Silica fertilization improved wheat performance and increased phosphorus concentrations during drought at the field scale

AbstractDrought and the availability of mineable phosphorus minerals used for fertilization are two of the important issues agriculture is facing in the future. High phosphorus availability in soils is necessary to maintain high agricultural yields. Drought is one of the major threats for terrestrial ecosystem performance and crop production in future. Among the measures proposed to cope with the upcoming challenges of intensifying drought stress and to decrease the need for phosphorus fertilizer application is the fertilization with silica (Si). Here we tested the importance of soil Si fertilization on wheat phosphorus concentration as well as wheat performance during drought at the field scale. Our data clearly showed a higher soil moisture for the Si fertilized plots. This higher soil moisture contributes to a better plant performance in terms of higher photosynthetic activity and later senescence as well as faster stomata responses ensuring higher productivity during drought periods. The plant phosphorus concentration was also higher in Si fertilized compared to control plots. Overall, Si fertilization or management of the soil Si pools seem to be a promising tool to maintain crop production under predicted longer and more serve droughts in the future and reduces phosphorus fertilizer requirements.

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Terrestrial Ecosystem Process Model Biome-BGCMuSo: Summary of improvements and new modeling possibilities

10.5194/gmd-2016-93 ◽

2016 ◽

Author(s):

Dóra Hidy ◽

Zoltán Barcza ◽

Hrvoje Marjanović ◽

Maša Zorana Ostrogović Sever ◽

Laura Dobor ◽

...

Keyword(s):

Soil Moisture ◽

Process Model ◽

Management Practices ◽

Terrestrial Ecosystem ◽

Terrestrial Ecosystems ◽

Fertilizer Application ◽

Biogeochemical Model ◽

Managed Forest ◽

Carbon And Nitrogen ◽

Related Plant

Abstract. The process-based biogeochemical model Biome-BGC was enhanced to improve its ability to simulate carbon, nitrogen and water cycles of various terrestrial ecosystems under contrasting management activities. Biome-BGC version 4.1.1 was used as base model. Improvements included addition of new modules such as the multilayer soil module, implementation of processes related to soil moisture and nitrogen balance, soil moisture related plant senescence, and phenological development. Vegetation management modules with annually varying options were also implemented to simulate management practices of grasslands (mowing, grazing), croplands (ploughing, fertilizer application, planting, harvesting), and forests (thinning). New carbon and nitrogen pools have been defined to simulate yield and soft stem development of herbaceous ecosystems. The model version containing all developments is referred to as Biome-BGCMuSo (Biome-BGC with multi-layer soil module). Case studies on a managed forest, cropland and grassland are presented to demonstrate the effect of model developments on the simulation of plant growth as well as on carbon and water balance.

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Terrestrial ecosystem process model Biome-BGCMuSo v4.0: summary of improvements and new modeling possibilities

Geoscientific Model Development ◽

10.5194/gmd-9-4405-2016 ◽

2016 ◽

Vol 9 (12) ◽

pp. 4405-4437 ◽

Cited By ~ 17

Author(s):

Dóra Hidy ◽

Zoltán Barcza ◽

Hrvoje Marjanović ◽

Maša Zorana Ostrogović Sever ◽

Laura Dobor ◽

...

Keyword(s):

Soil Moisture ◽

Process Model ◽

Management Practices ◽

Terrestrial Ecosystem ◽

Terrestrial Ecosystems ◽

Fertilizer Application ◽

Biogeochemical Model ◽

Managed Forest ◽

Carbon And Nitrogen ◽

Related Plant

Abstract. The process-based biogeochemical model Biome-BGC was enhanced to improve its ability to simulate carbon, nitrogen, and water cycles of various terrestrial ecosystems under contrasting management activities. Biome-BGC version 4.1.1 was used as a base model. Improvements included addition of new modules such as the multilayer soil module, implementation of processes related to soil moisture and nitrogen balance, soil-moisture-related plant senescence, and phenological development. Vegetation management modules with annually varying options were also implemented to simulate management practices of grasslands (mowing, grazing), croplands (ploughing, fertilizer application, planting, harvesting), and forests (thinning). New carbon and nitrogen pools have been defined to simulate yield and soft stem development of herbaceous ecosystems. The model version containing all developments is referred to as Biome-BGCMuSo (Biome-BGC with multilayer soil module; in this paper, Biome-BGCMuSo v4.0 is documented). Case studies on a managed forest, cropland, and grassland are presented to demonstrate the effect of model developments on the simulation of plant growth as well as on carbon and water balance.

