Internet of Things and smart sensors in agriculture: Scopes and challenges

Journal of Agriculture and Food Research, Journal Year: 2023, Volume and Issue: 14, P. 100776 - 100776

Published: Sept. 7, 2023

Agriculture is an essential sector needed for survival of the human community. Several measures have been taken to enhance crop production. However harsh environmental conditions and frequent pest infestation lead agricultural loss. In such scenario, integration advanced technologies as sensors coupled with internet things could escalate production minimize economic Studies conducted across world that satisfactorily demonstrated implication integrated IoT-smart in monitoring factors moisture, humidity, temperature soil composition are critical growth. Green house gases carbon dioxide, methane etc also measured through automated sensors. Smart farming enables measurement nitrogen contents helps farmers determine amount fertilizers be used farm lands. Some IoT enabled equipments UAVs useful accurate surveillance attack associated diseases vegetation. Though smart has great scopes future, it faces certain limitations related implementation cost, data security, lack sufficient digital knowledge farmers. Special policies, encryption literacy ease future.

Language: Английский

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Assessing uncertainties and discrepancies in agricultural greenhouse gas emissions estimation in China: A comprehensive review

Environmental Impact Assessment Review, Journal Year: 2024, Volume and Issue: 106, P. 107498 - 107498

Published: March 26, 2024

Language: Английский

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Intercomparison of eddy-covariance software for urban tall-tower sites

Atmospheric measurement techniques, Journal Year: 2024, Volume and Issue: 17(9), P. 2649 - 2669

Published: May 7, 2024

Abstract. Long-term tall-tower eddy-covariance (EC) measurements have been recently established in three European pilot cities as part of the ICOS-Cities project. We conducted a comparison EC software to ensure reliable generation interoperable flux estimates, which is prerequisite for avoiding methodological biases and improving comparability results. analyzed datasets covering 5 months collected from installations located urbanized areas Munich, Zurich, Paris. Fluxes sensible heat, latent CO2 were calculated using packages (i.e., TK3, EddyPro, eddy4R) assess uncertainty estimations attributed differences implemented postprocessing schemes. A very good agreement on mean values standard deviations was found across all sites, can probably be uniform instrumentation, data acquisition, preprocessing. The overall final time series products showed but not yet perfect among packages. TK3 EddyPro both fluxes with low-frequency spectral correction, resulting better than between eddy4R workflow disabled treatment. These observed discrepancies indicate crucial role treating loss estimation systems.

Language: Английский

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Demands and possibilities for field-scale estimation of agricultural greenhouse gas balances

CATENA, Journal Year: 2024, Volume and Issue: 249, P. 108649 - 108649

Published: Dec. 21, 2024

Language: Английский

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Soil carbon sequestration potential in subtropical grasslands estimated by DayCent‐CABBI

Soil Science Society of America Journal, Journal Year: 2025, Volume and Issue: 89(1)

Published: Jan. 1, 2025

Abstract Soil carbon (C) sequestration by restoring degraded grasslands with adequate management practices offers significant opportunities for climate change mitigation while remaining highly uncertain. In this study, a combination of biogeochemical model DayCent‐CABBI and eddy covariance (EC) flux towers was applied to evaluate soil C potential (at depth 0–0.3 m) strategies in subtropical grasslands. calibrated northeast Australia using biomass organic (SOC) data from long‐term trial then fine‐tuned EC tower seven sites the region. The validated cumulative net ecosystem exchange, biomass, SOC, resulting root mean square errors 1.16, 0.88, 2.81 Mg ha −1 , respectively. used project changes SOC stocks under innovative (time‐controlled grazing pasture legume incorporation), estimating 0.37–0.48 0.15–0.26 year toward 2050 respective practices. This study confirms validity Measure, Model, Verification (MMV) approach estimate evaluating methodologies grassland within shorter period than sampling—measuring baseline modeling dynamics DayCent‐CABBI, verifying projected data.

Language: Английский

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Impacts of dairy forage management on soil carbon change and net-zero accounting

Journal of Dairy Science, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

The US Dairy Industry has pledged to achieve net zero greenhouse gas emissions (GHG) by 2050, but reliance on corn (Zea mays L.) silage as a primary forage source undermines progress toward this goal. Soils managed for production are significant of carbon (C) the atmosphere, with soil C losses ranging from 3.7 7.0 Mg ha-1 yr-1 (13.5 25.6 CO2 yr-1) reported in literature. However, biogenic loss not typically represented within C-footprints or life cycle inventories. Using an example dairy farm, we demonstrate that including associated under can increase C-footprint milk nearly 2-fold. We suggest approach represents more accurate estimate impact production, and gains GHG efficiency have come, part, at expense where rotations predominated corn. balance systems likely be improved advanced manure management technologies application strategies return manurial while minimizing N P loading. argue extensive changes cropping will also required. Expanding role perennials winter annual crops rotations; breeding forages greater yield, persistence, deeper root systems; additional creative solutions retain plant-derived soils necessary budgets net-zero targets.

