tests revealed a significant (p < 0.05) modest downwards trend in the yield of the H614 cultivar compared to the KH600-23A. The eastern part of Trans Nzoia County demonstrated a consistent downwards trend for the vital yield enhancement strategies. Medium to high nitrogen levels revealed positive yield trends for more extensive coverage of the study area. Based on the results, we recommend the adoption of the KH600-23A cultivar which showed stability in yields under optimum nitrogen levels. Furthermore, we recommend measures that improve soil quality and structure in the western and northern parts, given the negative model response on maize yield in these areas. Knowledge of yield enhancement strategies and their spatial responses is of utmost importance for precision agricultural initiatives and optimization of maize production in Trans Nzoia County

Evaluating the value of a network of cosmic-ray probes for improving land surface modelling

Land surface models can model matter and energy fluxes between the land surface and atmosphere, and provide a lower boundary condition to atmospheric circulation models. For these applications, accurate soil moisture quantification is highly desirable but not always possible given limited observations and limited subsurface data accuracy. Cosmic-ray probes (CRPs) offer an interesting alternative to indirectly measure soil moisture and provide an observation that can be assimilated into land surface models for improved soil moisture prediction. Synthetic studies have shown the potential to estimate subsurface parameters of land surface models with the assimilation of CRP observations. In this study, the potential of a network of CRPs for estimating subsurface parameters and improved soil moisture states is tested in a real-world case scenario using the local ensemble transform Kalman filter with the Community Land Model. The potential of the CRP network was tested by assimilating CRP-data for the years 2011 and 2012 (with or without soil hydraulic parameter estimation), followed by the verification year 2013. This was done using (i) the regional soil map as input information for the simulations, and (ii) an erroneous, biased soil map. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the biased soil map, soil moisture characterization improved in both periods strongly from a ERMS of 0.11 cm3/cm3 to 0.03 cm3/cm3 (assimilation period) and from 0.12 cm3/cm3 to 0.05 cm3/cm3 (verification period) and the estimated soil hydraulic parameters were after assimilation closer to the ones of the regional soil map. Finally, the value of the CRP network was also evaluated with jackknifing data assimilation experiments. It was found that the CRP network is able to improve soil moisture estimates at locations between the assimilation sites from a ERMS of 0.12 cm3/cm3 to 0.06 cm3/cm3 (verification period), but again only if the initial soil map was biased

Amplifying Signals and avoiding surprises: Potential synergies between ICOS and eLTER at the Water-Climate-Greenhouse Gas nexus

Environmental thresholds. tipping points and subsequent regime shifts associated with the water/climate/greenhouse gas nexus pose a genuine threat to sustainability. Both the ongoing forest dieback in Central Europe caused by the extreme droughts of the last years and the effect of global warming on ecosystem functioning have the potential to cause ecological surprise (sensu Lindenmayer et al. 2010) where ecosystems are pushed into new, unexpected and usually undesirable states. Formulating appropriate scientific and societal responses to such regime shifts requires breadth, depth, intensity and duration of environmental, ecological and socio-ecological monitoring. Broad geographic coverage to encompass relevant biophysical and societal gradients, consideration of all appropriate parameters, adequate measurement frequency and long-term, standardized observations are all needed to provide reliable early warnings of severe environmental change, test ecosystem models, avoid double counting in carbon accounting and to reduce the likelihood of undesirable ecological outcomes. This is especially true of events driven by simultaneous changes in climate, the water cycle and human activities. Well-supported, site-based research infrastructures (RIs; e.g., eLTER and ICOS) are essential tools with the necessary breadth, depth, intensity and duration for early detection and attribution of environmental change. Individually, the eLTER and ICOS RIs generate a wealth of data supporting the ecosystem and carbon research communities. Achieving synergies between the two RIs can add value to both communities and potentially offer meaningful insight into the European water-climate-greenhouse gas nexus. The unique insights into processes and mechanisms of ecosystem dynamics and functioning obtained from high intensity monitoring conducted by the ICOS RI greatly increase the likelihood of detecting signals of environmental change. These signals must be placed into the context of their long-term trajectory and potential societal and environmental drivers. The spatially extensive, long-term, multi-disciplinary monitoring conducted at LTER sites and LTSER platforms under the umbrella of the eLTER programme can provide this context. Here, we outline one potential roadmap for achieving synergies between the ICOS and eLTER RIs focussing on the value of co-location for improved understanding of the water/climate/greenhouse gas nexus. Based on data and experiences from intensively studied research sites, we highlight some of the possibilities for reducing the likelihood of ecological surprise that could result from such synergies.Peer reviewe

COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors

[EN] Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information. In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability. The dataset, entitled "Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors", is shared via Forschungszentrum Julich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021).We thank TERENO (Terrestrial Environmental Observatories), funded by the Helmholtz-Gemeinschaft for the financing and maintenance of CRNS stations. We acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) of the research unit FOR 2694 Cosmic Sense (grant no. 357874777) and by the German Federal Ministry of Education of the Research BiookonomieREVIER, Digitales Geosystem -Rheinisches Revier project (grant no. 031B0918A). COSMOS-UK has been supported financially by the UK's Natural Environment Research Council (grant no. NE/R016429/1). The Olocau experimental watershed is partially supported by the Spanish Ministry of Science and Innovation through the research project TETISCHANGE (grant no. RTI2018-093717-BI00). The Calderona experimental site is partially supported by the Spanish Ministry of Science and Innovation through the research projects CEHYRFO-MED (grant no. CGL2017-86839C3-2-R) and SILVADAPT.NET (grant no. RED2018-102719-T) and the LIFE project RESILIENT FORESTS (grant no. LIFE17 CCA/ES/000063). The University of Bristol's Sheepdrove sites have been supported by the UK's Natural Environment Research Council through a number of projects (grant nos. NE/M003086/1, NE/R004897/1, and NE/T005645/1) and by the International Atomic Energy Agency of the United Nations (grant no. CRP D12014).Bogena, HR.; Schrön, M.; Jakobi, J.; Ney, P.; Zacharias, S.; Andreasen, M.; Baatz, R.... (2022). COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors. Earth System Science Data. 14(3):1125-1151. https://doi.org/10.5194/essd-14-1125-20221125115114

Spatio-temporal drivers of soil and ecosystem carbon fluxes at field scale in an upland grassland in Germany

Ecosystem carbon (C) fluxes in terrestrial ecosystems are affected by varying environmental conditions (e.g. soil heterogeneity and the weather) and land management. However, the interactions between soil respiration (Rs) and net ecosystem exchange (NEE) and their spatio-temporal dependence on environmental conditions and land management at field scale is not well understood. We performed repeated C flux measurement at 21 sites during the 2013 growing season in a temperate upland grassland in Germany, which was fertilized and cut three times according to the agricultural practice typical of the region. Repeated measurements included determination of NEE, Rs, leaf area index (LAI), meteorological conditions as well as physical and chemical soil properties. Temporal variability of Rs was controlled by air temperature, while LAI influenced the temporal variability of NEE. The three grass cuts reduced LAI and affected NEE markedly. More than 50% of NEE variability was explained by defoliation at field scale. Additionally, soil heterogeneity affected NEE, but to a lower extent (>30%), while Rs remained unaffected. We conclude that grassland management (i.e. repeated defoliation) and soil heterogeneity affects the spatio-temporal variability of NEE at field scale

Steering Operational Synergies in Terrestrial Observation Networks: Opportunity for Advancing Earth System Dynamics Modelling

Advancing our understanding of Earth system dynamics (ESD) depends on the development of models and other analytical tools that apply physical, biological, and chemical data. This ambition to increase understanding and develop models of ESD based on site observations was the stimulus for creating the networks of Long-Term Ecological Research (LTER), Critical Zone Observatories (CZOs), and others. We organized a survey, the results of which identified pressing gaps in data availability from these networks, in particular for the future development and evaluation of models that represent ESD processes, and provide insights for improvement in both data collection and model integration. From this survey overview of data applications in the context of LTER and CZO research, we identified three challenges: (1) widen application of terrestrial observation network data in Earth system modelling, (2) develop integrated Earth system models that incorporate process representation and data of multiple disciplines, and (3) identify complementarity in measured variables and spatial extent, and promoting synergies in the existing observational networks. These challenges lead to perspectives and recommendations for an improved dialogue between the observation networks and the ESD modelling community, including co-location of sites in the existing networks and further formalizing these recommendations among these communities. Developing these synergies will enable cross-site and cross-network comparison and synthesis studies, which will help produce insights around organizing principles, classifications, and general rules of coupling processes with environmental conditions