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Field Data Forecasting Using LSTM and Bi-LSTM Approaches

Applied Sciences ◽

10.3390/app112411820 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11820

Author(s):

Paweena Suebsombut ◽

Aicha Sekhari ◽

Pradorn Sureephong ◽

Abdelhak Belhi ◽

Abdelaziz Bouras

Keyword(s):

Soil Moisture ◽

Crop Yield ◽

Crop Production ◽

Cross Validation ◽

Short Term Memory ◽

Weather Forecasting ◽

Irrigation Scheduling ◽

The Future ◽

Cross Validation Error ◽

Crop Irrigation

Water, an essential resource for crop production, is becoming increasingly scarce, while cropland continues to expand due to the world’s population growth. Proper irrigation scheduling has been shown to help farmers improve crop yield and quality, resulting in more sustainable water consumption. Soil Moisture (SM), which indicates the amount of water in the soil, is one of the most important crop irrigation parameters. In terms of water usage optimization and crop yield, estimating future soil moisture (forecasting) is an essentially valuable task for crop irrigation. As a result, farmers can base crop irrigation decisions on this parameter. Sensors can be used to estimate this value in real time, which may assist farmers in deciding whether or not to irrigate. The soil moisture value provided by the sensors, on the other hand, is instantaneous and cannot be used to directly compute irrigation parameters such as the best timing or the required water quantity to irrigate. The soil moisture value can, in fact, vary greatly depending on factors such as humidity, weather, and time. Using machine learning methods, these parameters can be used to predict soil moisture levels in the near future. This paper proposes a new Long-Short Term Memory (LSTM)-based model to forecast soil moisture values in the future based on parameters collected from various sensors as a potential solution. To train and validate this model, a real-world dataset containing a set of parameters related to weather forecasting, soil moisture, and other related parameters was collected using smart sensors installed in a greenhouse in Chiang Mai province, Thailand. Preliminary results show that our LSTM-based model performs well in predicting soil moisture with a 0.72% RMSE error and a 0.52% cross-validation error (LSTM), and our Bi-LSTM model with a 0.76% RMSE error and a 0.57% cross-validation error. In the future, we aim to test and validate this model on other similar datasets.

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Disclosing the future food security risk of China based on crop production and water scarcity under diverse socioeconomic and climate scenarios

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.148110 ◽

2021 ◽

pp. 148110

Author(s):

Lei Chen ◽

Jianxia Chang ◽

Yimin Wang ◽

Aijun Guo ◽

Yuanyuan Liu ◽

...

Keyword(s):

Food Security ◽

Water Scarcity ◽

Crop Production ◽

Climate Scenarios ◽

Security Risk ◽

The Future

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Potassium Control of Plant Functions: Ecological and Agricultural Implications

Plants ◽

10.3390/plants10020419 ◽

2021 ◽

Vol 10 (2) ◽

pp. 419

Author(s):

Jordi Sardans ◽

Josep Peñuelas

Keyword(s):

Food Security ◽

Stress Responses ◽

Crop Production ◽

Terrestrial Ecosystem ◽

Terrestrial Ecosystems ◽

Wood Formation ◽

Cellular Growth ◽

Ecosystem Functions ◽

Metabolite Transport ◽

Physiological Functions

Potassium, mostly as a cation (K+), together with calcium (Ca2+) are the most abundant inorganic chemicals in plant cellular media, but they are rarely discussed. K+ is not a component of molecular or macromolecular plant structures, thus it is more difficult to link it to concrete metabolic pathways than nitrogen or phosphorus. Over the last two decades, many studies have reported on the role of K+ in several physiological functions, including controlling cellular growth and wood formation, xylem–phloem water content and movement, nutrient and metabolite transport, and stress responses. In this paper, we present an overview of contemporary findings associating K+ with various plant functions, emphasizing plant-mediated responses to environmental abiotic and biotic shifts and stresses by controlling transmembrane potentials and water, nutrient, and metabolite transport. These essential roles of K+ account for its high concentrations in the most active plant organs, such as leaves, and are consistent with the increasing number of ecological and agricultural studies that report K+ as a key element in the function and structure of terrestrial ecosystems, crop production, and global food security. We synthesized these roles from an integrated perspective, considering the metabolic and physiological functions of individual plants and their complex roles in terrestrial ecosystem functions and food security within the current context of ongoing global change. Thus, we provide a bridge between studies of K+ at the plant and ecological levels to ultimately claim that K+ should be considered at least at a level similar to N and P in terrestrial ecological studies.

