Identifying favourable conditions for farm scale trafficability and grass growth using a combined Sentinel-2 and soil moisture deficit approach DOI Creative Commons
Rumia Basu, Owen Fenton, Eve Daly

et al.

Frontiers in Environmental Science, Journal Year: 2024, Volume and Issue: 12

Published: Feb. 27, 2024

In Atlantic Europe, on poorly drained grasslands soils, compaction negatively affects soil health when trafficked in wet conditions, while optimum grass growth cannot be achieved excessively dry conditions. Ireland, daily moisture deficit (SMD) information is forecasted at regional scale for all drainage classes. Optimal paddock conditions can occur between trafficking (10 mm) and (50 SMD thresholds an identified class. The objective of this farm study to improve the identification time space by combining high resolution spatial estimates with class specific data. For that purpose, Sentinel- 2 (S-2) data was used a modified Optical Trapezoid Model (OPTRAM) derive normalised surface (nSSM) level. In-situ sensors providing volumetric were validation OPTRAM RMSE 0.05. Cumulative 7-day prior date each S-2 image analysed year from 2017-2021 select nSSM maps corresponding negative, 0 or −0 positive SMD. Results established relationship indicating optimal changed spatially temporally. months April, May, August September always presented least 35% area available management operations. Future refinement methodology utilising remote sensing could provide near real-time farmers.

Language: Английский

Optimising Farm Area Allocations Based on Soil Moisture Thresholds: A Comparative Study of Two Dairy Farms with Distinct Soil and Topographic Features DOI Creative Commons
Rumia Basu, Owen Fenton, Gourav Misra

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(9), P. 920 - 920

Published: April 23, 2025

On intensive dairy farms, good decision making regarding application of fertilisers and irrigation requires an understanding soil moisture conditions. Targeted fertiliser not only contributes to high nutrient use efficiency but reduces the potential for leaching nutrients controls emissions from farms. This calls development improved farm management support system focussed on precision agriculture solutions sustainable agriculture. Knowledge at resolution scale can help develop such while same time reducing risk compaction by machinery and/or animals, especially under wet The objective this study is examine compare two with similar average annual rainfall contrasting (but drainage) topographic characteristics, their resilience towards extreme conditions (e.g., saturation or drought). Soil thresholds optimal corresponding area proportions were calculated, identifying areas targeted management. addresses knowledge gap including high-resolution satellite derived as a variable in designing systems Farm 1 was situated drumlin belt, whereas 2 had lowland terrain, representing major land cover categories Ireland. results showed that more resilient topography heterogeneity act buffer regulating regimes farm, preventing movement extremes. Across years, less variability could be managed better than terms overall productivity weather droughts, even drought year. along variations type, features also dictate water therefore

Language: Английский

Citations

0
A Review on Optimizing Water Management in Agriculture through Smart Irrigation Systems and Machine Learning DOI Creative Commons

Zaid Belarbi,

Yacine El Younoussi

E3S Web of Conferences, Journal Year: 2025, Volume and Issue: 601, P. 00078 - 00078

Published: Jan. 1, 2025

Optimizing irrigation water usage is crucial for sustainable agriculture, especially in the context of increasing scarcity and climate variability. Accurate estimation evapotranspiration (ET), a key component determining requirements crops, essential effective management. Traditional methods measuring estimating ET, such as eddy-covariance systems lysimeters, provide valuable data but often face limitations scalability, cost, complexity. Recent advancements machine learning (ML) offer promising alternatives to enhance precision efficiency ET smart systems. This review explores integration techniques optimizing usage, with particular focus on prediction technologies. We examine various ML models, that have been employed predict using diverse datasets comprising meteorological, soil, remote sensing data. In addition estimation, highlights optimize schedules based real-time inputs. Through this review, we aim comprehensive overview state-of-the-art ML-based technologies, contributing development more resilient efficient agricultural management strategies.

Language: Английский

Citations

0
On the Variability in the Temporal Stability Pattern of Soil Moisture Under Mediterranean Conditions DOI Creative Commons
Ángel González‐Zamora, Pilar Benito-Verdugo, José Martínez‐Fernández

et al.

Spanish Journal of Soil Science, Journal Year: 2024, Volume and Issue: 14

Published: June 10, 2024

In recent decades, there has been increasing interest in studying the variability soil water properties and, specifically, spatiotemporal content. This is motivated by notable theoretical and applied research interests moisture dynamics their implications for many natural processes. study aimed to whether are variations spatial pattern of temporal stability over time analyze possible influences certain hydroclimatic (soil content, precipitation, evapotranspiration) factors (texture, bulk density, organic matter content) on these variations. was conducted within Soil Moisture Measurement Stations Network University Salamanca (REMEDHUS, Spain) under Mediterranean conditions, with daily surface data (0–5 cm depth) obtained from 20 stations 2006-2023 period. The results showed differences between average 18-year series that each year. more than half years studied, representative station differed derived pattern. mean annual precipitation summer characteristics seem be main influencing moisture.

Language: Английский

Citations

2
Elevation-dependent dynamics of soil properties in a hilly watershed: a landform-based approach DOI
Sahil Sharma, Deepak Swami

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 196(11)

Published: Oct. 2, 2024

Language: Английский

Citations

2
Identifying favourable conditions for farm scale trafficability and grass growth using a combined Sentinel-2 and soil moisture deficit approach DOI Creative Commons
Rumia Basu, Owen Fenton, Eve Daly

et al.

Frontiers in Environmental Science, Journal Year: 2024, Volume and Issue: 12

Published: Feb. 27, 2024

In Atlantic Europe, on poorly drained grasslands soils, compaction negatively affects soil health when trafficked in wet conditions, while optimum grass growth cannot be achieved excessively dry conditions. Ireland, daily moisture deficit (SMD) information is forecasted at regional scale for all drainage classes. Optimal paddock conditions can occur between trafficking (10 mm) and (50 SMD thresholds an identified class. The objective of this farm study to improve the identification time space by combining high resolution spatial estimates with class specific data. For that purpose, Sentinel- 2 (S-2) data was used a modified Optical Trapezoid Model (OPTRAM) derive normalised surface (nSSM) level. In-situ sensors providing volumetric were validation OPTRAM RMSE 0.05. Cumulative 7-day prior date each S-2 image analysed year from 2017-2021 select nSSM maps corresponding negative, 0 or −0 positive SMD. Results established relationship indicating optimal changed spatially temporally. months April, May, August September always presented least 35% area available management operations. Future refinement methodology utilising remote sensing could provide near real-time farmers.

Language: Английский

Citations

1