Leveraging environmental research and observation networks to advance soil carbon science

Soil organic matter (SOM) is a critical ecosystem variable regulated by interacting physical, chemical and biological processes. Collaborative efforts to integrate perspectives, data, and models from interdisciplinary research and observation networks can significantly advance predictive understanding of SOM. We outline how integrating three networks – the Long‐Term Ecological Research, with a focus on ecological dynamics, the Critical Zone Observatories with strengths in landscape/geologic context, and the National Ecological Observatory Network with standardized multi‐scale measurements—can advance SOM knowledge. This integration requires improved data dissemination and sharing, coordinated data collection activities, and enhanced collaboration between empiricists and modelers within and across networks

Remote sensing of geomorphodiversity linked to biodiversity — part III: traits, processes and remote sensing characteristics

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed

COSMOS-Europe : a European network of cosmic-ray neutron soil moisture sensors

We thank TERENO (Terrestrial Environmental Observatories), funded by the Helmholtz-Gemeinschaft for the financing and maintenance of CRNS stations. We acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) of the research unit FOR 2694 Cosmic Sense (grant no. 357874777) and by the German Federal Ministry of Education of the Research BioökonomieREVIER, Digitales Geosystem – Rheinisches Revier project (grant no. 031B0918A). COSMOS-UK has been supported financially by the UK’s Natural Environment Research Council (grant no. NE/R016429/1). The Olocau experimental watershed is partially supported by the Spanish Ministry of Science and Innovation through the research project TETISCHANGE (grant no. RTI2018-093717-BI00). The Calderona experimental site is partially supported by the Spanish Ministry of Science and Innovation through the research projects CEHYRFO-MED (grant no. CGL2017-86839- C3-2-R) and SILVADAPT.NET (grant no. RED2018-102719-T) and the LIFE project RESILIENT FORESTS (grant no. LIFE17 CCA/ES/000063). The University of Bristol’s Sheepdrove sites have been supported by the UK’s Natural Environment Research Council through a number of projects (grant nos. NE/M003086/1, NE/R004897/1, and NE/T005645/1) and by the International Atomic Energy Agency of the United Nations (grant no. CRP D12014). Acknowledgements. We thank Peter Strauss and Gerhab Rab from the Institute for Land and Water Management Research, Federal Agency for Water Management Austria, Petzenkirchen, Austria. We thank Trenton Franz from the School of Natural Resources, University of Nebraska–Lincoln, Lincoln, NE, United States. We also thank Carmen Zengerle, Mandy Kasner, Felix Pohl, and Solveig Landmark, UFZ Leipzig, for supporting field calibration, lab analysis, and data processing. We furthermore thank Daniel Dolfus, Marius Schmidt, Ansgar Weuthen, and Bernd Schilling, Forschungszentrum Jülich, Germany. The COSMOS-UK project team is thanked for making its data available to COSMOS-Europe. Luca Stevanato is thanked for the technical details about the Finapp sensor. The stations at Cunnersdorf, Lindenberg, and Harzgerode have been supported by Falk Böttcher, Frank Beyrich, and Petra Fude, German Weather Service (DWD). The Zerbst site has been supported by Getec Green Energy GmbH and Jörg Kachelmann (Meteologix AG). The CESBIO sites have been supported by the CNES TOSCA program. The ERA5-Land data are provided by ECMWF (Muñoz Sabater, 2021). The Jena dataset was retrieved at the site of The Jena Experiment, operated by DFG research unit FOR 1451.Peer reviewedPublisher PD