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Soil moisture retrieval from remote sensing measurements: Current knowledge and directions for the future

Earth-Science Reviews ◽

10.1016/j.earscirev.2021.103673 ◽

2021 ◽

pp. 103673

Author(s):

Zhao-Liang Li ◽

Pei Leng ◽

Cheng-Hu Zhou ◽

Kun-Shan Chen ◽

Fang-Cheng Zhou ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Current Knowledge ◽

The Future

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Anthropogenic warming and intraseasonal summer monsoon variability amplify the risk of future flash droughts in India

npj Climate and Atmospheric Science ◽

10.1038/s41612-020-00158-3 ◽

2021 ◽

Vol 4 (1) ◽

Cited By ~ 1

Author(s):

Vimal Mishra ◽

Saran Aadhar ◽

Shanti Shwarup Mahto

Keyword(s):

Soil Moisture ◽

Summer Monsoon ◽

Crop Production ◽

Monsoon Rainfall ◽

Root Zone ◽

Monsoon Season ◽

Intraseasonal Variability ◽

Positive Temperature ◽

Groundwater Abstraction ◽

Increased Risk

AbstractFlash droughts cause rapid depletion in root-zone soil moisture and severely affect crop health and irrigation water demands. However, their occurrence and impacts in the current and future climate in India remain unknown. Here we use observations and model simulations from the large ensemble of Community Earth System Model to quantify the risk of flash droughts in India. Root-zone soil moisture simulations conducted using Variable Infiltration Capacity model show that flash droughts predominantly occur during the summer monsoon season (June–September) and driven by the intraseasonal variability of monsoon rainfall. Positive temperature anomalies during the monsoon break rapidly deplete soil moisture, which is further exacerbated by the land-atmospheric feedback. The worst flash drought in the observed (1951–2016) climate occurred in 1979, affecting more than 40% of the country. The frequency of concurrent hot and dry extremes is projected to rise by about five-fold, causing approximately seven-fold increase in flash droughts like 1979 by the end of the 21st century. The increased risk of flash droughts in the future is attributed to intraseasonal variability of the summer monsoon rainfall and anthropogenic warming, which can have deleterious implications for crop production, irrigation demands, and groundwater abstraction in India.

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Assessment of Field-Scale Leaching Patterns for Management of Nitrogen Fertilizer Application

Field-Scale Water and Solute Flux in Soils ◽

10.1007/978-3-0348-9264-3_8 ◽

1990 ◽

pp. 55-63 ◽

Cited By ~ 4

Author(s):

D. J. Mulla ◽

J. G. Annandale

Keyword(s):

Nitrogen Fertilizer ◽

Fertilizer Application ◽

Field Scale

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How will future climate depending agronomic management impact the yield risk of wheat cropping systems? A regional case study of Eastern Denmark

The Journal of Agricultural Science ◽

10.1017/s0021859620001045 ◽

2021 ◽

pp. 1-16

Author(s):

J. Macholdt ◽

J. Glerup Gyldengren ◽

E. Diamantopoulos ◽

M. E. Styczen

Keyword(s):