Language: Английский

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Land cover change and socioecological influences on terrestrial carbon production in an agroecosystem

Landscape Ecology, Journal Year: 2023, Volume and Issue: 38(12), P. 3845 - 3867

Published: April 12, 2023

Language: Английский

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Inter-comparison of Eddy-Covariance Software for Urban Tall Tower Sites

Published: Jan. 22, 2024

Abstract. Long-term tall-tower eddy-covariance (EC) measurements have been recently established in three European pilot cities as part of the ICOS-Cities project. We conducted a comparison EC software to ensure reliable generation interoperable flux estimates, which is prerequisite for avoiding methodological biases and improving comparability results. analyzed datasets covering five months collected from installations located urbanized areas Munich, Zurich, Paris. Fluxes sensible heat, latent CO2 were calculated using packages (i.e., TK3, EddyPro, eddy4) assess uncertainty estimations attributed differences implemented post-processing schemes. A very good agreement on mean values standard deviations was found across all sites, can probably be uniform instrumentation, data acquisition, pre-processing. The overall final time-series products showed but not yet perfect among packages. TK3 EddyPro both fluxes with low-frequency spectral correction, resulting better than between eddy4R workflow disabled treatment. These observed discrepancies indicate crucial role treating loss estimation systems.

Language: Английский

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Lower-cost eddy covariance for CO₂ and H₂O fluxes over grassland and agroforestry

Published: March 18, 2024

Abstract. Eddy covariance (EC) measurements can provide direct and non-invasive ecosystem of the exchange energy, water (H2O) carbon dioxide (CO2). However, conventional eddy (CON-EC) setups (ultrasonic anemometer infrared gas analyser) be expensive, which recently led to development lower-cost (LC-EC) setups. In current study we test performance a LC-EC setup for CO2 H2O flux at an agroforestry adjacent grassland site in temperate northern Germany. The closed-path was compared with CON-EC using enclosed-path analyser (LI-7200, LI-COR Inc., Lincoln, NE, USA). fluxes were lower by 7–13 % (R2 = 0.91–0.95) latent heat higher 2–3 2020 23 2021 0.84–0.90). large difference between 2021, seems consequence LE measured CON-EC. Due slower response sensors setup, (co)spectra more attenuated high-frequency range This stronger attenuation requires larger spectral correction as differences factors different methods. At where tower taller grassland, lower, because cospectrum peak energy-containing eddies shift frequencies measure. With systems shown that had 2.3 times uptake both equal evapotranspiration when simultaneously one month. Our results show has potential measure EC various land-use approximately 25 costs system.

Language: Английский

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Predicting the fundamental fluxes of an eddy-covariance station using machine learning methods

Ecological Informatics, Journal Year: 2024, Volume and Issue: 81, P. 102638 - 102638

Published: May 9, 2024

Monitoring tools are needed to maximise living systems' ability mitigate emissions and adapt changing environmental conditions. Therefore, it is important be able predict the fundamental fluxes in crops, this case vineyards, such as sensible heat flux (H), latent (LE) carbon dioxide (CO2), order know their capacity effects of climate change. In study, Linear Regression (LR), Elastic Net (EN) regression, K-Nearest Neighbours (KNN), Gaussian-Process (GP), Decision Tree (TREE) Regression, Random Forest (RF) XGBoost (XGB) Support Vector (SVR) Multi-layer Perceptron (MLP) models have been applied an eddy-covariance station from conventional meteorological parameters. These reproduced well estimations three output parameters station. The performance each predictive model was evaluated using Root-Mean-Squared Error (RMSE), Mean Absolute (MAE), Square (MSE), Percentage (MAPE), Scaled (MASE) coefficient determination (R2). findings indicate that for variable H, GP outperformed SVR all other models, achieving R2 value 0.99. Conversely, demonstrated superior variables LE CO2, with values 0.96 both. summary, these suggest proposed show a robust prediction studied fluxes, underlining versatility adaptability various conditions vineyard.

Language: Английский

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