Climate Change ◽

Crop Production ◽

Cropping Systems ◽

Future Climate ◽

Catch Crops ◽

Agronomic Management ◽

The Future ◽

Yield Risk ◽

Management Factors ◽

The Impact

Abstract One of the major challenges in agriculture is how climate change influences crop production, for different environmental (soil type, topography, groundwater depth, etc.) and agronomic management conditions. Through systems modelling, this study aims to quantify the impact of future climate on yield risk of winter wheat for two common soil types of Eastern Denmark. The agro-ecosystem model DAISY was used to simulate arable, conventional cropping systems (CSs) and the study focused on the three main management factors: cropping sequence, usage of catch crops and cereal straw management. For the case region of Eastern Denmark, the future yield risk of wheat does not necessarily increase under climate change mainly due to lower water stress in the projections; rather, it depends on appropriate management and each CS design. Major management factors affecting the yield risk of wheat were N supply and the amount of organic material added during rotations. If a CS is characterized by straw removal and no catch crop within the rotation, an increased wheat yield risk must be expected in the future. In contrast, more favourable CSs, including catch crops and straw incorporation, maintain their capacity and result in a decreasing yield risk over time. Higher soil organic matter content, higher net nitrogen mineralization rate and higher soil organic nitrogen content were the main underlying causes for these positive effects. Furthermore, the simulation results showed better N recycling and reduced nitrate leaching for the more favourable CSs, which provide benefits for environment-friendly and sustainable crop production.

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Deriving SAR Soil Moisture Retrieval Algorithms and Soil Drainage Classification for Boreal Peatlands

10.5194/egusphere-egu21-6503 ◽

2021 ◽

Author(s):

Laura Bourgeau-Chavez ◽

Jeremy Graham ◽

Andrew Poley ◽

Dorthea Leisman ◽

Michael Battaglia

Keyword(s):

Climate Change ◽

Soil Moisture ◽

Land Surface ◽

Ground Surface ◽

Terrestrial Ecosystem ◽

Independent Study ◽

Additive Models ◽

Soil Drainage ◽

Boreal Peatlands ◽

Retrieval Algorithms

Eighty percent of global peatlands are distributed across the boreal and subarctic regions, storing an estimated 30% of earth&#8217;s soil organic carbon (1,016 to 1,105 Gt C) despite representing only about 3% of the global land surface. The accumulation of C in peatlands generally depends on hydrologic conditions that maintain saturated soils and impede rates of decomposition. Boreal Peatlands have provided rich reservoirs of stored C for millennia. However, with climate change, warming and drying patterns across the boreal and arctic are resulting in dramatic changes in ecosystems and putting these systems at risk of changing from a C sink to a source.&#160; Recent changes in climate including earlier springs, longer summers and changes in moisture patterns across the landscape, are affecting wildfire regimes of the boreal region including intensity, severity and frequency of wildfires. This in turn has potential to cause shifts in successional trajectories.&#160; Understanding how these changes in climate are affecting peatlands and their vulnerability to wildfire has been a focus of study of the research team since 2009. &#160;Soil moisture is one variable which can provide information to understand wildfire behavior including the depth of peat consumption in these wildfires but it also has a direct effect on post-fire successional trajectories. Further it is needed to understand methane emissions from peatlands.&#160; To develop the soil moisture retrieval algorithms, we studied a range of boreal peatland sites (bogs and fens) stratified across geographic regions from 2012-2014.&#160; We developed soil moisture retrieval algorithms from polarimetric C-band (5.7 cm wavelength) synthetic aperture radar (SAR) data.&#160; Peatlands have low enough aboveground biomass (<3.0 kg/m2) to allow this shorter wavelength SAR to penetrate the canopy to reach the ground surface.&#160; Data from over 60, 4 ha sites were collected over 3 seasons from Alaska and Michigan USA and Alberta Canada.&#160; Both multi-linear regressions and general additive models (GAM) were developed.&#160; Using both polarimetric SAR parameters that are sensitive to vegetation structure and parameters most sensitive to surface soil moisture in the models provided the best results.&#160; GAM models were tested in an independent study area, Northwest Territories (NWT), Canada.&#160; The sites of NWT were sampled in 2016-2019 coincident to Radarsat-2 polarimetric image collections.&#160; The high accuracy results will be presented as well as methods developed to use multidate C-band data from Sentinel-1 to classify soil drainage (well drained to poorly drained) in recently burned peatlands.&#160; These products are being used in a fire effects and emissions model, CanFIRE, as we parameterize it for peatlands; as well as the Functionally-Assembled Terrestrial Ecosystem Simulator (FATES) to understand the effects of wildfire and hydrology on peatland ecosystems.&#160; Characterization and quantification of boreal peatlands in global C cycling is critical for proper accounting given that peatlands play a significant role in sequestering and releasing large amounts of C. The ability to retrieve soil moisture from C-band SAR, therefore, provides a means to monitor a key variable in scaling C flux estimates as well as understanding the vulnerability and resiliency of boreal peatlands to climate change.&#160;

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